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martin-ha/toxic-comment-model

martin-ha

transformers

text-classification

--- language: en --- ## Model description This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments. ## How to use You can use the model with the following code. ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline model_path = "martin-ha/toxic-comment-model" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path) pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer) print(pipeline('This is a test text.')) ``` ## Limitations and Bias This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation). But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. | **subgroup** | **subgroup_size** | **subgroup_auc** | **bpsn_auc** | **bnsp_auc** | | ----------------------------- | ----------------- | ---------------- | ------------ | ------------ | | muslim | 108 | 0.689 | 0.811 | 0.88 | | jewish | 40 | 0.749 | 0.86 | 0.825 | | homosexual_gay_or_lesbian | 56 | 0.795 | 0.706 | 0.972 | | black | 84 | 0.866 | 0.758 | 0.975 | | white | 112 | 0.876 | 0.784 | 0.97 | | female | 306 | 0.898 | 0.887 | 0.948 | | christian | 231 | 0.904 | 0.917 | 0.93 | | male | 225 | 0.922 | 0.862 | 0.967 | | psychiatric_or_mental_illness | 26 | 0.924 | 0.907 | 0.95 | The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass the sentence "Muslims are people who follow or practice Islam, an Abrahamic monotheistic religion." Into the model, the model will classify it as toxic. Be mindful for this type of potential bias. ## Training data The training data comes this [Kaggle competition](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). We use 10% of the `train.csv` data to train the model. ## Training procedure You can see [this documentation and codes](https://github.com/MSIA/wenyang_pan_nlp_project_2021) for how we train the model. It takes about 3 hours in a P-100 GPU. ## Evaluation results The model achieves 94% accuracy and 0.59 f1-score in a 10000 rows held-out test set.

ealvaradob/bert-finetuned-phishing

ealvaradob

transformers

text-classification

--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer - phishing - BERT metrics: - accuracy - precision - recall model-index: - name: bert-finetuned-phishing results: [] widget: - text: https://www.verif22.com example_title: Phishing URL - text: Dear colleague, An important update about your email has exceeded your storage limit. You will not be able to send or receive all of your messages. We will close all older versions of our Mailbox as of Friday, June 12, 2023. To activate and complete the required information click here (https://ec-ec.squarespace.com). Account must be reactivated today to regenerate new space. Management Team example_title: Phishing Email - text: You have access to FREE Video Streaming in your plan. REGISTER with your email, password and then select the monthly subscription option. https://bit.ly/3vNrU5r example_title: Phishing SMS - text: if(data.selectedIndex > 0){$('#hidCflag').val(data.selectedData.value);};; var sprypassword1 = new Spry.Widget.ValidationPassword("sprypassword1"); var sprytextfield1 = new Spry.Widget.ValidationTextField("sprytextfield1", "email"); example_title: Phishing Script - text: Hi, this model is really accurate :) example_title: Benign message datasets: - ealvaradob/phishing-dataset language: - en pipeline_tag: text-classification --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT FINETUNED ON PHISHING DETECTION This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an [phishing dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset), capable of detecting phishing in its four most common forms: URLs, Emails, SMS messages and even websites. It achieves the following results on the evaluation set: - Loss: 0.1953 - Accuracy: 0.9717 - Precision: 0.9658 - Recall: 0.9670 - False Positive Rate: 0.0249 ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters ## Motivation and Purpose Phishing is one of the most frequent and most expensive cyber-attacks according to several security reports. This model aims to efficiently and accurately prevent phishing attacks against individuals and organizations. To achieve it, BERT was trained on a diverse and robust dataset containing: URLs, SMS Messages, Emails and Websites, which allows the model to extend its detection capability beyond the usual and to be used in various contexts. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | False Positive Rate | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-------------------:| | 0.1487 | 1.0 | 3866 | 0.1454 | 0.9596 | 0.9709 | 0.9320 | 0.0203 | | 0.0805 | 2.0 | 7732 | 0.1389 | 0.9691 | 0.9663 | 0.9601 | 0.0243 | | 0.0389 | 3.0 | 11598 | 0.1779 | 0.9683 | 0.9778 | 0.9461 | 0.0156 | | 0.0091 | 4.0 | 15464 | 0.1953 | 0.9717 | 0.9658 | 0.9670 | 0.0249 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

nlpconnect/vit-gpt2-image-captioning

nlpconnect

transformers

image-to-text

--- tags: - image-to-text - image-captioning license: apache-2.0 widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # nlpconnect/vit-gpt2-image-captioning This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts). # The Illustrated Image Captioning using transformers  * https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/ # Sample running code ```python from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer import torch from PIL import Image model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) max_length = 16 num_beams = 4 gen_kwargs = {"max_length": max_length, "num_beams": num_beams} def predict_step(image_paths): images = [] for image_path in image_paths: i_image = Image.open(image_path) if i_image.mode != "RGB": i_image = i_image.convert(mode="RGB") images.append(i_image) pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device) output_ids = model.generate(pixel_values, **gen_kwargs) preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) preds = [pred.strip() for pred in preds] return preds predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman in a hospital bed'] ``` # Sample running code using transformers pipeline ```python from transformers import pipeline image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png") # [{'generated_text': 'a soccer game with a player jumping to catch the ball '}] ``` # Contact for any help * https://huggingface.co/ankur310794 * https://twitter.com/ankur310794 * http://github.com/ankur3107 * https://www.linkedin.com/in/ankur310794

Helsinki-NLP/opus-mt-zh-en

Helsinki-NLP

transformers

translation

--- language: - zh - en tags: - translation license: cc-by-4.0 --- ### zho-eng ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details - **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation - **Language(s):** - Source Language: Chinese - Target Language: English - **License:** CC-BY-4.0 - **Resources for more information:** - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Uses #### Direct Use This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Further details about the dataset for this model can be found in the OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md) ## Training #### System Information * helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 * transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b * port_machine: brutasse * port_time: 2020-08-21-14:41 * src_multilingual: False * tgt_multilingual: False #### Training Data ##### Preprocessing * pre-processing: normalization + SentencePiece (spm32k,spm32k) * ref_len: 82826.0 * dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip) * test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt) ## Evaluation #### Results * test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt) * brevity_penalty: 0.948 ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.zho.eng | 36.1 | 0.548 | ## Citation Information ```bibtex @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en") ```

assemblyai/bert-large-uncased-sst2

assemblyai

transformers

text-classification

# BERT-Large-Uncased for Sentiment Analysis This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sentiment Treebank v2 (SST2)](https://nlp.stanford.edu/sentiment/); part of the [General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com) benchmark. This model was fine-tuned by the team at [AssemblyAI](https://www.assemblyai.com) and is released with the [corresponding blog post](). ## Usage To download and utilize this model for sentiment analysis please execute the following: ```python import torch.nn.functional as F from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("assemblyai/bert-large-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("assemblyai/bert-large-uncased-sst2") tokenized_segments = tokenizer(["AssemblyAI is the best speech-to-text API for modern developers with performance being second to none!"], return_tensors="pt", padding=True, truncation=True) tokenized_segments_input_ids, tokenized_segments_attention_mask = tokenized_segments.input_ids, tokenized_segments.attention_mask model_predictions = F.softmax(model(input_ids=tokenized_segments_input_ids, attention_mask=tokenized_segments_attention_mask)['logits'], dim=1) print("Positive probability: "+str(model_predictions[0][1].item()*100)+"%") print("Negative probability: "+str(model_predictions[0][0].item()*100)+"%") ``` For questions about how to use this model feel free to contact the team at [AssemblyAI](https://www.assemblyai.com)!

ai-forever/sbert_large_nlu_ru

ai-forever

transformers

feature-extraction

--- language: - ru tags: - PyTorch - Transformers --- # BERT large model (uncased) for Sentence Embeddings in Russian language. The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/) For better quality, use mean token embeddings. ## Usage (HuggingFace Models Repository) You can use the model directly from the model repository to compute sentence embeddings: ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask #Sentences we want sentence embeddings for sentences = ['Привет! Как твои дела?', 'А правда, что 42 твое любимое число?'] #Load AutoModel from huggingface model repository tokenizer = AutoTokenizer.from_pretrained("ai-forever/sbert_large_nlu_ru") model = AutoModel.from_pretrained("ai-forever/sbert_large_nlu_ru") #Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt') #Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) #Perform pooling. In this case, mean pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) ``` # Authors + [SberDevices](https://sberdevices.ru/) Team. + Aleksandr Abramov: [HF profile](https://huggingface.co/Andrilko), [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko); + Denis Antykhov: [Github](https://github.com/gaphex);

ProsusAI/finbert

ProsusAI

transformers

text-classification

--- language: "en" tags: - financial-sentiment-analysis - sentiment-analysis widget: - text: "Stocks rallied and the British pound gained." --- FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. --- About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com. Contact information Please contact Dogu Araci dogu.araci[at]prosus[dot]com and Zulkuf Genc zulkuf.genc[at]prosus[dot]com about any FinBERT related issues and questions.

timm/efficientnet_b3.ra2_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for efficientnet_b3.ra2_in1k A EfficientNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 12.2 - GMACs: 1.6 - Activations (M): 21.5 - Image size: train = 288 x 288, test = 320 x 320 - **Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('efficientnet_b3.ra2_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'efficientnet_b3.ra2_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 24, 144, 144]) # torch.Size([1, 32, 72, 72]) # torch.Size([1, 48, 36, 36]) # torch.Size([1, 136, 18, 18]) # torch.Size([1, 384, 9, 9]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'efficientnet_b3.ra2_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1536, 9, 9) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2019efficientnet, title={Efficientnet: Rethinking model scaling for convolutional neural networks}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={6105--6114}, year={2019}, organization={PMLR} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ```

EmergentMethods/gliner_medium_news-v2.1

EmergentMethods

gliner

token-classification

--- license: apache-2.0 datasets: - EmergentMethods/AskNews-NER-v0 tags: - gliner language: - en pipeline_tag: token-classification --- # Model Card for gliner_medium_news-v2.1 This model is a fine-tune of [GLiNER](https://huggingface.co/urchade/gliner_medium-v2.1) aimed at improving accuracy across a broad range of topics, especially with respect to long-context news entity extraction. As shown in the table below, these fine-tunes improved upon the base GLiNER model zero-shot accuracy by up to 7.5% across 18 benchmark datasets.  The underlying dataset, [AskNews-NER-v0](https://huggingface.co/datasets/EmergentMethods/AskNews-NER-v0) was engineered with the objective of diversifying global perspectives by enforcing country/language/topic/temporal diversity. All data used to fine-tune this model was synthetically generated. WizardLM 13B v1.2 was used for translation/summarization of open-web news articles, while Llama3 70b instruct was used for entity extraction. Both the diversification and fine-tuning methods are presented in a our paper on [ArXiv](https://arxiv.org/abs/2406.10258). # Usage ```python from gliner import GLiNER model = GLiNER.from_pretrained("EmergentMethods/gliner_medium_news-v2.1") text = """ The Chihuahua State Public Security Secretariat (SSPE) arrested 35-year-old Salomón C. T. in Ciudad Juárez, found in possession of a stolen vehicle, a white GMC Yukon, which was reported stolen in the city's streets. The arrest was made by intelligence and police analysis personnel during an investigation in the border city. The arrest is related to a previous detention on February 6, which involved armed men in a private vehicle. The detainee and the vehicle were turned over to the Chihuahua State Attorney General's Office for further investigation into the case. """ labels = ["person", "location", "date", "event", "facility", "vehicle", "number", "organization"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) ``` Output: ``` Chihuahua State Public Security Secretariat => organization SSPE => organization 35-year-old => number Salomón C. T. => person Ciudad Juárez => location GMC Yukon => vehicle February 6 => date Chihuahua State Attorney General's Office => organization ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The synthetic data underlying this news fine-tune was pulled from the [AskNews API](https://docs.asknews.app). We enforced diveristy across country/language/topic/time. Countries:  Entity types:  Topics:  - **Developed by:** [Emergent Methods](https://emergentmethods.ai/) - **Funded by:** [Emergent Methods](https://emergentmethods.ai/) - **Shared by:** [Emergent Methods](https://emergentmethods.ai/) - **Model type:** microsoft/deberta - **Language(s) (NLP):** English (en) (English texts and translations from Spanish (es), Portuguese (pt), German (de), Russian (ru), French (fr), Arabic (ar), Italian (it), Ukrainian (uk), Norwegian (no), Swedish (sv), Danish (da)). - **License:** Apache 2.0 - **Finetuned from model:** [GLiNER](https://huggingface.co/urchade/gliner_medium-v2.1) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** To be added - **Paper:** To be added - **Demo:** To be added ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> As the name suggests, this model is aimed at generalist entity extraction. Although we used news to fine-tune this model, it improved accuracy across 18 benchmark datasets by up to 7.5%. This means that the broad and diversified underlying dataset has helped it to recognize and extract more entity types. This model is shockingly compact, and can be used for high-throughput production usecases. This is another reason we have licensed this as Apache 2.0. Currently, [AskNews](https://asknews.app) is using this fine-tune for entity extraction in their system. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Although the goal of the dataset is to reduce bias, and improve diversity, it is still biased to western languages and countries. This limitation originates from the abilities of Llama2 for the translation and summary generations. Further, any bias originating in Llama2 training data will also be present in this dataset, since Llama2 was used to summarize the open-web articles. Further, any biases present in Llama3 will be present in the present dataaset since Llama3 was used to extract entities from the summaries.  ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The training dataset is [AskNews-NER-v0](https://huggingface.co/datasets/EmergentMethods/AskNews-NER-v0). Other training details can be found in the [companion paper](https://linktoarxiv.org). ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> - **Hardware Type:** 1xA4500 - **Hours used:** 10 - **Carbon Emitted:** 0.6 kg (According to [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute)) ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** To be added **APA:** To be added ## Model Authors Elin Törnquist, Emergent Methods elin at emergentmethods.ai Robert Caulk, Emergent Methods rob at emergentmethods.ai ## Model Contact Elin Törnquist, Emergent Methods elin at emergentmethods.ai Robert Caulk, Emergent Methods rob at emergentmethods.ai

albert/albert-base-v2

albert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # ALBERT Base v2 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 768 hidden dimension - 12 attention heads - 11M parameters ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-base-v2') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"▁modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"▁modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"▁model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"▁runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"▁lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertModel.from_pretrained("albert-base-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = TFAlbertModel.from_pretrained("albert-base-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-base-v2') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"▁chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"▁janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"▁shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"▁blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"▁lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"▁receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"▁janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"▁paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"▁chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"▁waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` ### Training The ALBERT procedure follows the BERT setup. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ## Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |----------------|----------|----------|----------|----------|----------|----------| |V2 | |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |V1 | |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language Representations}, journal = {CoRR}, volume = {abs/1909.11942}, year = {2019}, url = {http://arxiv.org/abs/1909.11942}, archivePrefix = {arXiv}, eprint = {1909.11942}, timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

ashawkey/stable-zero123-diffusers

ashawkey

diffusers

--- license: mit --- # Uses _Note: This section is originally taken from the [Stable Diffusion v2 model card](https://huggingface.co/stabilityai/stable-diffusion-2), but applies in the same way to Zero-1-to-3._ ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include: - Safe deployment of large-scale models. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism. - The model cannot render legible text. - Faces and people in general may not be parsed or generated properly. - The autoencoding part of the model is lossy. - Stable Diffusion was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, Stability AI has filtered the dataset using LAION's NSFW detector. - Zero-1-to-3 was subsequently finetuned on a subset of the large-scale dataset [Objaverse](https://objaverse.allenai.org/), which might also potentially contain inappropriate content. To partially mitigate this, our demo applies a safety check to every uploaded image. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Images and concepts from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as Western cultures are often overrepresented. Stable Diffusion mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model inputs against known hard-coded NSFW concepts. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the uploaded input images. The concepts are passed into the model with the image and compared to a hand-engineered weight for each NSFW concept. ## Citation ``` @misc{liu2023zero1to3, title={Zero-1-to-3: Zero-shot One Image to 3D Object}, author={Ruoshi Liu and Rundi Wu and Basile Van Hoorick and Pavel Tokmakov and Sergey Zakharov and Carl Vondrick}, year={2023}, eprint={2303.11328}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

cross-encoder/nli-roberta-base

cross-encoder

transformers

zero-shot-classification

--- language: en pipeline_tag: zero-shot-classification tags: - roberta-base datasets: - multi_nli - snli metrics: - accuracy license: apache-2.0 --- # Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. ## Training Data The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. ## Performance For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli). ## Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-roberta-base') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) #Convert scores to labels label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] ``` ## Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base') tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base') features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] print(labels) ``` ## Zero-Shot Classification This model can also be used for zero-shot-classification: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", "politics"] res = classifier(sent, candidate_labels) print(res) ```

microsoft/layoutlm-base-uncased

microsoft

transformers

--- language: en license: mit --- # LayoutLM **Multimodal (text + layout/format + image) pre-training for document AI** [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm) ## Model description LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, [KDD 2020](https://www.kdd.org/kdd2020/accepted-papers) ## Training data We pre-train LayoutLM on IIT-CDIP Test Collection 1.0\* dataset with two settings. * LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters **(This Model)** * LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters ## Citation If you find LayoutLM useful in your research, please cite the following paper: ``` latex @misc{xu2019layoutlm, title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding}, author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou}, year={2019}, eprint={1912.13318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

gguichard/camembert-large-resaved

gguichard

transformers

feature-extraction

Entry not found

myshell-ai/MeloTTS-English

myshell-ai

transformers

text-to-speech

--- license: mit language: - ko pipeline_tag: text-to-speech --- # MeloTTS MeloTTS is a **high-quality multi-lingual** text-to-speech library by [MyShell.ai](https://myshell.ai). Supported languages include: | Model card | Example | | --- | --- | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (American) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-US/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (British) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-BR/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Indian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN_INDIA/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Australian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-AU/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Default) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-Default/speed_1.0/sent_000.wav) | | [Spanish](https://huggingface.co/myshell-ai/MeloTTS-Spanish) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/es/ES/speed_1.0/sent_000.wav) | | [French](https://huggingface.co/myshell-ai/MeloTTS-French) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/fr/FR/speed_1.0/sent_000.wav) | | [Chinese](https://huggingface.co/myshell-ai/MeloTTS-Chinese) (mix EN) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/zh/ZH/speed_1.0/sent_008.wav) | | [Japanese](https://huggingface.co/myshell-ai/MeloTTS-Japanese) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/jp/JP/speed_1.0/sent_000.wav) | | [Korean](https://huggingface.co/myshell-ai/MeloTTS-Korean/) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/kr/KR/speed_1.0/sent_000.wav) | Some other features include: - The Chinese speaker supports `mixed Chinese and English`. - Fast enough for `CPU real-time inference`. ## Usage ### Without Installation An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces. #### Use it on MyShell There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/quick_use.md#use-melotts-without-installation). More can be found at the widget center of [MyShell.ai](https://app.myshell.ai/robot-workshop). ### Install and Use Locally Follow the installation steps [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/install.md#linux-and-macos-install) before using the following snippet: ```python from melo.api import TTS # Speed is adjustable speed = 1.0 # CPU is sufficient for real-time inference. # You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps' device = 'auto' # Will automatically use GPU if available # English text = "Did you ever hear a folk tale about a giant turtle?" model = TTS(language='EN', device=device) speaker_ids = model.hps.data.spk2id # American accent output_path = 'en-us.wav' model.tts_to_file(text, speaker_ids['EN-US'], output_path, speed=speed) # British accent output_path = 'en-br.wav' model.tts_to_file(text, speaker_ids['EN-BR'], output_path, speed=speed) # Indian accent output_path = 'en-india.wav' model.tts_to_file(text, speaker_ids['EN_INDIA'], output_path, speed=speed) # Australian accent output_path = 'en-au.wav' model.tts_to_file(text, speaker_ids['EN-AU'], output_path, speed=speed) # Default accent output_path = 'en-default.wav' model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed) ``` ## Join the Community **Open Source AI Grant** We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and engineering projects, as long as the open-source community needs them. Please contact [Zengyi Qin](https://www.qinzy.tech/) if you are interested. **Contributing** If you find this work useful, please consider contributing to the GitHub [repo](https://github.com/myshell-ai/MeloTTS). - Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part. ## License This library is under MIT License, which means it is free for both commercial and non-commercial use. ## Acknowledgements This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.

Ashishkr/query_wellformedness_score

Ashishkr

transformers

text-classification

--- license: apache-2.0 inference: false datasets: google_wellformed_query --- ```DOI @misc {ashish_kumar_2024, author = { {Ashish Kumar} }, title = { query_wellformedness_score (Revision 55a424c) }, year = 2024, url = { https://huggingface.co/Ashishkr/query_wellformedness_score }, doi = { 10.57967/hf/1980 }, publisher = { Hugging Face } } ``` **Intended Use Cases** *Content Creation*: Validate the well-formedness of written content. *Educational Platforms*: Helps students check the grammaticality of their sentences. *Chatbots & Virtual Assistants*: To validate user queries or generate well-formed responses. **contact: [email protected]** **Model name**: Query Wellformedness Scoring **Description** : Evaluate the well-formedness of sentences by checking grammatical correctness and completeness. Sensitive to case and penalizes sentences for incorrect grammar and case. **Features**: - *Wellformedness Score*: Provides a score indicating grammatical correctness and completeness. - *Case Sensitivity*: Recognizes and penalizes incorrect casing in sentences. - *Broad Applicability*: Can be used on a wide range of sentences. **Example**: 1. Dogs are mammals. 2. she loves to read books on history. 3. When the rain in Spain. 4. Eating apples are healthy for you. 5. The Eiffel Tower is in Paris. Among these sentences: Sentences 1 and 5 are well-formed and have correct grammar and case. Sentence 2 starts with a lowercase letter. Sentence 3 is a fragment and is not well-formed. Sentence 4 has a subject-verb agreement error. **example_usage:** *library: HuggingFace transformers* ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ashishkr/query_wellformedness_score") model = AutoModelForSequenceClassification.from_pretrained("Ashishkr/query_wellformedness_score") sentences = [ "The quarterly financial report are showing an increase.", # Incorrect "Him has completed the audit for last fiscal year.", # Incorrect "Please to inform the board about the recent developments.", # Incorrect "The team successfully achieved all its targets for the last quarter.", # Correct "Our company is exploring new ventures in the European market." # Correct ] features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` Cite Ashishkr/query_wellformedness_score

microsoft/layoutlmv2-base-uncased

microsoft

transformers

--- language: en license: cc-by-nc-sa-4.0 --- # LayoutLMv2 **Multimodal (text + layout/format + image) pre-training for document AI** The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutlmv2). [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://github.com/microsoft/unilm/tree/master/layoutlmv2) ## Introduction LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including , including FUNSD (0.7895 → 0.8420), CORD (0.9493 → 0.9601), SROIE (0.9524 → 0.9781), Kleister-NDA (0.834 → 0.852), RVL-CDIP (0.9443 → 0.9564), and DocVQA (0.7295 → 0.8672). [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, ACL 2021

vennify/t5-base-grammar-correction

vennify

transformers

text2text-generation

--- language: en tags: - grammar - text2text-generation license: cc-by-nc-sa-4.0 datasets: - jfleg --- # T5 Grammar Correction This model generates a revised version of inputted text with the goal of containing fewer grammatical errors. It was trained with [Happy Transformer](https://github.com/EricFillion/happy-transformer) using a dataset called [JFLEG](https://arxiv.org/abs/1702.04066). Here's a [full article](https://www.vennify.ai/fine-tune-grammar-correction/) on how to train a similar model. ## Usage `pip install happytransformer ` ```python from happytransformer import HappyTextToText, TTSettings happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction") args = TTSettings(num_beams=5, min_length=1) # Add the prefix "grammar: " before each input result = happy_tt.generate_text("grammar: This sentences has has bads grammar.", args=args) print(result.text) # This sentence has bad grammar. ```

unikei/t5-base-split-and-rephrase

unikei

transformers

text2text-generation

--- license: bigscience-openrail-m tags: - split and rephrase widget: - text: >- Cystic Fibrosis (CF) is an autosomal recessive disorder that affects multiple organs, which is common in the Caucasian population, symptomatically affecting 1 in 2500 newborns in the UK, and more than 80,000 individuals globally. datasets: - wiki_split - web_split language: - en --- # T5 model for splitting complex sentences to simple sentences in English Split-and-rephrase is the task of splitting a complex input sentence into shorter sentences while preserving meaning. (Narayan et al., 2017) E.g.: ``` Cystic Fibrosis (CF) is an autosomal recessive disorder that affects multiple organs, which is common in the Caucasian population, symptomatically affecting 1 in 2500 newborns in the UK, and more than 80,000 individuals globally. ``` could be split into ``` Cystic Fibrosis is an autosomal recessive disorder that affects multiple organs. ``` ``` Cystic Fibrosis is common in the Caucasian population. ``` ``` Cystic Fibrosis affects 1 in 2500 newborns in the UK. ``` ``` Cystic Fibrosis affects more than 80,000 individuals globally. ``` ## How to use it in your code: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration checkpoint="unikei/t5-base-split-and-rephrase" tokenizer = T5Tokenizer.from_pretrained(checkpoint) model = T5ForConditionalGeneration.from_pretrained(checkpoint) complex_sentence = "Cystic Fibrosis (CF) is an autosomal recessive disorder that \ affects multiple organs, which is common in the Caucasian \ population, symptomatically affecting 1 in 2500 newborns in \ the UK, and more than 80,000 individuals globally." complex_tokenized = tokenizer(complex_sentence, padding="max_length", truncation=True, max_length=256, return_tensors='pt') simple_tokenized = model.generate(complex_tokenized['input_ids'], attention_mask = complex_tokenized['attention_mask'], max_length=256, num_beams=5) simple_sentences = tokenizer.batch_decode(simple_tokenized, skip_special_tokens=True) print(simple_sentences) """ Output: Cystic Fibrosis is an autosomal recessive disorder that affects multiple organs. Cystic Fibrosis is common in the Caucasian population. Cystic Fibrosis affects 1 in 2500 newborns in the UK. Cystic Fibrosis affects more than 80,000 individuals globally. """ ```

sentence-transformers/distiluse-base-multilingual-cased-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- language: - multilingual - ar - bg - ca - cs - da - de - el - en - es - et - fa - fi - fr - gl - gu - he - hi - hr - hu - hy - id - it - ja - ka - ko - ku - lt - lv - mk - mn - mr - ms - my - nb - nl - pl - pt - ro - ru - sk - sl - sq - sr - sv - th - tr - uk - ur - vi license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity language_bcp47: - fr-ca - pt-br - zh-cn - zh-tw pipeline_tag: sentence-similarity --- # sentence-transformers/distiluse-base-multilingual-cased-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

tiiuae/falcon-7b-instruct

tiiuae

transformers

text-generation

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true widget: - text: "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?" example_title: "Abu Dhabi Trip" - text: "What's the Everett interpretation of quantum mechanics?" example_title: "Q/A: Quantum & Answers" - text: "Give me a list of the top 10 dive sites you would recommend around the world." example_title: "Diving Top 10" - text: "Can you tell me more about deep-water soloing?" example_title: "Extreme sports" - text: "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?" example_title: "Twitter Helper" - text: "What are the responsabilities of a Chief Llama Officer?" example_title: "Trendy Jobs" license: apache-2.0 --- # ✨ Falcon-7B-Instruct **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.** *Paper coming soon 😊.* 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).** * **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0; - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. | **Data source** | **Fraction** | **Tokens** | **Description** | |--------------------|--------------|------------|-----------------------------------| | [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat | | [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct | | [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct | | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 32 | | | `d_model` | 4544 | Increased to compensate for multiquery | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact [email protected]

vinvino02/glpn-kitti

vinvino02

transformers

depth-estimation

--- license: apache-2.0 tags: - vision - depth-estimation widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # GLPN fine-tuned on KITTI Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](https://github.com/vinvino02/GLPDepth). Disclaimer: The team releasing GLPN did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description GLPN uses SegFormer as backbone and adds a lightweight head on top for depth estimation.  ## Intended uses & limitations You can use the raw model for monocular depth estimation. See the [model hub](https://huggingface.co/models?search=glpn) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import GLPNImageProcessor, GLPNForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-kitti") model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti") # prepare image for the model inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) # visualize the prediction output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/glpn). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2201-07436, author = {Doyeon Kim and Woonghyun Ga and Pyunghwan Ahn and Donggyu Joo and Sehwan Chun and Junmo Kim}, title = {Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth}, journal = {CoRR}, volume = {abs/2201.07436}, year = {2022}, url = {https://arxiv.org/abs/2201.07436}, eprinttype = {arXiv}, eprint = {2201.07436}, timestamp = {Fri, 21 Jan 2022 13:57:15 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-07436.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

Jean-Baptiste/camembert-ner

Jean-Baptiste

transformers

token-classification

--- language: fr datasets: - Jean-Baptiste/wikiner_fr widget: - text: "Je m'appelle jean-baptiste et je vis à montréal" - text: "george washington est allé à washington" license: mit --- # camembert-ner: model fine-tuned from camemBERT for NER task. ## Introduction [camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset. Model was trained on wikiner-fr dataset (~170 634 sentences). Model was validated on emails/chat data and overperformed other models on this type of data specifically. In particular the model seems to work better on entity that don't start with an upper case. ## Training data Training data was classified as follow: Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location ## How to use camembert-ner with HuggingFace ##### Load camembert-ner and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner") model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner") ##### Process text sample (from wikipedia) from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.") [{'entity_group': 'ORG', 'score': 0.9472818374633789, 'word': 'Apple', 'start': 0, 'end': 5}, {'entity_group': 'PER', 'score': 0.9838564991950989, 'word': 'Steve Jobs', 'start': 74, 'end': 85}, {'entity_group': 'LOC', 'score': 0.9831605950991312, 'word': 'Los Altos', 'start': 87, 'end': 97}, {'entity_group': 'LOC', 'score': 0.9834540486335754, 'word': 'Californie', 'start': 100, 'end': 111}, {'entity_group': 'PER', 'score': 0.9841555754343668, 'word': 'Steve Jobs', 'start': 115, 'end': 126}, {'entity_group': 'PER', 'score': 0.9843501806259155, 'word': 'Steve Wozniak', 'start': 127, 'end': 141}, {'entity_group': 'PER', 'score': 0.9841533899307251, 'word': 'Ronald Wayne', 'start': 144, 'end': 157}, {'entity_group': 'ORG', 'score': 0.9468960364659628, 'word': 'Apple Computer', 'start': 243, 'end': 257}] ``` ## Model performances (metric: seqeval) Overall precision|recall|f1 -|-|- 0.8859|0.8971|0.8914 By entity entity|precision|recall|f1 -|-|-|- PER|0.9372|0.9598|0.9483 ORG|0.8099|0.8265|0.8181 LOC|0.8905|0.9005|0.8955 MISC|0.8175|0.8117|0.8146 For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa

microsoft/deberta-v3-large

microsoft

transformers

fill-mask

--- language: en tags: - deberta - deberta-v3 - fill-mask thumbnail: https://huggingface.co/front/thumbnails/microsoft.png license: mit --- ## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543). Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates. The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 2.0 and MNLI tasks. | Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)| |-------------------|----------|-------------------|-----------|----------| | RoBERTa-large |50 |304 | 89.4/86.5 | 90.2 | | XLNet-large |32 |- | 90.6/87.9 | 90.8 | | DeBERTa-large |50 |- | 90.7/88.0 | 91.3 | | **DeBERTa-v3-large**|128|304 | **91.5/89.0**| **91.8/91.9**| #### Fine-tuning with HF transformers ```bash #!/bin/bash cd transformers/examples/pytorch/text-classification/ pip install datasets export TASK_NAME=mnli output_dir="ds_results" num_gpus=8 batch_size=8 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \ run_glue.py \ --model_name_or_path microsoft/deberta-v3-large \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --evaluation_strategy steps \ --max_seq_length 256 \ --warmup_steps 50 \ --per_device_train_batch_size ${batch_size} \ --learning_rate 6e-6 \ --num_train_epochs 2 \ --output_dir $output_dir \ --overwrite_output_dir \ --logging_steps 1000 \ --logging_dir $output_dir ``` ### Citation If you find DeBERTa useful for your work, please cite the following papers: ``` latex @misc{he2021debertav3, title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, author={Pengcheng He and Jianfeng Gao and Weizhu Chen}, year={2021}, eprint={2111.09543}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```

cambridgeltl/SapBERT-from-PubMedBERT-fulltext

cambridgeltl

transformers

feature-extraction

--- license: apache-2.0 language: - en tags: - biomedical - lexical semantics - bionlp - biology - science - embedding - entity linking --- --- datasets: - UMLS **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br> **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! ### SapBERT-PubMedBERT SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model. ### Expected input and output The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output. #### Extracting embeddings from SapBERT The following script converts a list of strings (entity names) into embeddings. ```python import numpy as np import torch from tqdm.auto import tqdm from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda() # replace with your own list of entity names all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"] bs = 128 # batch size during inference all_embs = [] for i in tqdm(np.arange(0, len(all_names), bs)): toks = tokenizer.batch_encode_plus(all_names[i:i+bs], padding="max_length", max_length=25, truncation=True, return_tensors="pt") toks_cuda = {} for k,v in toks.items(): toks_cuda[k] = v.cuda() cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding all_embs.append(cls_rep.cpu().detach().numpy()) all_embs = np.concatenate(all_embs, axis=0) ``` For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert). ### Citation ```bibtex @inproceedings{liu-etal-2021-self, title = "Self-Alignment Pretraining for Biomedical Entity Representations", author = "Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.naacl-main.334", pages = "4228--4238", abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.", } ```

HuggingFaceFW/fineweb-edu-classifier

HuggingFaceFW

transformers

text-classification

--- language: - en license: apache-2.0 --- # FineWeb-Edu classifier ## Model summary This is a classifier for judging the educational value of web pages. It was developed to filter and curate educational content from web datasets and was trained on 450k [annotations](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-llama3-annotations) generated by [LLama3-70B-instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) for web samples from [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) dataset. We used this classifier to build [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) dataset. ### How to use in transformers To load the FineWeb-Edu classifier, use the following code: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/fineweb-edu-classifier") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/fineweb-edu-classifier") text = "This is a test sentence." inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True) outputs = model(**inputs) logits = outputs.logits.squeeze(-1).float().detach().numpy() score = logits.item() result = { "text": text, "score": score, "int_score": int(round(max(0, min(score, 5)))), } print(result) # {'text': 'This is a test sentence.', 'score': 0.07964489609003067, 'int_score': 0} ``` ## Training The classifier was trained on 450,000 pairs of web samples and their scores from 0 to 5, generated by Llama3. The samples were annotated based on their educational quality with 0 being not educational and 5 being highly educational. Below is the prompt used for LLama3 annotations: <div style="text-align: center; margin: 20px 0;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/fjZQ4izIj1rx1xQnBTKKr.png" alt="Prompt for LLM annotation" style="width: 90%; max-width: 800px; height: auto;"> </div> We added a classification head with a single regression output to [Snowflake-arctic-embed](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) and trained the model for 20 epochs with a learning rate of 3e-4. During training, the embedding and encoder layers were frozen to focus on the classification head. The model achieved an F1 score of 82% when converted to a binary classifier using a score threshold of 3. **Training Details:** - Model: Snowflake-arctic-embed with a classification head - Dataset: 450,000 samples from Llama3 annotations - Epochs: 20 - Learning Rate: 3e-4 - Evaluation Metric: F1 score **Classification report** We treat the regression model's predictions as discrete classes to calculate the metrics on a hold-out set of 46867 Llama3-annotated samples. ``` precision recall f1-score support 0 0.75 0.49 0.59 5694 1 0.78 0.84 0.81 26512 2 0.57 0.61 0.59 10322 3 0.56 0.50 0.53 3407 4 0.58 0.35 0.44 807 5 0.33 0.01 0.02 125 accuracy 0.71 46867 macro avg 0.60 0.47 0.50 46867 weighted avg 0.71 0.71 0.71 46867 ``` **Confusion matrix** We verify that the predicted educational scores are indeed close to their ground truth, and are mostry impacted by the noisy annotation. ``` 2791 2858 45 0 0 0 919 22343 3180 69 1 0 y_true 3 3225 6330 757 7 0 1 66 1473 1694 173 0 0 4 98 420 283 2 0 0 18 85 21 1 y_pred ``` ## Limitations While the FineWeb-Edu classifier performs well in distinguishing high-quality educational content for FineWeb dataset, there are some limitations: - Scope: The model's performance might change for other datasets, in particular for out of distribution samples. It is also focused on educational content relevant to primary and grade school levels and may not perform as well on content intended for higher education or specialized domains. - Bias: The model's performance is dependent on the quality and representativeness of the training data and the LLM used for the annotation. Biases in both can affect the classifier's judgments. It might overfit to academic looking content for the higher scores and we recommend using int_score >= 3 as a threshold for data curation. - Context: The classifier evaluates individual web pages or extracts without considering broader context, which might impact its effectiveness in certain scenarios. The training and inference code is available on GitHub https://github.com/huggingface/cosmopedia/tree/main/classification

Alibaba-NLP/gte-large-en-v1.5

Alibaba-NLP

transformers

sentence-similarity

--- datasets: - allenai/c4 library_name: transformers tags: - sentence-transformers - gte - mteb - transformers.js - sentence-similarity license: apache-2.0 language: - en model-index: - name: gte-large-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.01492537313432 - type: ap value: 35.05341696659522 - type: f1 value: 66.71270310883853 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.97189999999999 - type: ap value: 90.5952493948908 - type: f1 value: 93.95848137716877 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 54.196 - type: f1 value: 53.80122334012787 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 47.297 - type: map_at_10 value: 64.303 - type: map_at_100 value: 64.541 - type: map_at_1000 value: 64.541 - type: map_at_3 value: 60.728 - type: map_at_5 value: 63.114000000000004 - type: mrr_at_1 value: 48.435 - type: mrr_at_10 value: 64.657 - type: mrr_at_100 value: 64.901 - type: mrr_at_1000 value: 64.901 - type: mrr_at_3 value: 61.06 - type: mrr_at_5 value: 63.514 - type: ndcg_at_1 value: 47.297 - type: ndcg_at_10 value: 72.107 - type: ndcg_at_100 value: 72.963 - type: ndcg_at_1000 value: 72.963 - type: ndcg_at_3 value: 65.063 - type: ndcg_at_5 value: 69.352 - type: precision_at_1 value: 47.297 - type: precision_at_10 value: 9.623 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 25.865 - type: precision_at_5 value: 17.596 - type: recall_at_1 value: 47.297 - type: recall_at_10 value: 96.23 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 77.596 - type: recall_at_5 value: 87.98 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.467787861077475 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.39198391914257 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.12794820591384 - type: mrr value: 75.9331442641692 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.85062993863319 - type: cos_sim_spearman value: 85.39049989733459 - type: euclidean_pearson value: 86.00222680278333 - type: euclidean_spearman value: 85.45556162077396 - type: manhattan_pearson value: 85.88769871785621 - type: manhattan_spearman value: 85.11760211290839 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.32792207792208 - type: f1 value: 87.29132945999555 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.5779328301945 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.94425623865118 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 32.978 - type: map_at_10 value: 44.45 - type: map_at_100 value: 46.19 - type: map_at_1000 value: 46.303 - type: map_at_3 value: 40.849000000000004 - type: map_at_5 value: 42.55 - type: mrr_at_1 value: 40.629 - type: mrr_at_10 value: 50.848000000000006 - type: mrr_at_100 value: 51.669 - type: mrr_at_1000 value: 51.705 - type: mrr_at_3 value: 47.997 - type: mrr_at_5 value: 49.506 - type: ndcg_at_1 value: 40.629 - type: ndcg_at_10 value: 51.102000000000004 - type: ndcg_at_100 value: 57.159000000000006 - type: ndcg_at_1000 value: 58.669000000000004 - type: ndcg_at_3 value: 45.738 - type: ndcg_at_5 value: 47.632999999999996 - type: precision_at_1 value: 40.629 - type: precision_at_10 value: 9.700000000000001 - type: precision_at_100 value: 1.5970000000000002 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.698 - type: precision_at_5 value: 15.393 - type: recall_at_1 value: 32.978 - type: recall_at_10 value: 63.711 - type: recall_at_100 value: 88.39399999999999 - type: recall_at_1000 value: 97.513 - type: recall_at_3 value: 48.025 - type: recall_at_5 value: 53.52 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 30.767 - type: map_at_10 value: 42.195 - type: map_at_100 value: 43.541999999999994 - type: map_at_1000 value: 43.673 - type: map_at_3 value: 38.561 - type: map_at_5 value: 40.532000000000004 - type: mrr_at_1 value: 38.79 - type: mrr_at_10 value: 48.021 - type: mrr_at_100 value: 48.735 - type: mrr_at_1000 value: 48.776 - type: mrr_at_3 value: 45.594 - type: mrr_at_5 value: 46.986 - type: ndcg_at_1 value: 38.79 - type: ndcg_at_10 value: 48.468 - type: ndcg_at_100 value: 53.037 - type: ndcg_at_1000 value: 55.001999999999995 - type: ndcg_at_3 value: 43.409 - type: ndcg_at_5 value: 45.654 - type: precision_at_1 value: 38.79 - type: precision_at_10 value: 9.452 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 21.21 - type: precision_at_5 value: 15.171999999999999 - type: recall_at_1 value: 30.767 - type: recall_at_10 value: 60.118 - type: recall_at_100 value: 79.271 - type: recall_at_1000 value: 91.43299999999999 - type: recall_at_3 value: 45.36 - type: recall_at_5 value: 51.705 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 40.007 - type: map_at_10 value: 53.529 - type: map_at_100 value: 54.602 - type: map_at_1000 value: 54.647 - type: map_at_3 value: 49.951 - type: map_at_5 value: 52.066 - type: mrr_at_1 value: 45.705 - type: mrr_at_10 value: 56.745000000000005 - type: mrr_at_100 value: 57.43899999999999 - type: mrr_at_1000 value: 57.462999999999994 - type: mrr_at_3 value: 54.25299999999999 - type: mrr_at_5 value: 55.842000000000006 - type: ndcg_at_1 value: 45.705 - type: ndcg_at_10 value: 59.809 - type: ndcg_at_100 value: 63.837999999999994 - type: ndcg_at_1000 value: 64.729 - type: ndcg_at_3 value: 53.994 - type: ndcg_at_5 value: 57.028 - type: precision_at_1 value: 45.705 - type: precision_at_10 value: 9.762 - type: precision_at_100 value: 1.275 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.84 - type: recall_at_1 value: 40.007 - type: recall_at_10 value: 75.017 - type: recall_at_100 value: 91.99000000000001 - type: recall_at_1000 value: 98.265 - type: recall_at_3 value: 59.704 - type: recall_at_5 value: 67.109 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 26.639000000000003 - type: map_at_10 value: 35.926 - type: map_at_100 value: 37.126999999999995 - type: map_at_1000 value: 37.202 - type: map_at_3 value: 32.989000000000004 - type: map_at_5 value: 34.465 - type: mrr_at_1 value: 28.475 - type: mrr_at_10 value: 37.7 - type: mrr_at_100 value: 38.753 - type: mrr_at_1000 value: 38.807 - type: mrr_at_3 value: 35.066 - type: mrr_at_5 value: 36.512 - type: ndcg_at_1 value: 28.475 - type: ndcg_at_10 value: 41.245 - type: ndcg_at_100 value: 46.814 - type: ndcg_at_1000 value: 48.571 - type: ndcg_at_3 value: 35.528999999999996 - type: ndcg_at_5 value: 38.066 - type: precision_at_1 value: 28.475 - type: precision_at_10 value: 6.497 - type: precision_at_100 value: 0.9650000000000001 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.065999999999999 - type: precision_at_5 value: 10.599 - type: recall_at_1 value: 26.639000000000003 - type: recall_at_10 value: 55.759 - type: recall_at_100 value: 80.913 - type: recall_at_1000 value: 93.929 - type: recall_at_3 value: 40.454 - type: recall_at_5 value: 46.439 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 15.767999999999999 - type: map_at_10 value: 24.811 - type: map_at_100 value: 26.064999999999998 - type: map_at_1000 value: 26.186999999999998 - type: map_at_3 value: 21.736 - type: map_at_5 value: 23.283 - type: mrr_at_1 value: 19.527 - type: mrr_at_10 value: 29.179 - type: mrr_at_100 value: 30.153999999999996 - type: mrr_at_1000 value: 30.215999999999998 - type: mrr_at_3 value: 26.223000000000003 - type: mrr_at_5 value: 27.733999999999998 - type: ndcg_at_1 value: 19.527 - type: ndcg_at_10 value: 30.786 - type: ndcg_at_100 value: 36.644 - type: ndcg_at_1000 value: 39.440999999999995 - type: ndcg_at_3 value: 24.958 - type: ndcg_at_5 value: 27.392 - type: precision_at_1 value: 19.527 - type: precision_at_10 value: 5.995 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 12.520999999999999 - type: precision_at_5 value: 9.129 - type: recall_at_1 value: 15.767999999999999 - type: recall_at_10 value: 44.824000000000005 - type: recall_at_100 value: 70.186 - type: recall_at_1000 value: 89.934 - type: recall_at_3 value: 28.607 - type: recall_at_5 value: 34.836 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 31.952 - type: map_at_10 value: 44.438 - type: map_at_100 value: 45.778 - type: map_at_1000 value: 45.883 - type: map_at_3 value: 41.044000000000004 - type: map_at_5 value: 42.986000000000004 - type: mrr_at_1 value: 39.172000000000004 - type: mrr_at_10 value: 49.76 - type: mrr_at_100 value: 50.583999999999996 - type: mrr_at_1000 value: 50.621 - type: mrr_at_3 value: 47.353 - type: mrr_at_5 value: 48.739 - type: ndcg_at_1 value: 39.172000000000004 - type: ndcg_at_10 value: 50.760000000000005 - type: ndcg_at_100 value: 56.084 - type: ndcg_at_1000 value: 57.865 - type: ndcg_at_3 value: 45.663 - type: ndcg_at_5 value: 48.178 - type: precision_at_1 value: 39.172000000000004 - type: precision_at_10 value: 9.22 - type: precision_at_100 value: 1.387 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 21.976000000000003 - type: precision_at_5 value: 15.457 - type: recall_at_1 value: 31.952 - type: recall_at_10 value: 63.900999999999996 - type: recall_at_100 value: 85.676 - type: recall_at_1000 value: 97.03699999999999 - type: recall_at_3 value: 49.781 - type: recall_at_5 value: 56.330000000000005 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 25.332 - type: map_at_10 value: 36.874 - type: map_at_100 value: 38.340999999999994 - type: map_at_1000 value: 38.452 - type: map_at_3 value: 33.068 - type: map_at_5 value: 35.324 - type: mrr_at_1 value: 30.822 - type: mrr_at_10 value: 41.641 - type: mrr_at_100 value: 42.519 - type: mrr_at_1000 value: 42.573 - type: mrr_at_3 value: 38.413000000000004 - type: mrr_at_5 value: 40.542 - type: ndcg_at_1 value: 30.822 - type: ndcg_at_10 value: 43.414 - type: ndcg_at_100 value: 49.196 - type: ndcg_at_1000 value: 51.237 - type: ndcg_at_3 value: 37.230000000000004 - type: ndcg_at_5 value: 40.405 - type: precision_at_1 value: 30.822 - type: precision_at_10 value: 8.379 - type: precision_at_100 value: 1.315 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 18.417 - type: precision_at_5 value: 13.744 - type: recall_at_1 value: 25.332 - type: recall_at_10 value: 57.774 - type: recall_at_100 value: 82.071 - type: recall_at_1000 value: 95.60600000000001 - type: recall_at_3 value: 40.722 - type: recall_at_5 value: 48.754999999999995 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 25.91033333333334 - type: map_at_10 value: 36.23225000000001 - type: map_at_100 value: 37.55766666666667 - type: map_at_1000 value: 37.672583333333336 - type: map_at_3 value: 32.95666666666667 - type: map_at_5 value: 34.73375 - type: mrr_at_1 value: 30.634 - type: mrr_at_10 value: 40.19449999999999 - type: mrr_at_100 value: 41.099250000000005 - type: mrr_at_1000 value: 41.15091666666667 - type: mrr_at_3 value: 37.4615 - type: mrr_at_5 value: 39.00216666666667 - type: ndcg_at_1 value: 30.634 - type: ndcg_at_10 value: 42.162166666666664 - type: ndcg_at_100 value: 47.60708333333333 - type: ndcg_at_1000 value: 49.68616666666666 - type: ndcg_at_3 value: 36.60316666666666 - type: ndcg_at_5 value: 39.15616666666668 - type: precision_at_1 value: 30.634 - type: precision_at_10 value: 7.6193333333333335 - type: precision_at_100 value: 1.2198333333333333 - type: precision_at_1000 value: 0.15975000000000003 - type: precision_at_3 value: 17.087 - type: precision_at_5 value: 12.298333333333334 - type: recall_at_1 value: 25.91033333333334 - type: recall_at_10 value: 55.67300000000001 - type: recall_at_100 value: 79.20608333333334 - type: recall_at_1000 value: 93.34866666666667 - type: recall_at_3 value: 40.34858333333333 - type: recall_at_5 value: 46.834083333333325 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 25.006 - type: map_at_10 value: 32.177 - type: map_at_100 value: 33.324999999999996 - type: map_at_1000 value: 33.419 - type: map_at_3 value: 29.952 - type: map_at_5 value: 31.095 - type: mrr_at_1 value: 28.066999999999997 - type: mrr_at_10 value: 34.995 - type: mrr_at_100 value: 35.978 - type: mrr_at_1000 value: 36.042 - type: mrr_at_3 value: 33.103 - type: mrr_at_5 value: 34.001 - type: ndcg_at_1 value: 28.066999999999997 - type: ndcg_at_10 value: 36.481 - type: ndcg_at_100 value: 42.022999999999996 - type: ndcg_at_1000 value: 44.377 - type: ndcg_at_3 value: 32.394 - type: ndcg_at_5 value: 34.108 - type: precision_at_1 value: 28.066999999999997 - type: precision_at_10 value: 5.736 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.508999999999999 - type: recall_at_1 value: 25.006 - type: recall_at_10 value: 46.972 - type: recall_at_100 value: 72.138 - type: recall_at_1000 value: 89.479 - type: recall_at_3 value: 35.793 - type: recall_at_5 value: 39.947 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 16.07 - type: map_at_10 value: 24.447 - type: map_at_100 value: 25.685999999999996 - type: map_at_1000 value: 25.813999999999997 - type: map_at_3 value: 21.634 - type: map_at_5 value: 23.133 - type: mrr_at_1 value: 19.580000000000002 - type: mrr_at_10 value: 28.127999999999997 - type: mrr_at_100 value: 29.119 - type: mrr_at_1000 value: 29.192 - type: mrr_at_3 value: 25.509999999999998 - type: mrr_at_5 value: 26.878 - type: ndcg_at_1 value: 19.580000000000002 - type: ndcg_at_10 value: 29.804000000000002 - type: ndcg_at_100 value: 35.555 - type: ndcg_at_1000 value: 38.421 - type: ndcg_at_3 value: 24.654999999999998 - type: ndcg_at_5 value: 26.881 - type: precision_at_1 value: 19.580000000000002 - type: precision_at_10 value: 5.736 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.033000000000001 - type: precision_at_5 value: 8.871 - type: recall_at_1 value: 16.07 - type: recall_at_10 value: 42.364000000000004 - type: recall_at_100 value: 68.01899999999999 - type: recall_at_1000 value: 88.122 - type: recall_at_3 value: 27.846 - type: recall_at_5 value: 33.638 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 26.365 - type: map_at_10 value: 36.591 - type: map_at_100 value: 37.730000000000004 - type: map_at_1000 value: 37.84 - type: map_at_3 value: 33.403 - type: map_at_5 value: 35.272999999999996 - type: mrr_at_1 value: 30.503999999999998 - type: mrr_at_10 value: 39.940999999999995 - type: mrr_at_100 value: 40.818 - type: mrr_at_1000 value: 40.876000000000005 - type: mrr_at_3 value: 37.065 - type: mrr_at_5 value: 38.814 - type: ndcg_at_1 value: 30.503999999999998 - type: ndcg_at_10 value: 42.185 - type: ndcg_at_100 value: 47.416000000000004 - type: ndcg_at_1000 value: 49.705 - type: ndcg_at_3 value: 36.568 - type: ndcg_at_5 value: 39.416000000000004 - type: precision_at_1 value: 30.503999999999998 - type: precision_at_10 value: 7.276000000000001 - type: precision_at_100 value: 1.118 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 16.729 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 26.365 - type: recall_at_10 value: 55.616 - type: recall_at_100 value: 78.129 - type: recall_at_1000 value: 93.95599999999999 - type: recall_at_3 value: 40.686 - type: recall_at_5 value: 47.668 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 22.750999999999998 - type: map_at_10 value: 33.446 - type: map_at_100 value: 35.235 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.358 - type: map_at_5 value: 31.525 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 37.694 - type: mrr_at_100 value: 38.732 - type: mrr_at_1000 value: 38.779 - type: mrr_at_3 value: 34.223 - type: mrr_at_5 value: 36.08 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 40.557 - type: ndcg_at_100 value: 46.605999999999995 - type: ndcg_at_1000 value: 48.917 - type: ndcg_at_3 value: 33.677 - type: ndcg_at_5 value: 36.85 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 1.6260000000000001 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 12.292 - type: recall_at_1 value: 22.750999999999998 - type: recall_at_10 value: 55.643 - type: recall_at_100 value: 82.151 - type: recall_at_1000 value: 95.963 - type: recall_at_3 value: 36.623 - type: recall_at_5 value: 44.708 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 17.288999999999998 - type: map_at_10 value: 25.903 - type: map_at_100 value: 27.071 - type: map_at_1000 value: 27.173000000000002 - type: map_at_3 value: 22.935 - type: map_at_5 value: 24.573 - type: mrr_at_1 value: 18.669 - type: mrr_at_10 value: 27.682000000000002 - type: mrr_at_100 value: 28.691 - type: mrr_at_1000 value: 28.761 - type: mrr_at_3 value: 24.738 - type: mrr_at_5 value: 26.392 - type: ndcg_at_1 value: 18.669 - type: ndcg_at_10 value: 31.335 - type: ndcg_at_100 value: 36.913000000000004 - type: ndcg_at_1000 value: 39.300000000000004 - type: ndcg_at_3 value: 25.423000000000002 - type: ndcg_at_5 value: 28.262999999999998 - type: precision_at_1 value: 18.669 - type: precision_at_10 value: 5.379 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.214 - type: precision_at_5 value: 8.466 - type: recall_at_1 value: 17.288999999999998 - type: recall_at_10 value: 46.377 - type: recall_at_100 value: 71.53500000000001 - type: recall_at_1000 value: 88.947 - type: recall_at_3 value: 30.581999999999997 - type: recall_at_5 value: 37.354 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 21.795 - type: map_at_10 value: 37.614999999999995 - type: map_at_100 value: 40.037 - type: map_at_1000 value: 40.184999999999995 - type: map_at_3 value: 32.221 - type: map_at_5 value: 35.154999999999994 - type: mrr_at_1 value: 50.358000000000004 - type: mrr_at_10 value: 62.129 - type: mrr_at_100 value: 62.613 - type: mrr_at_1000 value: 62.62 - type: mrr_at_3 value: 59.272999999999996 - type: mrr_at_5 value: 61.138999999999996 - type: ndcg_at_1 value: 50.358000000000004 - type: ndcg_at_10 value: 48.362 - type: ndcg_at_100 value: 55.932 - type: ndcg_at_1000 value: 58.062999999999995 - type: ndcg_at_3 value: 42.111 - type: ndcg_at_5 value: 44.063 - type: precision_at_1 value: 50.358000000000004 - type: precision_at_10 value: 14.677999999999999 - type: precision_at_100 value: 2.2950000000000004 - type: precision_at_1000 value: 0.271 - type: precision_at_3 value: 31.77 - type: precision_at_5 value: 23.375 - type: recall_at_1 value: 21.795 - type: recall_at_10 value: 53.846000000000004 - type: recall_at_100 value: 78.952 - type: recall_at_1000 value: 90.41900000000001 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 44.661 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.728 - type: map_at_10 value: 22.691 - type: map_at_100 value: 31.734 - type: map_at_1000 value: 33.464 - type: map_at_3 value: 16.273 - type: map_at_5 value: 19.016 - type: mrr_at_1 value: 73.25 - type: mrr_at_10 value: 80.782 - type: mrr_at_100 value: 81.01899999999999 - type: mrr_at_1000 value: 81.021 - type: mrr_at_3 value: 79.583 - type: mrr_at_5 value: 80.146 - type: ndcg_at_1 value: 59.62499999999999 - type: ndcg_at_10 value: 46.304 - type: ndcg_at_100 value: 51.23 - type: ndcg_at_1000 value: 58.048 - type: ndcg_at_3 value: 51.541000000000004 - type: ndcg_at_5 value: 48.635 - type: precision_at_1 value: 73.25 - type: precision_at_10 value: 36.375 - type: precision_at_100 value: 11.53 - type: precision_at_1000 value: 2.23 - type: precision_at_3 value: 55.583000000000006 - type: precision_at_5 value: 47.15 - type: recall_at_1 value: 9.728 - type: recall_at_10 value: 28.793999999999997 - type: recall_at_100 value: 57.885 - type: recall_at_1000 value: 78.759 - type: recall_at_3 value: 17.79 - type: recall_at_5 value: 21.733 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.775 - type: f1 value: 41.89794273264891 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 85.378 - type: map_at_10 value: 91.51 - type: map_at_100 value: 91.666 - type: map_at_1000 value: 91.676 - type: map_at_3 value: 90.757 - type: map_at_5 value: 91.277 - type: mrr_at_1 value: 91.839 - type: mrr_at_10 value: 95.49 - type: mrr_at_100 value: 95.493 - type: mrr_at_1000 value: 95.493 - type: mrr_at_3 value: 95.345 - type: mrr_at_5 value: 95.47200000000001 - type: ndcg_at_1 value: 91.839 - type: ndcg_at_10 value: 93.806 - type: ndcg_at_100 value: 94.255 - type: ndcg_at_1000 value: 94.399 - type: ndcg_at_3 value: 93.027 - type: ndcg_at_5 value: 93.51 - type: precision_at_1 value: 91.839 - type: precision_at_10 value: 10.93 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 34.873 - type: precision_at_5 value: 21.44 - type: recall_at_1 value: 85.378 - type: recall_at_10 value: 96.814 - type: recall_at_100 value: 98.386 - type: recall_at_1000 value: 99.21600000000001 - type: recall_at_3 value: 94.643 - type: recall_at_5 value: 95.976 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 32.190000000000005 - type: map_at_10 value: 53.605000000000004 - type: map_at_100 value: 55.550999999999995 - type: map_at_1000 value: 55.665 - type: map_at_3 value: 46.62 - type: map_at_5 value: 50.517999999999994 - type: mrr_at_1 value: 60.34 - type: mrr_at_10 value: 70.775 - type: mrr_at_100 value: 71.238 - type: mrr_at_1000 value: 71.244 - type: mrr_at_3 value: 68.72399999999999 - type: mrr_at_5 value: 69.959 - type: ndcg_at_1 value: 60.34 - type: ndcg_at_10 value: 63.226000000000006 - type: ndcg_at_100 value: 68.60300000000001 - type: ndcg_at_1000 value: 69.901 - type: ndcg_at_3 value: 58.048 - type: ndcg_at_5 value: 59.789 - type: precision_at_1 value: 60.34 - type: precision_at_10 value: 17.130000000000003 - type: precision_at_100 value: 2.29 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 38.323 - type: precision_at_5 value: 27.87 - type: recall_at_1 value: 32.190000000000005 - type: recall_at_10 value: 73.041 - type: recall_at_100 value: 91.31 - type: recall_at_1000 value: 98.104 - type: recall_at_3 value: 53.70399999999999 - type: recall_at_5 value: 62.358999999999995 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 43.511 - type: map_at_10 value: 58.15 - type: map_at_100 value: 58.95399999999999 - type: map_at_1000 value: 59.018 - type: map_at_3 value: 55.31700000000001 - type: map_at_5 value: 57.04900000000001 - type: mrr_at_1 value: 87.022 - type: mrr_at_10 value: 91.32000000000001 - type: mrr_at_100 value: 91.401 - type: mrr_at_1000 value: 91.403 - type: mrr_at_3 value: 90.77 - type: mrr_at_5 value: 91.156 - type: ndcg_at_1 value: 87.022 - type: ndcg_at_10 value: 68.183 - type: ndcg_at_100 value: 70.781 - type: ndcg_at_1000 value: 72.009 - type: ndcg_at_3 value: 64.334 - type: ndcg_at_5 value: 66.449 - type: precision_at_1 value: 87.022 - type: precision_at_10 value: 13.406 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 39.023 - type: precision_at_5 value: 25.080000000000002 - type: recall_at_1 value: 43.511 - type: recall_at_10 value: 67.02900000000001 - type: recall_at_100 value: 77.11 - type: recall_at_1000 value: 85.294 - type: recall_at_3 value: 58.535000000000004 - type: recall_at_5 value: 62.70099999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.0996 - type: ap value: 87.86206089096373 - type: f1 value: 92.07554547510763 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 23.179 - type: map_at_10 value: 35.86 - type: map_at_100 value: 37.025999999999996 - type: map_at_1000 value: 37.068 - type: map_at_3 value: 31.921 - type: map_at_5 value: 34.172000000000004 - type: mrr_at_1 value: 23.926 - type: mrr_at_10 value: 36.525999999999996 - type: mrr_at_100 value: 37.627 - type: mrr_at_1000 value: 37.665 - type: mrr_at_3 value: 32.653 - type: mrr_at_5 value: 34.897 - type: ndcg_at_1 value: 23.910999999999998 - type: ndcg_at_10 value: 42.927 - type: ndcg_at_100 value: 48.464 - type: ndcg_at_1000 value: 49.533 - type: ndcg_at_3 value: 34.910000000000004 - type: ndcg_at_5 value: 38.937 - type: precision_at_1 value: 23.910999999999998 - type: precision_at_10 value: 6.758 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.838000000000001 - type: precision_at_5 value: 10.934000000000001 - type: recall_at_1 value: 23.179 - type: recall_at_10 value: 64.622 - type: recall_at_100 value: 90.135 - type: recall_at_1000 value: 98.301 - type: recall_at_3 value: 42.836999999999996 - type: recall_at_5 value: 52.512 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.59598723210215 - type: f1 value: 96.41913500001952 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 82.89557683538533 - type: f1 value: 63.379319722356264 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 78.93745796906524 - type: f1 value: 75.71616541785902 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 81.41223940820443 - type: f1 value: 81.2877893719078 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 35.03682528325662 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.942529406124 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.459949660460317 - type: mrr value: 32.70509582031616 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 6.497 - type: map_at_10 value: 13.843 - type: map_at_100 value: 17.713 - type: map_at_1000 value: 19.241 - type: map_at_3 value: 10.096 - type: map_at_5 value: 11.85 - type: mrr_at_1 value: 48.916 - type: mrr_at_10 value: 57.764 - type: mrr_at_100 value: 58.251 - type: mrr_at_1000 value: 58.282999999999994 - type: mrr_at_3 value: 55.623999999999995 - type: mrr_at_5 value: 57.018 - type: ndcg_at_1 value: 46.594 - type: ndcg_at_10 value: 36.945 - type: ndcg_at_100 value: 34.06 - type: ndcg_at_1000 value: 43.05 - type: ndcg_at_3 value: 41.738 - type: ndcg_at_5 value: 39.330999999999996 - type: precision_at_1 value: 48.916 - type: precision_at_10 value: 27.43 - type: precision_at_100 value: 8.616 - type: precision_at_1000 value: 2.155 - type: precision_at_3 value: 39.112 - type: precision_at_5 value: 33.808 - type: recall_at_1 value: 6.497 - type: recall_at_10 value: 18.163 - type: recall_at_100 value: 34.566 - type: recall_at_1000 value: 67.15 - type: recall_at_3 value: 11.100999999999999 - type: recall_at_5 value: 14.205000000000002 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 31.916 - type: map_at_10 value: 48.123 - type: map_at_100 value: 49.103 - type: map_at_1000 value: 49.131 - type: map_at_3 value: 43.711 - type: map_at_5 value: 46.323 - type: mrr_at_1 value: 36.181999999999995 - type: mrr_at_10 value: 50.617999999999995 - type: mrr_at_100 value: 51.329 - type: mrr_at_1000 value: 51.348000000000006 - type: mrr_at_3 value: 47.010999999999996 - type: mrr_at_5 value: 49.175000000000004 - type: ndcg_at_1 value: 36.181999999999995 - type: ndcg_at_10 value: 56.077999999999996 - type: ndcg_at_100 value: 60.037 - type: ndcg_at_1000 value: 60.63499999999999 - type: ndcg_at_3 value: 47.859 - type: ndcg_at_5 value: 52.178999999999995 - type: precision_at_1 value: 36.181999999999995 - type: precision_at_10 value: 9.284 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 22.006999999999998 - type: precision_at_5 value: 15.695 - type: recall_at_1 value: 31.916 - type: recall_at_10 value: 77.771 - type: recall_at_100 value: 94.602 - type: recall_at_1000 value: 98.967 - type: recall_at_3 value: 56.528 - type: recall_at_5 value: 66.527 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.486 - type: map_at_10 value: 85.978 - type: map_at_100 value: 86.587 - type: map_at_1000 value: 86.598 - type: map_at_3 value: 83.04899999999999 - type: map_at_5 value: 84.857 - type: mrr_at_1 value: 82.32000000000001 - type: mrr_at_10 value: 88.64 - type: mrr_at_100 value: 88.702 - type: mrr_at_1000 value: 88.702 - type: mrr_at_3 value: 87.735 - type: mrr_at_5 value: 88.36 - type: ndcg_at_1 value: 82.34 - type: ndcg_at_10 value: 89.67 - type: ndcg_at_100 value: 90.642 - type: ndcg_at_1000 value: 90.688 - type: ndcg_at_3 value: 86.932 - type: ndcg_at_5 value: 88.408 - type: precision_at_1 value: 82.34 - type: precision_at_10 value: 13.675999999999998 - type: precision_at_100 value: 1.544 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.24 - type: precision_at_5 value: 25.068 - type: recall_at_1 value: 71.486 - type: recall_at_10 value: 96.844 - type: recall_at_100 value: 99.843 - type: recall_at_1000 value: 99.996 - type: recall_at_3 value: 88.92099999999999 - type: recall_at_5 value: 93.215 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.75758437908334 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 68.03497914092789 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.808 - type: map_at_10 value: 16.059 - type: map_at_100 value: 19.048000000000002 - type: map_at_1000 value: 19.43 - type: map_at_3 value: 10.953 - type: map_at_5 value: 13.363 - type: mrr_at_1 value: 28.7 - type: mrr_at_10 value: 42.436 - type: mrr_at_100 value: 43.599 - type: mrr_at_1000 value: 43.62 - type: mrr_at_3 value: 38.45 - type: mrr_at_5 value: 40.89 - type: ndcg_at_1 value: 28.7 - type: ndcg_at_10 value: 26.346000000000004 - type: ndcg_at_100 value: 36.758 - type: ndcg_at_1000 value: 42.113 - type: ndcg_at_3 value: 24.254 - type: ndcg_at_5 value: 21.506 - type: precision_at_1 value: 28.7 - type: precision_at_10 value: 13.969999999999999 - type: precision_at_100 value: 2.881 - type: precision_at_1000 value: 0.414 - type: precision_at_3 value: 22.933 - type: precision_at_5 value: 19.220000000000002 - type: recall_at_1 value: 5.808 - type: recall_at_10 value: 28.310000000000002 - type: recall_at_100 value: 58.475 - type: recall_at_1000 value: 84.072 - type: recall_at_3 value: 13.957 - type: recall_at_5 value: 19.515 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.39274129958557 - type: cos_sim_spearman value: 79.78021235170053 - type: euclidean_pearson value: 79.35335401300166 - type: euclidean_spearman value: 79.7271870968275 - type: manhattan_pearson value: 79.35256263340601 - type: manhattan_spearman value: 79.76036386976321 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.99130429246708 - type: cos_sim_spearman value: 73.88322811171203 - type: euclidean_pearson value: 80.7569419170376 - type: euclidean_spearman value: 73.82542155409597 - type: manhattan_pearson value: 80.79468183847625 - type: manhattan_spearman value: 73.87027144047784 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.88548789489907 - type: cos_sim_spearman value: 85.07535893847255 - type: euclidean_pearson value: 84.6637222061494 - type: euclidean_spearman value: 85.14200626702456 - type: manhattan_pearson value: 84.75327892344734 - type: manhattan_spearman value: 85.24406181838596 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.88140039325008 - type: cos_sim_spearman value: 79.61211268112362 - type: euclidean_pearson value: 81.29639728816458 - type: euclidean_spearman value: 79.51284578041442 - type: manhattan_pearson value: 81.3381797137111 - type: manhattan_spearman value: 79.55683684039808 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.16716737270485 - type: cos_sim_spearman value: 86.14823841857738 - type: euclidean_pearson value: 85.36325733440725 - type: euclidean_spearman value: 86.04919691402029 - type: manhattan_pearson value: 85.3147511385052 - type: manhattan_spearman value: 86.00676205857764 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 80.34266645861588 - type: cos_sim_spearman value: 81.59914035005882 - type: euclidean_pearson value: 81.15053076245988 - type: euclidean_spearman value: 81.52776915798489 - type: manhattan_pearson value: 81.1819647418673 - type: manhattan_spearman value: 81.57479527353556 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.38263326821439 - type: cos_sim_spearman value: 89.10946308202642 - type: euclidean_pearson value: 88.87831312540068 - type: euclidean_spearman value: 89.03615865973664 - type: manhattan_pearson value: 88.79835539970384 - type: manhattan_spearman value: 88.9766156339753 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 70.1574915581685 - type: cos_sim_spearman value: 70.59144980004054 - type: euclidean_pearson value: 71.43246306918755 - type: euclidean_spearman value: 70.5544189562984 - type: manhattan_pearson value: 71.4071414609503 - type: manhattan_spearman value: 70.31799126163712 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.36215796635351 - type: cos_sim_spearman value: 83.07276756467208 - type: euclidean_pearson value: 83.06690453635584 - type: euclidean_spearman value: 82.9635366303289 - type: manhattan_pearson value: 83.04994049700815 - type: manhattan_spearman value: 82.98120125356036 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.92530011616722 - type: mrr value: 96.21826793395421 - task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 65.75 - type: map_at_10 value: 77.701 - type: map_at_100 value: 78.005 - type: map_at_1000 value: 78.006 - type: map_at_3 value: 75.48 - type: map_at_5 value: 76.927 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 78.511 - type: mrr_at_100 value: 78.704 - type: mrr_at_1000 value: 78.704 - type: mrr_at_3 value: 77 - type: mrr_at_5 value: 78.083 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 82.42699999999999 - type: ndcg_at_100 value: 83.486 - type: ndcg_at_1000 value: 83.511 - type: ndcg_at_3 value: 78.96300000000001 - type: ndcg_at_5 value: 81.028 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.667 - type: precision_at_100 value: 1.127 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 31.333 - type: precision_at_5 value: 20.133000000000003 - type: recall_at_1 value: 65.75 - type: recall_at_10 value: 95.578 - type: recall_at_100 value: 99.833 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 86.506 - type: recall_at_5 value: 91.75 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.75247524752476 - type: cos_sim_ap value: 94.16065078045173 - type: cos_sim_f1 value: 87.22986247544205 - type: cos_sim_precision value: 85.71428571428571 - type: cos_sim_recall value: 88.8 - type: dot_accuracy value: 99.74554455445545 - type: dot_ap value: 93.90633887037264 - type: dot_f1 value: 86.9873417721519 - type: dot_precision value: 88.1025641025641 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.75247524752476 - type: euclidean_ap value: 94.17466319018055 - type: euclidean_f1 value: 87.3405299313052 - type: euclidean_precision value: 85.74181117533719 - type: euclidean_recall value: 89 - type: manhattan_accuracy value: 99.75445544554455 - type: manhattan_ap value: 94.27688371923577 - type: manhattan_f1 value: 87.74002954209749 - type: manhattan_precision value: 86.42095053346266 - type: manhattan_recall value: 89.1 - type: max_accuracy value: 99.75445544554455 - type: max_ap value: 94.27688371923577 - type: max_f1 value: 87.74002954209749 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 71.26500637517056 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 39.17507906280528 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.4848744828509 - type: mrr value: 53.33678168236992 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.599864323827887 - type: cos_sim_spearman value: 30.91116204665598 - type: dot_pearson value: 30.82637894269936 - type: dot_spearman value: 30.957573868416066 - task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.892 - type: map_at_100 value: 11.586 - type: map_at_1000 value: 27.761999999999997 - type: map_at_3 value: 0.653 - type: map_at_5 value: 1.028 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 94 - type: mrr_at_100 value: 94 - type: mrr_at_1000 value: 94 - type: mrr_at_3 value: 94 - type: mrr_at_5 value: 94 - type: ndcg_at_1 value: 82 - type: ndcg_at_10 value: 77.48899999999999 - type: ndcg_at_100 value: 60.141 - type: ndcg_at_1000 value: 54.228 - type: ndcg_at_3 value: 82.358 - type: ndcg_at_5 value: 80.449 - type: precision_at_1 value: 88 - type: precision_at_10 value: 82.19999999999999 - type: precision_at_100 value: 61.760000000000005 - type: precision_at_1000 value: 23.684 - type: precision_at_3 value: 88 - type: precision_at_5 value: 85.6 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 2.117 - type: recall_at_100 value: 14.985000000000001 - type: recall_at_1000 value: 51.107 - type: recall_at_3 value: 0.688 - type: recall_at_5 value: 1.1039999999999999 - task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.3040000000000003 - type: map_at_10 value: 9.025 - type: map_at_100 value: 15.312999999999999 - type: map_at_1000 value: 16.954 - type: map_at_3 value: 4.981 - type: map_at_5 value: 6.32 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 39.835 - type: mrr_at_100 value: 40.8 - type: mrr_at_1000 value: 40.8 - type: mrr_at_3 value: 35.034 - type: mrr_at_5 value: 37.687 - type: ndcg_at_1 value: 22.448999999999998 - type: ndcg_at_10 value: 22.545 - type: ndcg_at_100 value: 35.931999999999995 - type: ndcg_at_1000 value: 47.665 - type: ndcg_at_3 value: 23.311 - type: ndcg_at_5 value: 22.421 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 20.408 - type: precision_at_100 value: 7.815999999999999 - type: precision_at_1000 value: 1.553 - type: precision_at_3 value: 25.169999999999998 - type: precision_at_5 value: 23.265 - type: recall_at_1 value: 2.3040000000000003 - type: recall_at_10 value: 15.693999999999999 - type: recall_at_100 value: 48.917 - type: recall_at_1000 value: 84.964 - type: recall_at_3 value: 6.026 - type: recall_at_5 value: 9.066 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 82.6074 - type: ap value: 23.187467098602013 - type: f1 value: 65.36829506379657 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 63.16355404640635 - type: f1 value: 63.534725639863346 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.91004094411276 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.55301901412649 - type: cos_sim_ap value: 75.25312618556728 - type: cos_sim_f1 value: 68.76561719140429 - type: cos_sim_precision value: 65.3061224489796 - type: cos_sim_recall value: 72.61213720316623 - type: dot_accuracy value: 86.29671574178936 - type: dot_ap value: 75.11910195501207 - type: dot_f1 value: 68.44048376830045 - type: dot_precision value: 66.12546125461255 - type: dot_recall value: 70.92348284960423 - type: euclidean_accuracy value: 86.5828217202122 - type: euclidean_ap value: 75.22986344900924 - type: euclidean_f1 value: 68.81267797449549 - type: euclidean_precision value: 64.8238861674831 - type: euclidean_recall value: 73.3245382585752 - type: manhattan_accuracy value: 86.61262442629791 - type: manhattan_ap value: 75.24401608557328 - type: manhattan_f1 value: 68.80473982483257 - type: manhattan_precision value: 67.21187720181177 - type: manhattan_recall value: 70.47493403693932 - type: max_accuracy value: 86.61262442629791 - type: max_ap value: 75.25312618556728 - type: max_f1 value: 68.81267797449549 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.10688089416696 - type: cos_sim_ap value: 84.17862178779863 - type: cos_sim_f1 value: 76.17305208781748 - type: cos_sim_precision value: 71.31246641590543 - type: cos_sim_recall value: 81.74468740375731 - type: dot_accuracy value: 88.1844995536927 - type: dot_ap value: 84.33816725235876 - type: dot_f1 value: 76.43554032918746 - type: dot_precision value: 74.01557767200346 - type: dot_recall value: 79.0190945488143 - type: euclidean_accuracy value: 88.07001203089223 - type: euclidean_ap value: 84.12267000814985 - type: euclidean_f1 value: 76.12232600180778 - type: euclidean_precision value: 74.50604541433205 - type: euclidean_recall value: 77.81028641823221 - type: manhattan_accuracy value: 88.06419063142779 - type: manhattan_ap value: 84.11648917164187 - type: manhattan_f1 value: 76.20579953925474 - type: manhattan_precision value: 72.56772755762935 - type: manhattan_recall value: 80.22790267939637 - type: max_accuracy value: 88.1844995536927 - type: max_ap value: 84.33816725235876 - type: max_f1 value: 76.43554032918746 --- <!-- **English** | [中文](./README_zh.md) --> # gte-large-en-v1.5 We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct), a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. <!-- Provide a longer summary of what this model is. --> - **Developed by:** Institute for Intelligent Computing, Alibaba Group - **Model type:** Text Embeddings - **Paper:** Coming soon. <!-- - **Demo [optional]:** [More Information Needed] --> ### Model list | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 | |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | ## How to Get Started with the Model Use the code below to get started with the model. ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model_path = 'Alibaba-NLP/gte-large-en-v1.5' tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModel.from_pretrained(model_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** Use with sentence-transformers: ```python # Requires sentence_transformers>=2.7.0 from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True) embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` Use with `transformers.js`: ```js // npm i @xenova/transformers import { pipeline, dot } from '@xenova/transformers'; // Create feature extraction pipeline const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', { quantized: false, // Comment out this line to use the quantized version }); // Generate sentence embeddings const sentences = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); // Compute similarity scores const [source_embeddings, ...document_embeddings ] = output.tolist(); const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x)); console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899] ``` ## Training Details ### Training Data - Masked language modeling (MLM): `c4-en` - Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data - Supervised contrastive fine-tuning: GTE(https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data ### Training Procedure To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. The entire training process is as follows: - MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000 - MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000 - MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000 - WSC: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000 - Fine-tuning: TODO ## Evaluation ### MTEB The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | ### LoCo | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | ## Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

CompVis/stable-diffusion-v1-4

CompVis

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ### PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Note**: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision: ```py import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to(device) pipe.enable_attention_slicing() prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` To swap out the noise scheduler, pass it to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` **Note**: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide four checkpoints, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

meta-llama/Meta-Llama-3-8B

meta-llama

transformers

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3 extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. 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If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python >>> import transformers >>> import torch >>> model_id = "meta-llama/Meta-Llama-3-8B" >>> pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) >>> pipeline("Hey how are you doing today?") ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

jonatasgrosman/wav2vec2-large-xlsr-53-japanese

jonatasgrosman

transformers

automatic-speech-recognition

--- language: ja datasets: - common_voice metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Japanese by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ja type: common_voice args: ja metrics: - name: Test WER type: wer value: 81.80 - name: Test CER type: cer value: 20.16 --- # Fine-tuned XLSR-53 large model for speech recognition in Japanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "ja" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 人母は重にきね起くさいがしている | | 財布をなくしたので、交番へ行きます。 | 財布をなく手端ので勾番へ行きます | | 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ宮屋のお親じ旅館の主に医者をはじめ交際のアル人トに聞いて回ったらみんな私より収入が多いはなうに税金は安い | | 新しい靴をはいて出かけます。 | だらしい靴をはいて出かけます | | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表弁することがある | | 松井さんはサッカーより野球のほうが上手です。 | 松井さんはサッカーより野球のほうが上手です | | 新しいお皿を使います。 | 新しいお皿を使います | | 結婚以来三年半ぶりの東京も、旧友とのお酒も、夜行列車も、駅で寝て、朝を待つのも久しぶりだ。 | 結婚ル二来三年半降りの東京も吸とのお酒も野越者も駅で寝て朝を待つの久しぶりた | | これまで、少年野球、ママさんバレーなど、地域スポーツを支え、市民に密着してきたのは、無数のボランティアだった。 | これまで少年野球<unk>三バレーなど地域スポーツを支え市民に満着してきたのは娘数のボランティアだった | | 靴を脱いで、スリッパをはきます。 | 靴を脱いでスイパーをはきます | ## Evaluation The model can be evaluated as follows on the Japanese test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "ja" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese" DEVICE = "cuda" CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"] test_dataset = load_dataset("common_voice", LANG_ID, split="test") wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) model.to(DEVICE) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): with warnings.catch_warnings(): warnings.simplefilter("ignore") speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) predictions = [x.upper() for x in result["pred_strings"]] references = [x.upper() for x in result["sentence"]] print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") ``` **Test Result**: In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. | Model | WER | CER | | ------------- | ------------- | ------------- | | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | **81.80%** | **20.16%** | | vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | 23.40% | | qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 70.77% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-japanese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {J}apanese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese}}, year={2021} } ```

dmis-lab/biobert-v1.1

dmis-lab

transformers

feature-extraction

Entry not found

prithivida/parrot_paraphraser_on_T5

prithivida

transformers

text2text-generation

# Parrot ## 1. What is Parrot? Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the [github page](https://github.com/PrithivirajDamodaran/Parrot) ### Installation ```python pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git ``` ### Quickstart ```python from parrot import Parrot import torch import warnings warnings.filterwarnings("ignore") ''' uncomment to get reproducable paraphrase generations def random_state(seed): torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) random_state(1234) ''' #Init models (make sure you init ONLY once if you integrate this to your code) parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5", use_gpu=False) phrases = ["Can you recommed some upscale restaurants in Newyork?", "What are the famous places we should not miss in Russia?" ] for phrase in phrases: print("-"*100) print("Input_phrase: ", phrase) print("-"*100) para_phrases = parrot.augment(input_phrase=phrase) for para_phrase in para_phrases: print(para_phrase) ``` ``` ---------------------------------------------------------------------- Input_phrase: Can you recommed some upscale restaurants in Newyork? ---------------------------------------------------------------------- list some excellent restaurants to visit in new york city? what upscale restaurants do you recommend in new york? i want to try some upscale restaurants in new york? recommend some upscale restaurants in newyork? can you recommend some high end restaurants in newyork? can you recommend some upscale restaurants in new york? can you recommend some upscale restaurants in newyork? ---------------------------------------------------------------------- Input_phrase: What are the famous places we should not miss in Russia ---------------------------------------------------------------------- what should we not miss when visiting russia? recommend some of the best places to visit in russia? list some of the best places to visit in russia? can you list the top places to visit in russia? show the places that we should not miss in russia? list some famous places which we should not miss in russia? ``` ### Knobs ```python para_phrases = parrot.augment(input_phrase=phrase, diversity_ranker="levenshtein", do_diverse=False, max_return_phrases = 10, max_length=32, adequacy_threshold = 0.99, fluency_threshold = 0.90) ``` ## 2. Why Parrot? **Huggingface** lists [12 paraphrase models,](https://huggingface.co/models?pipeline_tag=text2text-generation&search=paraphrase) **RapidAPI** lists 7 fremium and commercial paraphrasers like [QuillBot](https://rapidapi.com/search/paraphrase?section=apis&page=1), Rasa has discussed an experimental paraphraser for augmenting text data [here](https://forum.rasa.com/t/paraphrasing-for-nlu-data-augmentation-experimental/27744), Sentence-transfomers offers a [paraphrase mining utility](https://www.sbert.net/examples/applications/paraphrase-mining/README.html) and [NLPAug](https://github.com/makcedward/nlpaug) offers word level augmentation with a [PPDB](http://paraphrase.org/#/download) (a multi-million paraphrase database). While these attempts at paraphrasing are great, there are still some gaps and paraphrasing is NOT yet a mainstream option for text augmentation in building NLU models....Parrot is a humble attempt to fill some of these gaps. **What is a good paraphrase?** Almost all conditioned text generation models are validated on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct english (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But [a good paraphrase](https://www.aclweb.org/anthology/D10-1090.pdf) should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the **3 key metrics** that measures the quality of paraphrases are: - **Adequacy** (Is the meaning preserved adequately?) - **Fluency** (Is the paraphrase fluent English?) - **Diversity (Lexical / Phrasal / Syntactical)** (How much has the paraphrase changed the original sentence?) *Parrot offers knobs to control Adequacy, Fluency and Diversity as per your needs.* **What makes a paraphraser a good augmentor?** For training a NLU model we just don't need a lot of utterances but utterances with intents and slots/entities annotated. Typical flow would be: - Given an **input utterance + input annotations** a good augmentor spits out N **output paraphrases** while preserving the intent and slots. - The output paraphrases are then converted into annotated data using the input annotations that we got in step 1. - The annotated data created out of the output paraphrases then makes the training dataset for your NLU model. But in general being a generative model paraphrasers doesn't guarantee to preserve the slots/entities. So the ability to generate high quality paraphrases in a constrained fashion without trading off the intents and slots for lexical dissimilarity makes a paraphraser a good augmentor. *More on this in section 3 below* ## 3. Scope In the space of conversational engines, knowledge bots are to which **we ask questions** like *"when was the Berlin wall teared down?"*, transactional bots are to which **we give commands** like *"Turn on the music please"* and voice assistants are the ones which can do both answer questions and action our commands. Parrot mainly foucses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models. (*So usually people neither type out or yell out long paragraphs to conversational interfaces. Hence the pre-trained model is trained on text samples of maximum length of 32.*) *While Parrot predominantly aims to be a text augmentor for building good NLU models, it can also be used as a pure-play paraphraser.*

google/flan-t5-base

google

transformers

text2text-generation

--- language: - en - fr - ro - de - multilingual tags: - text2text-generation widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed license: apache-2.0 --- # Model Card for FLAN-T5 base <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg" alt="drawing" width="600"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) # TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). # Model Details ## Model Description - **Model type:** Language model - **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian - **License:** Apache 2.0 - **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", torch_dtype=torch.float16) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", load_in_8bit=True) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> # Uses ## Direct Use and Downstream Use The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that: > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. ## Ethical considerations and risks > Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. ## Known Limitations > Flan-T5 has not been tested in real world applications. ## Sensitive Use: > Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech. # Training Details ## Training Data The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):  ## Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf): > These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). # Evaluation ## Testing Data, Factors & Metrics The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:  For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf). ## Results For full results for FLAN-T5-Base, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4. - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @misc{https://doi.org/10.48550/arxiv.2210.11416, doi = {10.48550/ARXIV.2210.11416}, url = {https://arxiv.org/abs/2210.11416}, author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Scaling Instruction-Finetuned Language Models}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ## Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=9.16&mnli_lp=nan&20_newsgroup=3.34&ag_news=1.49&amazon_reviews_multi=0.21&anli=13.91&boolq=16.75&cb=23.12&cola=9.97&copa=34.50&dbpedia=6.90&esnli=5.37&financial_phrasebank=18.66&imdb=0.33&isear=1.37&mnli=11.74&mrpc=16.63&multirc=6.24&poem_sentiment=14.62&qnli=3.41&qqp=6.18&rotten_tomatoes=2.98&rte=24.26&sst2=0.67&sst_5bins=5.44&stsb=20.68&trec_coarse=3.95&trec_fine=10.73&tweet_ev_emoji=13.39&tweet_ev_emotion=4.62&tweet_ev_hate=3.46&tweet_ev_irony=9.04&tweet_ev_offensive=1.69&tweet_ev_sentiment=0.75&wic=14.22&wnli=9.44&wsc=5.53&yahoo_answers=4.14&model_name=google%2Fflan-t5-base&base_name=google%2Ft5-v1_1-base) using google/flan-t5-base as a base model yields average score of 77.98 in comparison to 68.82 by google/t5-v1_1-base. The model is ranked 1st among all tested models for the google/t5-v1_1-base architecture as of 06/02/2023 Results: | 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|-------:|--------:|----------------:| | 86.2188 | 89.6667 | 67.12 | 51.9688 | 82.3242 | 78.5714 | 80.1534 | 75 | 77.6667 | 90.9507 | 85.4 | 93.324 | 72.425 | 87.2457 | 89.4608 | 62.3762 | 82.6923 | 92.7878 | 89.7724 | 89.0244 | 84.8375 | 94.3807 | 57.2851 | 89.4759 | 97.2 | 92.8 | 46.848 | 80.2252 | 54.9832 | 76.6582 | 84.3023 | 70.6366 | 70.0627 | 56.338 | 53.8462 | 73.4 | For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)

meta-llama/Llama-2-7b-hf

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. 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You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. 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Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

pysentimiento/robertuito-sentiment-analysis

pysentimiento

pysentimiento

--- language: - es library_name: pysentimiento tags: - twitter - sentiment-analysis --- # Sentiment Analysis in Spanish ## robertuito-sentiment-analysis Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets. Uses `POS`, `NEG`, `NEU` labels. ## Usage Use it directly with [pysentimiento](https://github.com/pysentimiento/pysentimiento) ```python from pysentimiento import create_analyzer analyzer = create_analyzer(task="sentiment", lang="es") analyzer.predict("Qué gran jugador es Messi") # returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000}) ``` ## Results Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores | model | emotion | hate_speech | irony | sentiment | |:--------------|:--------------|:--------------|:--------------|:--------------| | robertuito | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 | | roberta | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 | | bertin | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 | | beto_uncased | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 | | beto_cased | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 | | mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 | | biGRU | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 | Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B ## Citation If you use this model in your research, please cite pysentimiento, RoBERTuito and TASS papers: ```latex @article{perez2021pysentimiento, title={pysentimiento: a python toolkit for opinion mining and social NLP tasks}, author={P{\'e}rez, Juan Manuel and Rajngewerc, Mariela and Giudici, Juan Carlos and Furman, Dami{\'a}n A and Luque, Franco and Alemany, Laura Alonso and Mart{\'\i}nez, Mar{\'\i}a Vanina}, journal={arXiv preprint arXiv:2106.09462}, year={2021} } @inproceedings{perez-etal-2022-robertuito, title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish", author = "P{\'e}rez, Juan Manuel and Furman, Dami{\'a}n Ariel and Alonso Alemany, Laura and Luque, Franco M.", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.785", pages = "7235--7243", abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.", } @inproceedings{garcia2020overview, title={Overview of TASS 2020: Introducing emotion detection}, author={Garc{\'\i}a-Vega, Manuel and D{\'\i}az-Galiano, MC and Garc{\'\i}a-Cumbreras, MA and Del Arco, FMP and Montejo-R{\'a}ez, A and Jim{\'e}nez-Zafra, SM and Mart{\'\i}nez C{\'a}mara, E and Aguilar, CA and Cabezudo, MAS and Chiruzzo, L and others}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020) Co-Located with 36th Conference of the Spanish Society for Natural Language Processing (SEPLN 2020), M{\'a}laga, Spain}, pages={163--170}, year={2020} } ```

MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli

MoritzLaurer

transformers

zero-shot-classification

--- language: - en license: mit tags: - text-classification - zero-shot-classification datasets: - multi_nli - facebook/anli - fever metrics: - accuracy pipeline_tag: zero-shot-classification model-index: - name: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli results: - task: type: natural-language-inference name: Natural Language Inference dataset: name: anli type: anli config: plain_text split: test_r3 metrics: - type: accuracy value: 0.495 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWViYjQ5YTZlYjU4NjQyN2NhOTVhNjFjNGQyMmFiNmQyZjRkOTdhNzJmNjc3NGU4MmY0MjYyMzY5MjZhYzE0YiIsInZlcnNpb24iOjF9.S8pIQ7gEGokd_wKXMi6Bc3B2DThIP3cvVkTFErZ-2JxXTSCy1TBuulY3dzGfaiP7kTHbL52OuBhG_-wb7Ue9DQ - type: precision value: 0.4984740618243923 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTllZDU3NmVmYjk4ZmYzNjAwNzExMGZjNDMzOWRkZjRjMTRhNzhlZmI0ZmNlM2E0Mzk4OWE5NTM5MTYyYWU5NCIsInZlcnNpb24iOjF9.WHz_TUJgPVn-rU-9vBCDdmSMOuWzADwr09rJY6ktqRM46zytbyWs7Vcm7jqDrTkfU-rp0_7IyoNv_xEsKhJbBA - 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This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf). For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli. ### How to use the model #### Simple zero-shot classification pipeline ```python #!pip install transformers[sentencepiece] from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli") sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." hypothesis = "The movie was good." input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data DeBERTa-v3-base-mnli-fever-anli was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. ### Training procedure DeBERTa-v3-base-mnli-fever-anli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=3, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=32, # batch size per device during training per_device_eval_batch_size=32, # batch size for evaluation warmup_ratio=0.1, # number of warmup steps for learning rate scheduler weight_decay=0.06, # strength of weight decay fp16=True # mixed precision training ) ``` ### Eval results The model was evaluated using the test sets for MultiNLI and ANLI and the dev set for Fever-NLI. The metric used is accuracy. mnli-m | mnli-mm | fever-nli | anli-all | anli-r3 ---------|----------|---------|----------|---------- 0.903 | 0.903 | 0.777 | 0.579 | 0.495 ## Limitations and bias Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ### Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ### Debugging and issues Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. Also make sure to install sentencepiece to avoid tokenizer errors. Run: `pip install transformers[sentencepiece]` or `pip install sentencepiece` ## Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.65&mnli_lp=nan&20_newsgroup=-0.61&ag_news=-0.01&amazon_reviews_multi=0.46&anli=0.84&boolq=2.12&cb=16.07&cola=-0.76&copa=8.60&dbpedia=-0.40&esnli=-0.29&financial_phrasebank=-1.98&imdb=-0.47&isear=-0.22&mnli=-0.21&mrpc=0.50&multirc=1.91&poem_sentiment=1.73&qnli=0.07&qqp=-0.37&rotten_tomatoes=-0.74&rte=3.94&sst2=-0.45&sst_5bins=0.07&stsb=1.27&trec_coarse=-0.16&trec_fine=0.18&tweet_ev_emoji=-0.93&tweet_ev_emotion=-1.33&tweet_ev_hate=-1.67&tweet_ev_irony=-5.46&tweet_ev_offensive=-0.17&tweet_ev_sentiment=-0.11&wic=-0.21&wnli=-1.20&wsc=4.18&yahoo_answers=-0.70&model_name=MoritzLaurer%2FDeBERTa-v3-base-mnli-fever-anli&base_name=microsoft%2Fdeberta-v3-base) using MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli as a base model yields average score of 79.69 in comparison to 79.04 by microsoft/deberta-v3-base. The model is ranked 2nd among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023. Results: | 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |---------------:|----------:|-----------------------:|-------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|-------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| | 85.8072 | 90.4333 | 67.32 | 59.625 | 85.107 | 91.0714 | 85.8102 | 67 | 79.0333 | 91.6327 | 82.5 | 94.02 | 71.6428 | 89.5749 | 89.7059 | 64.1708 | 88.4615 | 93.575 | 91.4148 | 89.6811 | 86.2816 | 94.6101 | 57.0588 | 91.5508 | 97.6 | 91.2 | 45.264 | 82.6179 | 54.5455 | 74.3622 | 84.8837 | 71.6949 | 71.0031 | 69.0141 | 68.2692 | 71.3333 | For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)

ckiplab/bert-base-chinese-ner

ckiplab

transformers

token-classification

--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - bert - zh license: gpl-3.0 --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。

cardiffnlp/twitter-roberta-base-sentiment

cardiffnlp

transformers

text-classification

--- datasets: - tweet_eval language: - en --- # Twitter-roBERTa-base for Sentiment Analysis This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)). - Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). <b>Labels</b>: 0 -> Negative; 1 -> Neutral; 2 -> Positive <b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details. ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='sentiment' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) positive 0.8466 2) neutral 0.1458 3) negative 0.0076 ``` ### BibTeX entry and citation info Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model. ```bibtex @inproceedings{barbieri-etal-2020-tweeteval, title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification", author = "Barbieri, Francesco and Camacho-Collados, Jose and Espinosa Anke, Luis and Neves, Leonardo", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.148", doi = "10.18653/v1/2020.findings-emnlp.148", pages = "1644--1650" } ```

theainerd/Wav2Vec2-large-xlsr-hindi

theainerd

transformers

automatic-speech-recognition

--- language: - hi --- # Wav2Vec2-Large-XLSR-53-hindi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the hindi test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "hi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 72.62 % ## Training The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1m-F7et3CHT_kpFqg7UffTIwnUV9AKgrg?usp=sharing)

facebook/m2m100_418M

facebook

transformers

text2text-generation

--- language: - multilingual - af - am - ar - ast - az - ba - be - bg - bn - br - bs - ca - ceb - cs - cy - da - de - el - en - es - et - fa - ff - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - ht - hu - hy - id - ig - ilo - is - it - ja - jv - ka - kk - km - kn - ko - lb - lg - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - ns - oc - or - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - th - tl - tn - tr - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu license: mit --- # M2M100 418M M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` ```python from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chinese_text = "生活就像一盒巧克力。" model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") # translate Hindi to French tokenizer.src_lang = "hi" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "La vie est comme une boîte de chocolat." # translate Chinese to English tokenizer.src_lang = "zh" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Life is like a box of chocolate." ``` See the [model hub](https://huggingface.co/models?filter=m2m_100) to look for more fine-tuned versions. ## Languages covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu) ## BibTeX entry and citation info ``` @misc{fan2020englishcentric, title={Beyond English-Centric Multilingual Machine Translation}, author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin}, year={2020}, eprint={2010.11125}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

runwayml/stable-diffusion-inpainting

runwayml

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: false library_name: diffusers extra_gated_prompt: |- One more step before getting this model. This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well. extra_gated_fields: I have read the License and agree with its terms: checkbox --- Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. [](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) :-------------------------:|:-------------------------:| ## Examples: You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```python from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, ) prompt = "Face of a yellow cat, high resolution, sitting on a park bench" #image and mask_image should be PIL images. #The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0] image.save("./yellow_cat_on_park_bench.png") ``` **How it works:** `image` | `mask_image` :-------------------------:|:-------------------------:| <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/> `prompt` | `Output` :-------------------------:|:-------------------------:| <span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/> ### Original GitHub Repository 1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt) 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/runwayml/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt` which were trained as follows, - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-4.ckpt`: Resumed from stable-diffusion-v1-2.225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. - `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Inpainting Evaluation To assess the performance of the inpainting model, we used the same evaluation protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed prompt of `photograph of a beautiful empty scene, highest quality settings`. | Model | FID | LPIPS | |-----------------------------|------|------------------| | Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) | | Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) | | CoModGAN | 1.82 | 0.15 | | LaMa | 2.21 | 0.134 (+- 0.080) | ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

superb/wav2vec2-base-superb-sid

superb

transformers

audio-classification

--- language: en datasets: - superb tags: - speech - audio - wav2vec2 - audio-classification widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav license: apache-2.0 --- # Wav2Vec2-Base for Speaker Identification ## Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) ## Task and dataset description Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) dataset is adopted For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sid-speaker-identification). ## Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "si", split="test") classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-sid") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example # load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "si", split="test") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid") # compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:2]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] ``` ## Eval results The evaluation metric is accuracy. | | **s3prl** | **transformers** | |--------|-----------|------------------| |**test**| `0.7518` | `0.7518` | ### BibTeX entry and citation info ```bibtex @article{yang2021superb, title={SUPERB: Speech processing Universal PERformance Benchmark}, author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others}, journal={arXiv preprint arXiv:2105.01051}, year={2021} } ```

mixedbread-ai/mxbai-embed-large-v1

mixedbread-ai

sentence-transformers

feature-extraction

--- tags: - mteb - transformers.js - transformers model-index: - name: mxbai-angle-large-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.044776119403 - type: ap value: 37.7362433623053 - type: f1 value: 68.92736573359774 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.84025000000001 - type: ap value: 90.93190875404055 - type: f1 value: 93.8297833897293 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.184 - type: f1 value: 48.74163227751588 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 41.252 - type: map_at_10 value: 57.778 - type: map_at_100 value: 58.233000000000004 - type: map_at_1000 value: 58.23700000000001 - type: map_at_3 value: 53.449999999999996 - type: map_at_5 value: 56.376000000000005 - type: mrr_at_1 value: 41.679 - type: mrr_at_10 value: 57.92699999999999 - type: mrr_at_100 value: 58.389 - type: mrr_at_1000 value: 58.391999999999996 - type: mrr_at_3 value: 53.651 - type: mrr_at_5 value: 56.521 - type: ndcg_at_1 value: 41.252 - type: ndcg_at_10 value: 66.018 - type: ndcg_at_100 value: 67.774 - type: ndcg_at_1000 value: 67.84400000000001 - type: ndcg_at_3 value: 57.372 - type: ndcg_at_5 value: 62.646 - type: precision_at_1 value: 41.252 - type: precision_at_10 value: 9.189 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.902 - type: precision_at_5 value: 16.302 - type: recall_at_1 value: 41.252 - type: recall_at_10 value: 91.892 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 68.706 - type: recall_at_5 value: 81.50800000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.97294504317859 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.98071077674629 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 65.16477858490782 - type: mrr value: 78.23583080508287 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.6277629421789 - type: cos_sim_spearman value: 88.4056288400568 - type: euclidean_pearson value: 87.94871847578163 - type: euclidean_spearman value: 88.4056288400568 - type: manhattan_pearson value: 87.73271254229648 - type: manhattan_spearman value: 87.91826833762677 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.81818181818181 - type: f1 value: 87.79879337316918 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.91773608582761 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.73059477462478 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.745999999999995 - type: map_at_10 value: 43.632 - type: map_at_100 value: 45.206 - type: map_at_1000 value: 45.341 - type: map_at_3 value: 39.956 - type: map_at_5 value: 42.031 - type: mrr_at_1 value: 39.485 - type: mrr_at_10 value: 49.537 - type: mrr_at_100 value: 50.249 - type: mrr_at_1000 value: 50.294000000000004 - type: mrr_at_3 value: 46.757 - type: mrr_at_5 value: 48.481 - type: ndcg_at_1 value: 39.485 - type: ndcg_at_10 value: 50.058 - type: ndcg_at_100 value: 55.586 - type: ndcg_at_1000 value: 57.511 - type: ndcg_at_3 value: 44.786 - type: ndcg_at_5 value: 47.339999999999996 - type: precision_at_1 value: 39.485 - type: precision_at_10 value: 9.557 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.412 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 32.745999999999995 - type: recall_at_10 value: 62.056 - type: recall_at_100 value: 85.088 - type: recall_at_1000 value: 96.952 - type: recall_at_3 value: 46.959 - type: recall_at_5 value: 54.06999999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.898 - type: map_at_10 value: 42.142 - type: map_at_100 value: 43.349 - type: map_at_1000 value: 43.483 - type: map_at_3 value: 39.18 - type: map_at_5 value: 40.733000000000004 - type: mrr_at_1 value: 39.617999999999995 - type: mrr_at_10 value: 47.922 - type: mrr_at_100 value: 48.547000000000004 - type: mrr_at_1000 value: 48.597 - type: mrr_at_3 value: 45.86 - type: mrr_at_5 value: 46.949000000000005 - type: ndcg_at_1 value: 39.617999999999995 - type: ndcg_at_10 value: 47.739 - type: ndcg_at_100 value: 51.934999999999995 - type: ndcg_at_1000 value: 54.007000000000005 - type: ndcg_at_3 value: 43.748 - type: ndcg_at_5 value: 45.345 - type: precision_at_1 value: 39.617999999999995 - type: precision_at_10 value: 8.962 - type: precision_at_100 value: 1.436 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 21.083 - type: precision_at_5 value: 14.752 - type: recall_at_1 value: 31.898 - type: recall_at_10 value: 57.587999999999994 - type: recall_at_100 value: 75.323 - type: recall_at_1000 value: 88.304 - type: recall_at_3 value: 45.275 - type: recall_at_5 value: 49.99 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.458 - type: map_at_10 value: 52.942 - type: map_at_100 value: 53.974 - type: map_at_1000 value: 54.031 - type: map_at_3 value: 49.559999999999995 - type: map_at_5 value: 51.408 - type: mrr_at_1 value: 46.27 - type: mrr_at_10 value: 56.31699999999999 - type: mrr_at_100 value: 56.95099999999999 - type: mrr_at_1000 value: 56.98 - type: mrr_at_3 value: 53.835 - type: mrr_at_5 value: 55.252 - type: ndcg_at_1 value: 46.27 - type: ndcg_at_10 value: 58.964000000000006 - type: ndcg_at_100 value: 62.875 - type: ndcg_at_1000 value: 63.969 - type: ndcg_at_3 value: 53.297000000000004 - type: ndcg_at_5 value: 55.938 - type: precision_at_1 value: 46.27 - type: precision_at_10 value: 9.549000000000001 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.762 - type: precision_at_5 value: 16.262999999999998 - type: recall_at_1 value: 40.458 - type: recall_at_10 value: 73.446 - type: recall_at_100 value: 90.12400000000001 - type: recall_at_1000 value: 97.795 - type: recall_at_3 value: 58.123000000000005 - type: recall_at_5 value: 64.68 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.443 - type: map_at_10 value: 36.081 - type: map_at_100 value: 37.163000000000004 - type: map_at_1000 value: 37.232 - type: map_at_3 value: 33.308 - type: map_at_5 value: 34.724 - type: mrr_at_1 value: 29.492 - type: mrr_at_10 value: 38.138 - type: mrr_at_100 value: 39.065 - type: mrr_at_1000 value: 39.119 - type: mrr_at_3 value: 35.593 - type: mrr_at_5 value: 36.785000000000004 - type: ndcg_at_1 value: 29.492 - type: ndcg_at_10 value: 41.134 - type: ndcg_at_100 value: 46.300999999999995 - type: ndcg_at_1000 value: 48.106 - type: ndcg_at_3 value: 35.77 - type: ndcg_at_5 value: 38.032 - type: precision_at_1 value: 29.492 - type: precision_at_10 value: 6.249 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.065999999999999 - type: precision_at_5 value: 10.373000000000001 - type: recall_at_1 value: 27.443 - type: recall_at_10 value: 54.80199999999999 - type: recall_at_100 value: 78.21900000000001 - type: recall_at_1000 value: 91.751 - type: recall_at_3 value: 40.211000000000006 - type: recall_at_5 value: 45.599000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.731 - type: map_at_10 value: 26.717999999999996 - type: map_at_100 value: 27.897 - type: map_at_1000 value: 28.029 - type: map_at_3 value: 23.91 - type: map_at_5 value: 25.455 - type: mrr_at_1 value: 23.134 - type: mrr_at_10 value: 31.769 - type: mrr_at_100 value: 32.634 - type: mrr_at_1000 value: 32.707 - type: mrr_at_3 value: 28.938999999999997 - type: mrr_at_5 value: 30.531000000000002 - type: ndcg_at_1 value: 23.134 - type: ndcg_at_10 value: 32.249 - type: ndcg_at_100 value: 37.678 - type: ndcg_at_1000 value: 40.589999999999996 - type: ndcg_at_3 value: 26.985999999999997 - type: ndcg_at_5 value: 29.457 - type: precision_at_1 value: 23.134 - type: precision_at_10 value: 5.8709999999999996 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.852 - type: precision_at_5 value: 9.428 - type: recall_at_1 value: 18.731 - type: recall_at_10 value: 44.419 - type: recall_at_100 value: 67.851 - type: recall_at_1000 value: 88.103 - type: recall_at_3 value: 29.919 - type: recall_at_5 value: 36.230000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.324 - type: map_at_10 value: 41.265 - type: map_at_100 value: 42.559000000000005 - type: map_at_1000 value: 42.669000000000004 - type: map_at_3 value: 38.138 - type: map_at_5 value: 39.881 - type: mrr_at_1 value: 36.67 - type: mrr_at_10 value: 46.774 - type: mrr_at_100 value: 47.554 - type: mrr_at_1000 value: 47.593 - type: mrr_at_3 value: 44.338 - type: mrr_at_5 value: 45.723 - type: ndcg_at_1 value: 36.67 - type: ndcg_at_10 value: 47.367 - type: ndcg_at_100 value: 52.623 - type: ndcg_at_1000 value: 54.59 - type: ndcg_at_3 value: 42.323 - type: ndcg_at_5 value: 44.727 - type: precision_at_1 value: 36.67 - type: precision_at_10 value: 8.518 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 19.955000000000002 - type: precision_at_5 value: 14.11 - type: recall_at_1 value: 30.324 - type: recall_at_10 value: 59.845000000000006 - type: recall_at_100 value: 81.77499999999999 - type: recall_at_1000 value: 94.463 - type: recall_at_3 value: 46.019 - type: recall_at_5 value: 52.163000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.229 - type: map_at_10 value: 35.004000000000005 - type: map_at_100 value: 36.409000000000006 - type: map_at_1000 value: 36.521 - type: map_at_3 value: 31.793 - type: map_at_5 value: 33.432 - type: mrr_at_1 value: 30.365 - type: mrr_at_10 value: 40.502 - type: mrr_at_100 value: 41.372 - type: mrr_at_1000 value: 41.435 - type: mrr_at_3 value: 37.804 - type: mrr_at_5 value: 39.226 - type: ndcg_at_1 value: 30.365 - type: ndcg_at_10 value: 41.305 - type: ndcg_at_100 value: 47.028999999999996 - type: ndcg_at_1000 value: 49.375 - type: ndcg_at_3 value: 35.85 - type: ndcg_at_5 value: 38.12 - type: precision_at_1 value: 30.365 - type: precision_at_10 value: 7.808 - type: precision_at_100 value: 1.228 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 17.352 - type: precision_at_5 value: 12.42 - type: recall_at_1 value: 24.229 - type: recall_at_10 value: 54.673 - type: recall_at_100 value: 78.766 - type: recall_at_1000 value: 94.625 - type: recall_at_3 value: 39.602 - type: recall_at_5 value: 45.558 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.695 - type: map_at_10 value: 36.0895 - type: map_at_100 value: 37.309416666666664 - type: map_at_1000 value: 37.42558333333334 - type: map_at_3 value: 33.19616666666666 - type: map_at_5 value: 34.78641666666667 - type: mrr_at_1 value: 31.486083333333337 - type: mrr_at_10 value: 40.34774999999999 - type: mrr_at_100 value: 41.17533333333333 - type: mrr_at_1000 value: 41.231583333333326 - type: mrr_at_3 value: 37.90075 - type: mrr_at_5 value: 39.266999999999996 - type: ndcg_at_1 value: 31.486083333333337 - type: ndcg_at_10 value: 41.60433333333334 - type: ndcg_at_100 value: 46.74525 - type: ndcg_at_1000 value: 48.96166666666667 - type: ndcg_at_3 value: 36.68825 - type: ndcg_at_5 value: 38.966499999999996 - type: precision_at_1 value: 31.486083333333337 - type: precision_at_10 value: 7.29675 - type: precision_at_100 value: 1.1621666666666666 - type: precision_at_1000 value: 0.1545 - type: precision_at_3 value: 16.8815 - type: precision_at_5 value: 11.974583333333333 - type: recall_at_1 value: 26.695 - type: recall_at_10 value: 53.651916666666665 - type: recall_at_100 value: 76.12083333333332 - type: recall_at_1000 value: 91.31191666666668 - type: recall_at_3 value: 40.03575 - type: recall_at_5 value: 45.876666666666665 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.668000000000003 - type: map_at_10 value: 32.486 - type: map_at_100 value: 33.371 - type: map_at_1000 value: 33.458 - type: map_at_3 value: 30.261 - type: map_at_5 value: 31.418000000000003 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.414 - type: mrr_at_100 value: 36.149 - type: mrr_at_1000 value: 36.215 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 34.43 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 36.732 - type: ndcg_at_100 value: 41.331 - type: ndcg_at_1000 value: 43.575 - type: ndcg_at_3 value: 32.413 - type: ndcg_at_5 value: 34.316 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 13.65 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 25.668000000000003 - type: recall_at_10 value: 47.147 - type: recall_at_100 value: 68.504 - type: recall_at_1000 value: 85.272 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 39.925 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.256 - type: map_at_10 value: 24.58 - type: map_at_100 value: 25.773000000000003 - type: map_at_1000 value: 25.899 - type: map_at_3 value: 22.236 - type: map_at_5 value: 23.507 - type: mrr_at_1 value: 20.957 - type: mrr_at_10 value: 28.416000000000004 - type: mrr_at_100 value: 29.447000000000003 - type: mrr_at_1000 value: 29.524 - type: mrr_at_3 value: 26.245 - type: mrr_at_5 value: 27.451999999999998 - type: ndcg_at_1 value: 20.957 - type: ndcg_at_10 value: 29.285 - type: ndcg_at_100 value: 35.003 - type: ndcg_at_1000 value: 37.881 - type: ndcg_at_3 value: 25.063000000000002 - type: ndcg_at_5 value: 26.983 - type: precision_at_1 value: 20.957 - type: precision_at_10 value: 5.344 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 11.918 - type: precision_at_5 value: 8.596 - type: recall_at_1 value: 17.256 - type: recall_at_10 value: 39.644 - type: recall_at_100 value: 65.279 - type: recall_at_1000 value: 85.693 - type: recall_at_3 value: 27.825 - type: recall_at_5 value: 32.792 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.700000000000003 - type: map_at_10 value: 36.205999999999996 - type: map_at_100 value: 37.316 - type: map_at_1000 value: 37.425000000000004 - type: map_at_3 value: 33.166000000000004 - type: map_at_5 value: 35.032999999999994 - type: mrr_at_1 value: 31.436999999999998 - type: mrr_at_10 value: 40.61 - type: mrr_at_100 value: 41.415 - type: mrr_at_1000 value: 41.48 - type: mrr_at_3 value: 37.966 - type: mrr_at_5 value: 39.599000000000004 - type: ndcg_at_1 value: 31.436999999999998 - type: ndcg_at_10 value: 41.771 - type: ndcg_at_100 value: 46.784 - type: ndcg_at_1000 value: 49.183 - type: ndcg_at_3 value: 36.437000000000005 - type: ndcg_at_5 value: 39.291 - type: precision_at_1 value: 31.436999999999998 - type: precision_at_10 value: 6.987 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.448999999999998 - type: precision_at_5 value: 11.866 - type: recall_at_1 value: 26.700000000000003 - type: recall_at_10 value: 54.301 - type: recall_at_100 value: 75.871 - type: recall_at_1000 value: 92.529 - type: recall_at_3 value: 40.201 - type: recall_at_5 value: 47.208 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.296 - type: map_at_10 value: 33.116 - type: map_at_100 value: 34.81 - type: map_at_1000 value: 35.032000000000004 - type: map_at_3 value: 30.105999999999998 - type: map_at_5 value: 31.839000000000002 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.803 - type: mrr_at_100 value: 38.856 - type: mrr_at_1000 value: 38.903999999999996 - type: mrr_at_3 value: 35.211 - type: mrr_at_5 value: 36.545 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.007 - type: ndcg_at_100 value: 45.321 - type: ndcg_at_1000 value: 47.665 - type: ndcg_at_3 value: 34.1 - type: ndcg_at_5 value: 36.437000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.897 - type: recall_at_1 value: 24.296 - type: recall_at_10 value: 49.85 - type: recall_at_100 value: 78.457 - type: recall_at_1000 value: 92.618 - type: recall_at_3 value: 36.138999999999996 - type: recall_at_5 value: 42.223 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.591 - type: map_at_10 value: 28.902 - type: map_at_100 value: 29.886000000000003 - type: map_at_1000 value: 29.987000000000002 - type: map_at_3 value: 26.740000000000002 - type: map_at_5 value: 27.976 - type: mrr_at_1 value: 22.366 - type: mrr_at_10 value: 30.971 - type: mrr_at_100 value: 31.865 - type: mrr_at_1000 value: 31.930999999999997 - type: mrr_at_3 value: 28.927999999999997 - type: mrr_at_5 value: 30.231 - type: ndcg_at_1 value: 22.366 - type: ndcg_at_10 value: 33.641 - type: ndcg_at_100 value: 38.477 - type: ndcg_at_1000 value: 41.088 - type: ndcg_at_3 value: 29.486 - type: ndcg_at_5 value: 31.612000000000002 - type: precision_at_1 value: 22.366 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 12.939 - type: precision_at_5 value: 9.094 - type: recall_at_1 value: 20.591 - type: recall_at_10 value: 46.052 - type: recall_at_100 value: 68.193 - type: recall_at_1000 value: 87.638 - type: recall_at_3 value: 34.966 - type: recall_at_5 value: 40.082 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.091 - type: map_at_10 value: 26.38 - type: map_at_100 value: 28.421999999999997 - type: map_at_1000 value: 28.621999999999996 - type: map_at_3 value: 21.597 - type: map_at_5 value: 24.12 - type: mrr_at_1 value: 34.266999999999996 - type: mrr_at_10 value: 46.864 - type: mrr_at_100 value: 47.617 - type: mrr_at_1000 value: 47.644 - type: mrr_at_3 value: 43.312 - type: mrr_at_5 value: 45.501000000000005 - type: ndcg_at_1 value: 34.266999999999996 - type: ndcg_at_10 value: 36.095 - type: ndcg_at_100 value: 43.447 - type: ndcg_at_1000 value: 46.661 - type: ndcg_at_3 value: 29.337999999999997 - type: ndcg_at_5 value: 31.824 - type: precision_at_1 value: 34.266999999999996 - type: precision_at_10 value: 11.472 - type: precision_at_100 value: 1.944 - type: precision_at_1000 value: 0.255 - type: precision_at_3 value: 21.933 - type: precision_at_5 value: 17.224999999999998 - type: recall_at_1 value: 15.091 - type: recall_at_10 value: 43.022 - type: recall_at_100 value: 68.075 - type: recall_at_1000 value: 85.76 - type: recall_at_3 value: 26.564 - type: recall_at_5 value: 33.594 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.252 - type: map_at_10 value: 20.923 - type: map_at_100 value: 30.741000000000003 - type: map_at_1000 value: 32.542 - type: map_at_3 value: 14.442 - type: map_at_5 value: 17.399 - type: mrr_at_1 value: 70.25 - type: mrr_at_10 value: 78.17 - type: mrr_at_100 value: 78.444 - type: mrr_at_1000 value: 78.45100000000001 - type: mrr_at_3 value: 76.958 - type: mrr_at_5 value: 77.571 - type: ndcg_at_1 value: 58.375 - type: ndcg_at_10 value: 44.509 - type: ndcg_at_100 value: 49.897999999999996 - type: ndcg_at_1000 value: 57.269999999999996 - type: ndcg_at_3 value: 48.64 - type: ndcg_at_5 value: 46.697 - type: precision_at_1 value: 70.25 - type: precision_at_10 value: 36.05 - type: precision_at_100 value: 11.848 - type: precision_at_1000 value: 2.213 - type: precision_at_3 value: 52.917 - type: precision_at_5 value: 45.7 - type: recall_at_1 value: 9.252 - type: recall_at_10 value: 27.006999999999998 - type: recall_at_100 value: 57.008 - type: recall_at_1000 value: 80.697 - type: recall_at_3 value: 15.798000000000002 - type: recall_at_5 value: 20.4 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 50.88 - type: f1 value: 45.545495028653384 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 75.424 - type: map_at_10 value: 83.435 - type: map_at_100 value: 83.66900000000001 - type: map_at_1000 value: 83.685 - type: map_at_3 value: 82.39800000000001 - type: map_at_5 value: 83.07 - type: mrr_at_1 value: 81.113 - type: mrr_at_10 value: 87.77199999999999 - type: mrr_at_100 value: 87.862 - type: mrr_at_1000 value: 87.86500000000001 - type: mrr_at_3 value: 87.17099999999999 - type: mrr_at_5 value: 87.616 - type: ndcg_at_1 value: 81.113 - type: ndcg_at_10 value: 86.909 - type: ndcg_at_100 value: 87.746 - type: ndcg_at_1000 value: 88.017 - type: ndcg_at_3 value: 85.368 - type: ndcg_at_5 value: 86.28099999999999 - type: precision_at_1 value: 81.113 - type: precision_at_10 value: 10.363 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 32.507999999999996 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 75.424 - type: recall_at_10 value: 93.258 - type: recall_at_100 value: 96.545 - type: recall_at_1000 value: 98.284 - type: recall_at_3 value: 89.083 - type: recall_at_5 value: 91.445 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.532 - type: map_at_10 value: 37.141999999999996 - type: map_at_100 value: 39.162 - type: map_at_1000 value: 39.322 - type: map_at_3 value: 32.885 - type: map_at_5 value: 35.093999999999994 - type: mrr_at_1 value: 44.29 - type: mrr_at_10 value: 53.516 - type: mrr_at_100 value: 54.24 - type: mrr_at_1000 value: 54.273 - type: mrr_at_3 value: 51.286 - type: mrr_at_5 value: 52.413 - type: ndcg_at_1 value: 44.29 - type: ndcg_at_10 value: 45.268 - type: ndcg_at_100 value: 52.125 - type: ndcg_at_1000 value: 54.778000000000006 - type: ndcg_at_3 value: 41.829 - type: ndcg_at_5 value: 42.525 - type: precision_at_1 value: 44.29 - type: precision_at_10 value: 12.5 - type: precision_at_100 value: 1.9720000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 28.035 - type: precision_at_5 value: 20.093 - type: recall_at_1 value: 22.532 - type: recall_at_10 value: 52.419000000000004 - type: recall_at_100 value: 77.43299999999999 - type: recall_at_1000 value: 93.379 - type: recall_at_3 value: 38.629000000000005 - type: recall_at_5 value: 43.858000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 39.359 - type: map_at_10 value: 63.966 - type: map_at_100 value: 64.87 - type: map_at_1000 value: 64.92599999999999 - type: map_at_3 value: 60.409 - type: map_at_5 value: 62.627 - type: mrr_at_1 value: 78.717 - type: mrr_at_10 value: 84.468 - type: mrr_at_100 value: 84.655 - type: mrr_at_1000 value: 84.661 - type: mrr_at_3 value: 83.554 - type: mrr_at_5 value: 84.133 - type: ndcg_at_1 value: 78.717 - type: ndcg_at_10 value: 72.03399999999999 - type: ndcg_at_100 value: 75.158 - type: ndcg_at_1000 value: 76.197 - type: ndcg_at_3 value: 67.049 - type: ndcg_at_5 value: 69.808 - type: precision_at_1 value: 78.717 - type: precision_at_10 value: 15.201 - type: precision_at_100 value: 1.764 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 43.313 - type: precision_at_5 value: 28.165000000000003 - type: recall_at_1 value: 39.359 - type: recall_at_10 value: 76.003 - type: recall_at_100 value: 88.197 - type: recall_at_1000 value: 95.003 - type: recall_at_3 value: 64.97 - type: recall_at_5 value: 70.41199999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.83200000000001 - type: ap value: 89.33560571859861 - type: f1 value: 92.82322915005167 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.983 - type: map_at_10 value: 34.259 - type: map_at_100 value: 35.432 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 30.275999999999996 - type: map_at_5 value: 32.566 - type: mrr_at_1 value: 22.579 - type: mrr_at_10 value: 34.882999999999996 - type: mrr_at_100 value: 35.984 - type: mrr_at_1000 value: 36.028 - type: mrr_at_3 value: 30.964999999999996 - type: mrr_at_5 value: 33.245000000000005 - type: ndcg_at_1 value: 22.564 - type: ndcg_at_10 value: 41.258 - type: ndcg_at_100 value: 46.824 - type: ndcg_at_1000 value: 48.037 - type: ndcg_at_3 value: 33.17 - type: ndcg_at_5 value: 37.263000000000005 - type: precision_at_1 value: 22.564 - type: precision_at_10 value: 6.572 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.130999999999998 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.983 - type: recall_at_10 value: 62.775000000000006 - type: recall_at_100 value: 88.389 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 40.878 - type: recall_at_5 value: 50.690000000000005 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.95120839033288 - type: f1 value: 93.73824125055208 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.78978568171455 - type: f1 value: 57.50180552858304 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.24411566913248 - type: f1 value: 74.37851403532832 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.94620040349699 - type: f1 value: 80.21293397970435 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.44403096245675 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.659594631336812 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53833075108798 - type: mrr value: 33.78840823218308 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.185999999999999 - type: map_at_10 value: 15.193999999999999 - type: map_at_100 value: 19.538 - type: map_at_1000 value: 21.178 - type: map_at_3 value: 11.208 - type: map_at_5 value: 12.745999999999999 - type: mrr_at_1 value: 48.916 - type: mrr_at_10 value: 58.141 - type: mrr_at_100 value: 58.656 - type: mrr_at_1000 value: 58.684999999999995 - type: mrr_at_3 value: 55.521 - type: mrr_at_5 value: 57.239 - type: ndcg_at_1 value: 47.059 - type: ndcg_at_10 value: 38.644 - type: ndcg_at_100 value: 36.272999999999996 - type: ndcg_at_1000 value: 44.996 - type: ndcg_at_3 value: 43.293 - type: ndcg_at_5 value: 40.819 - type: precision_at_1 value: 48.916 - type: precision_at_10 value: 28.607 - type: precision_at_100 value: 9.195 - type: precision_at_1000 value: 2.225 - type: precision_at_3 value: 40.454 - type: precision_at_5 value: 34.985 - type: recall_at_1 value: 7.185999999999999 - type: recall_at_10 value: 19.654 - type: recall_at_100 value: 37.224000000000004 - type: recall_at_1000 value: 68.663 - type: recall_at_3 value: 12.158 - type: recall_at_5 value: 14.674999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.552000000000003 - type: map_at_10 value: 47.75 - type: map_at_100 value: 48.728 - type: map_at_1000 value: 48.754 - type: map_at_3 value: 43.156 - type: map_at_5 value: 45.883 - type: mrr_at_1 value: 35.66 - type: mrr_at_10 value: 50.269 - type: mrr_at_100 value: 50.974 - type: mrr_at_1000 value: 50.991 - type: mrr_at_3 value: 46.519 - type: mrr_at_5 value: 48.764 - type: ndcg_at_1 value: 35.632000000000005 - type: ndcg_at_10 value: 55.786 - type: ndcg_at_100 value: 59.748999999999995 - type: ndcg_at_1000 value: 60.339 - type: ndcg_at_3 value: 47.292 - type: ndcg_at_5 value: 51.766999999999996 - type: precision_at_1 value: 35.632000000000005 - type: precision_at_10 value: 9.267 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.601 - type: precision_at_5 value: 15.539 - type: recall_at_1 value: 31.552000000000003 - type: recall_at_10 value: 77.62400000000001 - type: recall_at_100 value: 94.527 - type: recall_at_1000 value: 98.919 - type: recall_at_3 value: 55.898 - type: recall_at_5 value: 66.121 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.414 - type: map_at_10 value: 85.37400000000001 - type: map_at_100 value: 86.01100000000001 - type: map_at_1000 value: 86.027 - type: map_at_3 value: 82.562 - type: map_at_5 value: 84.284 - type: mrr_at_1 value: 82.24000000000001 - type: mrr_at_10 value: 88.225 - type: mrr_at_100 value: 88.324 - type: mrr_at_1000 value: 88.325 - type: mrr_at_3 value: 87.348 - type: mrr_at_5 value: 87.938 - type: ndcg_at_1 value: 82.24000000000001 - type: ndcg_at_10 value: 88.97699999999999 - type: ndcg_at_100 value: 90.16 - type: ndcg_at_1000 value: 90.236 - type: ndcg_at_3 value: 86.371 - type: ndcg_at_5 value: 87.746 - type: precision_at_1 value: 82.24000000000001 - type: precision_at_10 value: 13.481000000000002 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.86 - type: precision_at_5 value: 24.738 - type: recall_at_1 value: 71.414 - type: recall_at_10 value: 95.735 - type: recall_at_100 value: 99.696 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.105 - type: recall_at_5 value: 92.17999999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.22146692057259 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.29273320614578 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.023 - type: map_at_10 value: 14.161000000000001 - type: map_at_100 value: 16.68 - type: map_at_1000 value: 17.072000000000003 - type: map_at_3 value: 9.763 - type: map_at_5 value: 11.977 - type: mrr_at_1 value: 24.8 - type: mrr_at_10 value: 37.602999999999994 - type: mrr_at_100 value: 38.618 - type: mrr_at_1000 value: 38.659 - type: mrr_at_3 value: 34.117 - type: 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default split: test revision: None metrics: - type: map_at_1 value: 2.752 - type: map_at_10 value: 9.951 - type: map_at_100 value: 16.794999999999998 - type: map_at_1000 value: 18.251 - type: map_at_3 value: 5.288 - type: map_at_5 value: 6.954000000000001 - type: mrr_at_1 value: 38.775999999999996 - type: mrr_at_10 value: 50.458000000000006 - type: mrr_at_100 value: 51.324999999999996 - type: mrr_at_1000 value: 51.339999999999996 - type: mrr_at_3 value: 46.939 - type: mrr_at_5 value: 47.857 - type: ndcg_at_1 value: 36.735 - type: ndcg_at_10 value: 25.198999999999998 - type: ndcg_at_100 value: 37.938 - type: ndcg_at_1000 value: 49.145 - type: ndcg_at_3 value: 29.348000000000003 - type: ndcg_at_5 value: 25.804 - type: precision_at_1 value: 38.775999999999996 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 7.939 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.752 - type: recall_at_10 value: 16.197 - type: recall_at_100 value: 49.166 - type: recall_at_1000 value: 84.18900000000001 - type: recall_at_3 value: 6.438000000000001 - type: recall_at_5 value: 9.093 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.47980000000001 - type: ap value: 14.605194452178754 - type: f1 value: 55.07362924988948 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.708545557441994 - type: f1 value: 60.04751270975683 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 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87.5663110210407 - type: manhattan_ap value: 78.49982050876562 - type: manhattan_f1 value: 71.35488740722104 - type: manhattan_precision value: 68.18946862226497 - type: manhattan_recall value: 74.82849604221636 - type: max_accuracy value: 87.58419264469214 - type: max_ap value: 78.55300477331477 - type: max_f1 value: 71.49673530889001 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09069740365584 - type: cos_sim_ap value: 86.22749303724757 - type: cos_sim_f1 value: 78.36863452005407 - type: cos_sim_precision value: 76.49560117302053 - type: cos_sim_recall value: 80.33569448721897 - type: dot_accuracy value: 89.09069740365584 - type: dot_ap value: 86.22750233655673 - type: dot_f1 value: 78.36863452005407 - type: dot_precision value: 76.49560117302053 - type: dot_recall value: 80.33569448721897 - 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347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b> </p> # mxbai-embed-large-v1 Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl). ## Quickstart Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. ### sentence-transformers ``` python -m pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim from sentence_transformers.quantization import quantize_embeddings # 1. Specify preffered dimensions dimensions = 512 # 2. load model model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions) # For retrieval you need to pass this prompt. query = 'Represent this sentence for searching relevant passages: A man is eating a piece of bread' docs = [ query, "A man is eating food.", "A man is eating pasta.", "The girl is carrying a baby.", "A man is riding a horse.", ] # 2. Encode embeddings = model.encode(docs) # Optional: Quantize the embeddings binary_embeddings = quantize_embeddings(embeddings, precision="ubinary") similarities = cos_sim(embeddings[0], embeddings[1:]) print('similarities:', similarities) ``` ### Transformers ```python from typing import Dict import torch import numpy as np from transformers import AutoModel, AutoTokenizer from sentence_transformers.util import cos_sim # For retrieval you need to pass this prompt. Please find our more in our blog post. def transform_query(query: str) -> str: """ For retrieval, add the prompt for query (not for documents). """ return f'Represent this sentence for searching relevant passages: {query}' # The model works really well with cls pooling (default) but also with mean pooling. def pooling(outputs: torch.Tensor, inputs: Dict, strategy: str = 'cls') -> np.ndarray: if strategy == 'cls': outputs = outputs[:, 0] elif strategy == 'mean': outputs = torch.sum( outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"]) else: raise NotImplementedError return outputs.detach().cpu().numpy() # 1. load model model_id = 'mixedbread-ai/mxbai-embed-large-v1' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModel.from_pretrained(model_id).cuda() docs = [ transform_query('A man is eating a piece of bread'), "A man is eating food.", "A man is eating pasta.", "The girl is carrying a baby.", "A man is riding a horse.", ] # 2. encode inputs = tokenizer(docs, padding=True, return_tensors='pt') for k, v in inputs.items(): inputs[k] = v.cuda() outputs = model(**inputs).last_hidden_state embeddings = pooling(outputs, inputs, 'cls') similarities = cos_sim(embeddings[0], embeddings[1:]) print('similarities:', similarities) ``` ### Transformers.js If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: ```bash npm i @xenova/transformers ``` You can then use the model to compute embeddings like this: ```js import { pipeline, cos_sim } from '@xenova/transformers'; // Create a feature extraction pipeline const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', { quantized: false, // Comment out this line to use the quantized version }); // Generate sentence embeddings const docs = [ 'Represent this sentence for searching relevant passages: A man is eating a piece of bread', 'A man is eating food.', 'A man is eating pasta.', 'The girl is carrying a baby.', 'A man is riding a horse.', ] const output = await extractor(docs, { pooling: 'cls' }); // Compute similarity scores const [source_embeddings, ...document_embeddings ] = output.tolist(); const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x)); console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027] ``` ### Using API You can use the model via our API as follows: ```python from mixedbread_ai.client import MixedbreadAI, EncodingFormat from sklearn.metrics.pairwise import cosine_similarity import os mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") english_sentences = [ 'What is the capital of Australia?', 'Canberra is the capital of Australia.' ] res = mxbai.embeddings( input=english_sentences, model="mixedbread-ai/mxbai-embed-large-v1", normalized=True, encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8], dimensions=512 ) encoded_embeddings = res.data[0].embedding print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8) ``` The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information. ## Evaluation As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2. | Model | Avg (56 datasets) | Classification (12 datasets) | Clustering (11 datasets) | PairClassification (3 datasets) | Reranking (4 datasets) | Retrieval (15 datasets) | STS (10 datasets) | Summarization (1 dataset) | | --------------------------------------------------------------------------------------------- | ----------------- | ---------------------------- | ------------------------ | ------------------------------- | ---------------------- | ----------------------- | ----------------- | ------------------------- | | **mxbai-embed-large-v1** | **64.68** | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85.00 | 32.71 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [mxbai-embed-2d-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-2d-large-v1) | 63.25 | 74.14 | 46.07 | 85.89 | 58.94 | 51.42 | 84.9 | 31.55 | | [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) | 62.39 | 74.12 | 43.91 | 85.15 | 55.69 | 52.81 | 82.06 | 30.08 | | [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) | 60.38 | 73.45 | 41.73 | 85.38 | 56.98 | 47.87 | 80.7 | 31.6 | | *Proprietary Models* | | | | | | | | | | [OpenAI text-embedding-3-large](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58 | 75.45 | 49.01 | 85.72 | 59.16 | 55.44 | 81.73 | 29.92 | | [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/) | 64.47 | 76.49 | 47.43 | 85.84 | 58.01 | 55.00 | 82.62 | 30.18 | | [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) | 60.99 | 70.93 | 45.90 | 84.89 | 56.32 | 49.25 | 80.97 | 30.80 | Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1). ## Matryoshka and Binary Quantization Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). <b> The model supports both approaches! </b> You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our [blog post](https://www.mixedbread.ai/blog/binary-mrl). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0 ## Citation ```bibtex @online{emb2024mxbai, title={Open Source Strikes Bread - New Fluffy Embeddings Model}, author={Sean Lee, Aamir Shakir, Darius Koenig, Julius Lipp}, year={2024}, url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1}, } @article{li2023angle, title={AnglE-optimized Text Embeddings}, author={Li, Xianming and Li, Jing}, journal={arXiv preprint arXiv:2309.12871}, year={2023} } ```

microsoft/Phi-3-mini-4k-instruct

microsoft

transformers

text-generation

--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- ## Model Summary The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. Resources and Technical Documentation: 🏡 [Phi-3 Portal](https://azure.microsoft.com/en-us/products/phi-3) <br> 📰 [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) <br> 📖 [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) <br> 🛠️ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) <br> 👩‍🍳 [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) <br> 🖥️ [Try It](https://aka.ms/try-phi3) | | Short Context | Long Context | | :------- | :------------- | :------------ | | Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)| | Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)| | Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)| | Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)| ## Intended Uses **Primary use cases** The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications which require 1) memory/compute constrained environments; 2) latency bound scenarios; 3) strong reasoning (especially math and logic). Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. **Out-of-scope use cases** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. **Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.** ## Release Notes This is an update over the original instruction-tuned Phi-3-mini release based on valuable customer feedback. The model used additional post-training data leading to substantial gains on instruction following and structure output. We also improve multi-turn conversation quality, explicitly support <|system|> tag, and significantly improve reasoning capability. We believe most use cases will benefit from this release, but we encourage users to test in their particular AI applications. We appreciate the enthusiastic adoption of the Phi-3 model family, and continue to welcome all feedback from the community. The table below highlights improvements on instruction following, structure output, and reasoning of the new release on publich and internal benchmark datasets. | Benchmarks | Original | June 2024 Update | |:------------|:----------|:------------------| | Instruction Extra Hard | 5.7 | 6.0 | | Instruction Hard | 4.9 | 5.1 | | Instructions Challenge | 24.6 | 42.3 | | JSON Structure Output | 11.5 | 52.3 | | XML Structure Output | 14.4 | 49.8 | | GPQA | 23.7 | 30.6 | | MMLU | 68.8 | 70.9 | | **Average** | **21.9** | **36.7** | Notes: if users would like to check out the previous version, use the git commit id **ff07dc01615f8113924aed013115ab2abd32115b**. For the model conversion, e.g. GGUF and other formats, we invite the community to experiment with various approaches and share your valuable feedback. Let's innovate together! ## How to Use Phi-3 Mini-4K-Instruct has been integrated in the `4.41.2` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`. Examples of required packages: ``` flash_attn==2.5.8 torch==2.3.1 accelerate==0.31.0 transformers==4.41.2 ``` Phi-3 Mini-4K-Instruct is also available in [Azure AI Studio](https://aka.ms/try-phi3) ### Tokenizer Phi-3 Mini-4K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <|system|> You are a helpful assistant.<|end|> <|user|> Question?<|end|> <|assistant|> ``` For example: ```markdown <|system|> You are a helpful assistant.<|end|> <|user|> How to explain Internet for a medieval knight?<|end|> <|assistant|> ``` where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <|system|> You are a helpful travel assistant.<|end|> <|user|> I am going to Paris, what should I see?<|end|> <|assistant|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|> <|user|> What is so great about #1?<|end|> <|assistant|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-4k-instruct", device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_ ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 10 days * Training data: 4.9T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between May and June 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. * Release dates: June, 2024. ### Datasets Our training data includes a wide variety of sources, totaling 4.9 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. As an example, the result of a game in premier league in a particular day might be good training data for frontier models, but we need to remove such information to leave more model capacity for reasoning for the small size models. More details about data can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report). ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/sample_finetune.py). ## Benchmarks We report the results under completion format for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT3.5-Turbo-1106. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. | Category | Benchmark | Phi-3-Mini-4K-Ins | Gemma-7B | Mistral-7b | Mixtral-8x7b | Llama-3-8B-Ins | GPT3.5-Turbo-1106 | |:----------|:-----------|:-------------------|:----------|:------------|:--------------|:----------------|:-------------------| | Popular aggregated benchmark | AGI Eval <br>5-shot| 39.0 | 42.1 | 35.1 | 45.2 | 42 | 48.4 | | | MMLU <br>5-shot | 70.9 | 63.6 | 61.7 | 70.5 | 66.5 | 71.4 | | | BigBench Hard CoT<br>3-shot| 73.5 | 59.6 | 57.3 | 69.7 | 51.5 | 68.3 | | Language Understanding | ANLI <br>7-shot | 53.6 | 48.7 | 47.1 | 55.2 | 57.3 | 58.1 | | | HellaSwag <br>5-shot| 75.3 | 49.8 | 58.5 | 70.4 | 71.1 | 78.8 | | Reasoning | ARC Challenge <br>10-shot | 86.3 | 78.3 | 78.6 | 87.3 | 82.8 | 87.4 | | | BoolQ <br>0-shot | 78.1 | 66 | 72.2 | 76.6 | 80.9 | 79.1 | | | MedQA <br>2-shot| 56.5 | 49.6 | 50 | 62.2 | 60.5 | 63.4 | | | OpenBookQA <br>10-shot| 82.2 | 78.6 | 79.8 | 85.8 | 82.6 | 86 | | | PIQA <br>5-shot| 83.5 | 78.1 | 77.7 | 86 | 75.7 | 86.6 | | | GPQA <br>0-shot| 30.6 | 2.9 | 15 | 6.9 | 32.4 | 30.8 | | | Social IQA <br>5-shot| 77.6 | 65.5 | 74.6 | 75.9 | 73.9 | 68.3 | | | TruthfulQA (MC2) <br>10-shot| 64.7 | 52.1 | 53 | 60.1 | 63.2 | 67.7 | | | WinoGrande <br>5-shot| 71.6 | 55.6 | 54.2 | 62 | 65 | 68.8 | | Factual Knowledge | TriviaQA <br>5-shot| 61.4 | 72.3 | 75.2 | 82.2 | 67.7 | 85.8 | | Math | GSM8K CoT <br>8-shot| 85.7 | 59.8 | 46.4 | 64.7 | 77.4 | 78.1 | | Code Generation | HumanEval <br>0-shot| 57.3 | 34.1 | 28.0 | 37.8 | 60.4 | 62.2 | | | MBPP <br>3-shot| 69.8 | 51.5 | 50.8 | 60.2 | 67.7 | 77.8 | | **Average** | | **67.6** | **56.0** | **56.4** | **64.4** | **65.5** | **70.4** | We take a closer look at different categories across 100 public benchmark datasets at the table below: | Category | Phi-3-Mini-4K-Instruct | Gemma-7B | Mistral-7B | Mixtral 8x7B | Llama-3-8B-Instruct | GPT-3.5-Turbo | |:----------|:------------------------|:----------|:------------|:--------------|:---------------------|:---------------| | Popular aggregated benchmark | 61.1 | 59.4 | 56.5 | 66.2 | 59.9 | 67.0 | | Reasoning | 70.8 | 60.3 | 62.8 | 68.1 | 69.6 | 71.8 | | Language understanding | 60.5 | 57.6 | 52.5 | 66.1 | 63.2 | 67.7 | | Code generation | 60.7 | 45.6 | 42.9 | 52.7 | 56.4 | 70.4 | | Math | 50.6 | 35.8 | 25.4 | 40.3 | 41.1 | 52.8 | | Factual knowledge | 38.4 | 46.7 | 49.8 | 58.6 | 43.1 | 63.4 | | Multilingual | 56.7 | 66.5 | 57.4 | 66.7 | 66.6 | 71.0 | | Robustness | 61.1 | 38.4 | 40.6 | 51.0 | 64.5 | 69.3 | Overall, the model with only 3.8B-param achieves a similar level of language understanding and reasoning ability as much larger models. However, it is still fundamentally limited by its size for certain tasks. The model simply does not have the capacity to store too much world knowledge, which can be seen for example with low performance on TriviaQA. However, we believe such weakness can be resolved by augmenting Phi-3-Mini with a search engine. ## Cross Platform Support [ONNX runtime](https://onnxruntime.ai/blogs/accelerating-phi-3) now supports Phi-3 mini models across platforms and hardware. Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA). Along with DML, ONNX Runtime provides cross platform support for Phi3 mini across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via R ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3 Mini-4K-Instruct model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * CPU: use the **GGUF** quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-4k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

tohoku-nlp/bert-base-japanese

tohoku-nlp

transformers

fill-mask

--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia widget: - text: 東北大学で[MASK]の研究をしています。 --- # BERT base Japanese (IPA dictionary) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.

microsoft/deberta-large-mnli

microsoft

transformers

text-classification

--- language: en tags: - deberta-v1 - deberta-mnli tasks: mnli thumbnail: https://huggingface.co/front/thumbnails/microsoft.png license: mit widget: - text: "[CLS] I love you. [SEP] I like you. [SEP]" --- ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. This is the DeBERTa large model fine-tuned with MNLI task. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B | |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------| | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S | | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- | | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- | | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- | | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)¹ | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 | | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)¹ | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7| | [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)¹|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9| |**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)^1,2**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** | -------- #### Notes. - ¹ Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks. - ² To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp** ```bash cd transformers/examples/text-classification/ export TASK_NAME=mrpc python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\ --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\ --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16 ``` ### Citation If you find DeBERTa useful for your work, please cite the following paper: ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```

mistralai/Mixtral-8x7B-v0.1

mistralai

transformers

text-generation

--- license: apache-2.0 language: - fr - it - de - es - en tags: - moe --- # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Notice Mixtral-8x7B is a pretrained base model and therefore does not have any moderation mechanisms. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

Salesforce/blip-image-captioning-large

Salesforce

transformers

image-to-text

--- pipeline_tag: image-to-text tags: - image-captioning languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). |  | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

emilyalsentzer/Bio_ClinicalBERT

emilyalsentzer

transformers

fill-mask

--- language: "en" tags: - fill-mask license: mit --- # ClinicalBERT - Bio + Clinical BERT Model The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes. ## Pretraining Data The `Bio_ClinicalBERT` model was trained on all notes from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words). ## Model Pretraining ### Note Preprocessing Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer). ### Pretraining Procedures The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`). ### Pretraining Hyperparameters We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20). ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") ``` ## More Information Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks. ## Questions? Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email [email protected] with any questions.

nlptown/bert-base-multilingual-uncased-sentiment

nlptown

transformers

text-classification

--- language: - en - nl - de - fr - it - es license: mit --- # bert-base-multilingual-uncased-sentiment This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks. ## Training data Here is the number of product reviews we used for finetuning the model: | Language | Number of reviews | | -------- | ----------------- | | English | 150k | | Dutch | 80k | | German | 137k | | French | 140k | | Italian | 72k | | Spanish | 50k | ## Accuracy The fine-tuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages: - Accuracy (exact) is the exact match for the number of stars. - Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer. | Language | Accuracy (exact) | Accuracy (off-by-1) | | -------- | ---------------------- | ------------------- | | English | 67% | 95% | Dutch | 57% | 93% | German | 61% | 94% | French | 59% | 94% | Italian | 59% | 95% | Spanish | 58% | 95% ## Contact If you found this model useful, you can buy me a coffee at https://www.buymeacoffee.com/yvespeirsman. In addition to this model, [NLP Town](http://nlp.town) offers custom models for many languages and NLP tasks. Feel free to contact me for questions, feedback and/or requests for similar models.

tsmatz/xlm-roberta-ner-japanese

tsmatz

transformers

token-classification

--- language: - ja license: mit tags: - generated_from_trainer - ner - bert metrics: - f1 widget: - text: 鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った - text: 中国では、中国共産党による一党統治が続く base_model: xlm-roberta-base model-index: - name: xlm-roberta-ner-ja results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-ner-japanese (Japanese caption : 日本語の固有表現抽出のモデル) This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) (pre-trained cross-lingual ```RobertaModel```) trained for named entity recognition (NER) token classification. The model is fine-tuned on NER dataset provided by Stockmark Inc, in which data is collected from Japanese Wikipedia articles.<br> See [here](https://github.com/stockmarkteam/ner-wikipedia-dataset) for the license of this dataset. Each token is labeled by : | Label id | Tag | Tag in Widget | Description | |---|---|---|---| | 0 | O | (None) | others or nothing | | 1 | PER | PER | person | | 2 | ORG | ORG | general corporation organization | | 3 | ORG-P | P | political organization | | 4 | ORG-O | O | other organization | | 5 | LOC | LOC | location | | 6 | INS | INS | institution, facility | | 7 | PRD | PRD | product | | 8 | EVT | EVT | event | ## Intended uses ```python from transformers import pipeline model_name = "tsmatz/xlm-roberta-ner-japanese" classifier = pipeline("token-classification", model=model_name) result = classifier("鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った") print(result) ``` ## Training procedure You can download the source code for fine-tuning from [here](https://github.com/tsmatz/huggingface-finetune-japanese/blob/master/01-named-entity.ipynb). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 446 | 0.1510 | 0.8457 | | No log | 2.0 | 892 | 0.0626 | 0.9261 | | No log | 3.0 | 1338 | 0.0366 | 0.9580 | | No log | 4.0 | 1784 | 0.0196 | 0.9792 | | No log | 5.0 | 2230 | 0.0173 | 0.9864 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

distilbert/distilbert-base-multilingual-cased

distilbert

transformers

fill-mask

--- language: - multilingual - af - sq - ar - an - hy - ast - az - ba - eu - bar - be - bn - inc - bs - br - bg - my - ca - ceb - ce - zh - cv - hr - cs - da - nl - en - et - fi - fr - gl - ka - de - el - gu - ht - he - hi - hu - is - io - id - ga - it - ja - jv - kn - kk - ky - ko - la - lv - lt - roa - nds - lm - mk - mg - ms - ml - mr - mn - min - ne - new - nb - nn - oc - fa - pms - pl - pt - pa - ro - ru - sco - sr - hr - scn - sk - sl - aze - es - su - sw - sv - tl - tg - th - ta - tt - te - tr - uk - ud - uz - vi - vo - war - cy - fry - pnb - yo license: apache-2.0 datasets: - wikipedia --- # Model Card for DistilBERT base multilingual (cased) # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description This model is a distilled version of the [BERT base multilingual model](https://huggingface.co/bert-base-multilingual-cased/). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average, this model, referred to as DistilmBERT, is twice as fast as mBERT-base. We encourage potential users of this model to check out the [BERT base multilingual model card](https://huggingface.co/bert-base-multilingual-cased) to learn more about usage, limitations and potential biases. - **Developed by:** Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf (Hugging Face) - **Model type:** Transformer-based language model - **Language(s) (NLP):** 104 languages; see full list [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) - **License:** Apache 2.0 - **Related Models:** [BERT base multilingual model](https://huggingface.co/bert-base-multilingual-cased) - **Resources for more information:** - [GitHub Repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md) - [Associated Paper](https://arxiv.org/abs/1910.01108) # Uses ## Direct Use and Downstream Use You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## Out of Scope Use The model should not be used to intentionally create hostile or alienating environments for people. The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training Details - The model was pretrained with the supervision of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the concatenation of Wikipedia in 104 different languages - The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters. - Further information about the training procedure and data is included in the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model card. # Evaluation The model developers report the following accuracy results for DistilmBERT (see [GitHub Repo](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md)): > Here are the results on the test sets for 6 of the languages available in XNLI. The results are computed in the zero shot setting (trained on the English portion and evaluated on the target language portion): | Model | English | Spanish | Chinese | German | Arabic | Urdu | | :---: | :---: | :---: | :---: | :---: | :---: | :---:| | mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 | | mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 | | DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 | # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` APA - Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. # How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-multilingual-cased') >>> unmasker("Hello I'm a [MASK] model.") [{'score': 0.040800247341394424, 'sequence': "Hello I'm a virtual model.", 'token': 37859, 'token_str': 'virtual'}, {'score': 0.020015988498926163, 'sequence': "Hello I'm a big model.", 'token': 22185, 'token_str': 'big'}, {'score': 0.018680453300476074, 'sequence': "Hello I'm a Hello model.", 'token': 31178, 'token_str': 'Hello'}, {'score': 0.017396586015820503, 'sequence': "Hello I'm a model model.", 'token': 13192, 'token_str': 'model'}, {'score': 0.014229810796678066, 'sequence': "Hello I'm a perfect model.", 'token': 43477, 'token_str': 'perfect'}] ```

google/siglip-so400m-patch14-384

google

transformers

zero-shot-image-classification

--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog --- # SigLIP (shape-optimized model) SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision). This model has the SoViT-400m architecture, which is the shape-optimized version as presented in [Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design](https://arxiv.org/abs/2305.13035) by Alabdulmohsin et al. Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes. A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713). ## Intended uses & limitations You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for other versions on a task that interests you. ### How to use Here is how to use this model to perform zero-shot image classification: ```python from PIL import Image import requests from transformers import AutoProcessor, AutoModel import torch model = AutoModel.from_pretrained("google/siglip-so400m-patch14-384") processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-384") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = ["a photo of 2 cats", "a photo of 2 dogs"] inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image probs = torch.sigmoid(logits_per_image) # these are the probabilities print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") ``` Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user: ```python from transformers import pipeline from PIL import Image import requests # load pipe image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-so400m-patch14-384") # load image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) # inference outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"]) outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs] print(outputs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#). ## Training procedure ### Training data SigLIP is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794). ### Preprocessing Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). Texts are tokenized and padded to the same length (64 tokens). ### Compute The model was trained on 16 TPU-v4 chips for three days. ## Evaluation results Evaluation of SigLIP compared to CLIP is shown below (taken from the paper). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg" alt="drawing" width="600"/> ### BibTeX entry and citation info ```bibtex @misc{zhai2023sigmoid, title={Sigmoid Loss for Language Image Pre-Training}, author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer}, year={2023}, eprint={2303.15343}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

facebook/nllb-200-distilled-600M

facebook

transformers

translation

--- language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn" pipeline_tag: translation tags: - nllb license: "cc-by-nc-4.0" datasets: - flores-200 metrics: - bleu - spbleu - chrf++ inference: false --- # NLLB-200 This is the model card of NLLB-200's distilled 600M variant. Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - License: CC-BY-NC - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues ## Intended Use - Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data. - Primary intended users: Primary users are researchers and machine translation research community. - Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations. ## Metrics • Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations. ## Evaluation Data - Datasets: Flores-200 dataset is described in Section 4 - Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200 - Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The SentencePiece model is released along with NLLB-200. ## Training Data • We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2. ## Ethical Considerations • In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety). ## Caveats and Recommendations • Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments. ## Carbon Footprint Details • The carbon dioxide (CO2e) estimate is reported in Section 8.8.

indobenchmark/indobert-base-p1

indobenchmark

transformers

feature-extraction

--- language: id tags: - indobert - indobenchmark - indonlu license: mit inference: false datasets: - Indo4B --- # IndoBERT Base Model (phase1 - uncased) [IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. ## All Pre-trained Models | Model | #params | Arch. | Training data | |--------------------------------|--------------------------------|-------|-----------------------------------| | `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) | ## How to use ### Load model and tokenizer ```python from transformers import BertTokenizer, AutoModel tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-base-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-base-p1") ``` ### Extract contextual representation ```python x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1) print(x, model(x)[0].sum()) ``` ## Authors <b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti. ## Citation If you use our work, please cite: ```bibtex @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, year={2020} } ```

M-CLIP/XLM-Roberta-Large-Vit-B-32

M-CLIP

transformers

--- language: - multilingual - af - sq - am - ar - az - bn - bs - bg - ca - zh - hr - cs - da - nl - en - et - fr - de - el - hi - hu - is - id - it - ja - mk - ml - mr - pl - pt - ro - ru - sr - sl - es - sw - sv - tl - te - tr - tk - uk - ur - ug - uz - vi - xh --- ## Multilingual-clip: XLM-Roberta-Large-Vit-B-32 Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-B-32` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.com/openai/CLIP). We provide a usage example below. ## Requirements To use both the multilingual text encoder and corresponding image encoder, we need to install the packages [`multilingual-clip`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`clip`](https://github.com/openai/CLIP). ``` pip install multilingual-clip pip install git+https://github.com/openai/CLIP.git ``` ## Usage Extracting embeddings from the text encoder can be done in the following way: ```python from multilingual_clip import pt_multilingual_clip import transformers texts = [ 'Three blind horses listening to Mozart.', 'Älgen är skogens konung!', 'Wie leben Eisbären in der Antarktis?', 'Вы знали, что все белые медведи левши?' ] model_name = 'M-CLIP/XLM-Roberta-Large-Vit-B-32' # Load Model & Tokenizer model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name) tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) embeddings = model.forward(texts, tokenizer) print("Text features shape:", embeddings.shape) ``` Extracting embeddings from the corresponding image encoder: ```python import torch import clip import requests from PIL import Image device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) image = preprocess(image).unsqueeze(0).to(device) with torch.no_grad(): image_features = model.encode_image(image) print("Image features shape:", image_features.shape) ``` ## Evaluation results None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following **R@10** results: | Name | En | De | Es | Fr | Zh | It | Pl | Ko | Ru | Tr | Jp | | ----------------------------------|:-----: |:-----: |:-----: |:-----: | :-----: |:-----: |:-----: |:-----: |:-----: |:-----: |:-----: | | [OpenAI CLIP Vit-B/32](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - | | [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - | | [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - | | [LABSE Vit-L/14](https://huggingface.co/M-CLIP/LABSE-Vit-L-14)| 91.6 | 89.6 | 89.5 | 89.9 | 88.9 | 90.1 | 89.8 | 80.8 | 85.5 | 89.8 | 73.9 | | [XLM-R Large Vit-B/32](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-32)| 91.8 | 88.7 | 89.1 | 89.4 | 89.3 | 89.8| 91.4 | 82.1 | 86.1 | 88.8 | 81.0 | | [XLM-R Vit-L/14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14)| 92.4 | 90.6 | 91.0 | 90.0 | 89.7 | 91.1 | 91.3 | 85.2 | 85.8 | 90.3 | 81.9 | | [XLM-R Large Vit-B/16+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **95.0** | **93.0** | **93.6** | **93.1** | **94.0** | **93.1** | **94.4** | **89.0** | **90.0** | **93.0** | **84.2** | ## Training/Model details Further details about the model training and data can be found in the [model card](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md).

thenlper/gte-base

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-base results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.17910447761193 - type: ap value: 36.827146398068926 - type: f1 value: 68.11292888046363 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.77345000000001 - type: ap value: 88.33530426691347 - type: f1 value: 91.76549906404642 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.964 - type: f1 value: 48.22995586184998 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.147999999999996 - type: map_at_10 value: 48.253 - type: map_at_100 value: 49.038 - type: map_at_1000 value: 49.042 - type: map_at_3 value: 43.433 - type: map_at_5 value: 46.182 - type: mrr_at_1 value: 32.717 - type: mrr_at_10 value: 48.467 - type: mrr_at_100 value: 49.252 - type: mrr_at_1000 value: 49.254999999999995 - type: mrr_at_3 value: 43.599 - type: mrr_at_5 value: 46.408 - type: ndcg_at_1 value: 32.147999999999996 - type: ndcg_at_10 value: 57.12199999999999 - type: ndcg_at_100 value: 60.316 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 47.178 - type: ndcg_at_5 value: 52.146 - type: precision_at_1 value: 32.147999999999996 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.026 - type: recall_at_1 value: 32.147999999999996 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 99.004 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 58.037000000000006 - type: recall_at_5 value: 70.128 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.59706013699614 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.01463593002057 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.80250355752458 - type: mrr value: 74.79455216989844 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.87448576082345 - type: cos_sim_spearman value: 87.64235843637468 - type: euclidean_pearson value: 88.4901825511062 - type: euclidean_spearman value: 87.74537283182033 - type: manhattan_pearson value: 88.39040638362911 - type: manhattan_spearman value: 87.62669542888003 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.06818181818183 - type: f1 value: 85.02524460098233 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.20471092679967 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.58967592147641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.411 - type: map_at_10 value: 45.162 - type: map_at_100 value: 46.717 - type: map_at_1000 value: 46.836 - type: map_at_3 value: 41.428 - type: map_at_5 value: 43.54 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 51.534 - type: mrr_at_100 value: 52.185 - type: mrr_at_1000 value: 52.22 - type: mrr_at_3 value: 49.046 - type: mrr_at_5 value: 50.548 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 52.235 - type: ndcg_at_100 value: 57.4 - type: ndcg_at_1000 value: 58.982 - type: ndcg_at_3 value: 47.332 - type: ndcg_at_5 value: 49.62 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 10.258000000000001 - type: precision_at_100 value: 1.6219999999999999 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.71 - type: recall_at_1 value: 32.411 - type: recall_at_10 value: 65.408 - type: recall_at_100 value: 87.248 - type: recall_at_1000 value: 96.951 - type: recall_at_3 value: 50.349999999999994 - type: recall_at_5 value: 57.431 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.911 - type: map_at_10 value: 42.608000000000004 - type: map_at_100 value: 43.948 - type: map_at_1000 value: 44.089 - type: map_at_3 value: 39.652 - type: map_at_5 value: 41.236 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.916 - type: mrr_at_100 value: 49.539 - type: mrr_at_1000 value: 49.583 - type: mrr_at_3 value: 46.741 - type: mrr_at_5 value: 48.037 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.442 - type: ndcg_at_100 value: 52.798 - type: ndcg_at_1000 value: 54.871 - type: ndcg_at_3 value: 44.528 - type: ndcg_at_5 value: 46.211 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.178 - type: precision_at_100 value: 1.452 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.614 - type: precision_at_5 value: 15.185 - type: recall_at_1 value: 31.911 - type: recall_at_10 value: 58.155 - type: recall_at_100 value: 76.46300000000001 - type: recall_at_1000 value: 89.622 - type: recall_at_3 value: 46.195 - type: recall_at_5 value: 51.288999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.597 - type: map_at_10 value: 54.290000000000006 - type: map_at_100 value: 55.340999999999994 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.931000000000004 - type: map_at_5 value: 52.839999999999996 - type: mrr_at_1 value: 46.646 - type: mrr_at_10 value: 57.524 - type: mrr_at_100 value: 58.225 - type: mrr_at_1000 value: 58.245999999999995 - type: mrr_at_3 value: 55.235 - type: mrr_at_5 value: 56.589 - type: ndcg_at_1 value: 46.646 - type: ndcg_at_10 value: 60.324999999999996 - type: ndcg_at_100 value: 64.30900000000001 - type: ndcg_at_1000 value: 65.19 - type: ndcg_at_3 value: 54.983000000000004 - type: ndcg_at_5 value: 57.621 - type: precision_at_1 value: 46.646 - type: precision_at_10 value: 9.774 - type: precision_at_100 value: 1.265 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 24.911 - type: precision_at_5 value: 16.977999999999998 - type: recall_at_1 value: 40.597 - type: recall_at_10 value: 74.773 - type: recall_at_100 value: 91.61200000000001 - type: recall_at_1000 value: 97.726 - type: recall_at_3 value: 60.458 - type: recall_at_5 value: 66.956 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.122 - type: map_at_10 value: 36.711 - type: map_at_100 value: 37.775 - type: map_at_1000 value: 37.842999999999996 - type: map_at_3 value: 33.693 - type: map_at_5 value: 35.607 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.873999999999995 - type: mrr_at_100 value: 39.739000000000004 - type: mrr_at_1000 value: 39.794000000000004 - type: mrr_at_3 value: 36.102000000000004 - type: mrr_at_5 value: 37.876 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 42.048 - type: ndcg_at_100 value: 47.144999999999996 - type: ndcg_at_1000 value: 48.901 - type: ndcg_at_3 value: 36.402 - type: ndcg_at_5 value: 39.562999999999995 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.4750000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 15.479999999999999 - type: precision_at_5 value: 11.028 - type: recall_at_1 value: 27.122 - type: recall_at_10 value: 56.279999999999994 - type: recall_at_100 value: 79.597 - type: recall_at_1000 value: 92.804 - type: recall_at_3 value: 41.437000000000005 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.757 - type: map_at_10 value: 26.739 - type: map_at_100 value: 28.015 - type: map_at_1000 value: 28.127999999999997 - type: map_at_3 value: 23.986 - type: map_at_5 value: 25.514 - type: mrr_at_1 value: 22.015 - type: mrr_at_10 value: 31.325999999999997 - type: mrr_at_100 value: 32.368 - type: mrr_at_1000 value: 32.426 - type: mrr_at_3 value: 28.897000000000002 - type: mrr_at_5 value: 30.147000000000002 - type: ndcg_at_1 value: 22.015 - type: ndcg_at_10 value: 32.225 - type: ndcg_at_100 value: 38.405 - type: ndcg_at_1000 value: 40.932 - type: ndcg_at_3 value: 27.403 - type: ndcg_at_5 value: 29.587000000000003 - type: precision_at_1 value: 22.015 - type: precision_at_10 value: 5.9830000000000005 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.602 - type: recall_at_1 value: 17.757 - type: recall_at_10 value: 44.467 - type: recall_at_100 value: 71.53699999999999 - type: recall_at_1000 value: 89.281 - type: recall_at_3 value: 31.095 - type: recall_at_5 value: 36.818 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.354 - type: map_at_10 value: 42.134 - type: map_at_100 value: 43.429 - type: map_at_1000 value: 43.532 - type: map_at_3 value: 38.491 - type: map_at_5 value: 40.736 - type: mrr_at_1 value: 37.247 - type: mrr_at_10 value: 47.775 - type: mrr_at_100 value: 48.522999999999996 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.059 - type: mrr_at_5 value: 46.811 - type: ndcg_at_1 value: 37.247 - type: ndcg_at_10 value: 48.609 - type: ndcg_at_100 value: 53.782 - type: ndcg_at_1000 value: 55.666000000000004 - type: ndcg_at_3 value: 42.866 - type: ndcg_at_5 value: 46.001 - type: precision_at_1 value: 37.247 - type: precision_at_10 value: 8.892999999999999 - type: precision_at_100 value: 1.341 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.5 - type: precision_at_5 value: 14.976 - type: recall_at_1 value: 30.354 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 83.65599999999999 - type: recall_at_1000 value: 95.82000000000001 - type: recall_at_3 value: 46.464 - type: recall_at_5 value: 54.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.949 - type: map_at_10 value: 37.230000000000004 - type: map_at_100 value: 38.644 - type: map_at_1000 value: 38.751999999999995 - type: map_at_3 value: 33.816 - type: map_at_5 value: 35.817 - type: mrr_at_1 value: 33.446999999999996 - type: mrr_at_10 value: 42.970000000000006 - type: mrr_at_100 value: 43.873 - type: mrr_at_1000 value: 43.922 - type: mrr_at_3 value: 40.467999999999996 - type: mrr_at_5 value: 41.861 - type: ndcg_at_1 value: 33.446999999999996 - type: ndcg_at_10 value: 43.403000000000006 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 51.361999999999995 - type: ndcg_at_3 value: 38.155 - type: ndcg_at_5 value: 40.643 - type: precision_at_1 value: 33.446999999999996 - type: precision_at_10 value: 8.128 - type: precision_at_100 value: 1.274 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.493000000000002 - type: precision_at_5 value: 13.333 - type: recall_at_1 value: 26.949 - type: recall_at_10 value: 56.006 - type: recall_at_100 value: 80.99199999999999 - type: recall_at_1000 value: 95.074 - type: recall_at_3 value: 40.809 - type: recall_at_5 value: 47.57 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.243583333333333 - type: map_at_10 value: 37.193250000000006 - type: map_at_100 value: 38.44833333333334 - type: map_at_1000 value: 38.56083333333333 - type: map_at_3 value: 34.06633333333333 - type: map_at_5 value: 35.87858333333334 - type: mrr_at_1 value: 32.291583333333335 - type: mrr_at_10 value: 41.482749999999996 - type: mrr_at_100 value: 42.33583333333333 - type: mrr_at_1000 value: 42.38683333333333 - type: mrr_at_3 value: 38.952999999999996 - type: mrr_at_5 value: 40.45333333333333 - type: ndcg_at_1 value: 32.291583333333335 - type: ndcg_at_10 value: 42.90533333333334 - type: ndcg_at_100 value: 48.138666666666666 - type: ndcg_at_1000 value: 50.229083333333335 - type: ndcg_at_3 value: 37.76133333333334 - type: ndcg_at_5 value: 40.31033333333334 - type: precision_at_1 value: 32.291583333333335 - type: precision_at_10 value: 7.585583333333333 - type: precision_at_100 value: 1.2045000000000001 - type: precision_at_1000 value: 0.15733333333333335 - type: precision_at_3 value: 17.485416666666666 - type: precision_at_5 value: 12.5145 - type: recall_at_1 value: 27.243583333333333 - type: recall_at_10 value: 55.45108333333334 - type: recall_at_100 value: 78.25858333333335 - type: recall_at_1000 value: 92.61716666666665 - type: recall_at_3 value: 41.130583333333334 - type: recall_at_5 value: 47.73133333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.325 - type: map_at_10 value: 32.795 - type: map_at_100 value: 33.96 - type: map_at_1000 value: 34.054 - type: map_at_3 value: 30.64 - type: map_at_5 value: 31.771 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 35.83 - type: mrr_at_100 value: 36.868 - type: mrr_at_1000 value: 36.928 - type: mrr_at_3 value: 33.896 - type: mrr_at_5 value: 34.893 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 36.746 - type: ndcg_at_100 value: 42.225 - type: ndcg_at_1000 value: 44.523 - type: ndcg_at_3 value: 32.82 - type: ndcg_at_5 value: 34.583000000000006 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 13.753000000000002 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 26.325 - type: recall_at_10 value: 45.975 - type: recall_at_100 value: 70.393 - type: recall_at_1000 value: 87.217 - type: recall_at_3 value: 35.195 - type: recall_at_5 value: 39.69 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.828 - type: map_at_10 value: 25.759 - type: map_at_100 value: 26.961000000000002 - type: map_at_1000 value: 27.094 - type: map_at_3 value: 23.166999999999998 - type: map_at_5 value: 24.610000000000003 - type: mrr_at_1 value: 21.61 - type: mrr_at_10 value: 29.605999999999998 - type: mrr_at_100 value: 30.586000000000002 - type: mrr_at_1000 value: 30.664 - type: mrr_at_3 value: 27.214 - type: mrr_at_5 value: 28.571 - type: ndcg_at_1 value: 21.61 - type: ndcg_at_10 value: 30.740000000000002 - type: ndcg_at_100 value: 36.332 - type: ndcg_at_1000 value: 39.296 - type: ndcg_at_3 value: 26.11 - type: ndcg_at_5 value: 28.297 - type: precision_at_1 value: 21.61 - type: precision_at_10 value: 5.643 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.4 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 17.828 - type: recall_at_10 value: 41.876000000000005 - type: recall_at_100 value: 66.648 - type: recall_at_1000 value: 87.763 - type: recall_at_3 value: 28.957 - type: recall_at_5 value: 34.494 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.921000000000003 - type: map_at_10 value: 37.156 - type: map_at_100 value: 38.399 - type: map_at_1000 value: 38.498 - type: map_at_3 value: 34.134 - type: map_at_5 value: 35.936 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.19 - type: mrr_at_100 value: 42.102000000000004 - type: mrr_at_1000 value: 42.157 - type: mrr_at_3 value: 38.464 - type: mrr_at_5 value: 40.148 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.679 - type: ndcg_at_100 value: 48.27 - type: ndcg_at_1000 value: 50.312 - type: ndcg_at_3 value: 37.269000000000005 - type: ndcg_at_5 value: 40.055 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.155 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 16.791 - type: precision_at_5 value: 12.015 - type: recall_at_1 value: 27.921000000000003 - type: recall_at_10 value: 55.357 - type: recall_at_100 value: 79.476 - type: recall_at_1000 value: 93.314 - type: recall_at_3 value: 40.891 - type: recall_at_5 value: 47.851 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.524 - type: map_at_10 value: 35.135 - type: map_at_100 value: 36.665 - type: map_at_1000 value: 36.886 - type: map_at_3 value: 31.367 - type: map_at_5 value: 33.724 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.616 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.585 - type: mrr_at_3 value: 36.462 - type: mrr_at_5 value: 38.507999999999996 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 41.61 - type: ndcg_at_100 value: 47.249 - type: ndcg_at_1000 value: 49.662 - type: ndcg_at_3 value: 35.421 - type: ndcg_at_5 value: 38.811 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 8.123 - type: precision_at_100 value: 1.5810000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.568999999999999 - type: recall_at_1 value: 25.524 - type: recall_at_10 value: 54.994 - type: recall_at_100 value: 80.03099999999999 - type: recall_at_1000 value: 95.25099999999999 - type: recall_at_3 value: 37.563 - type: recall_at_5 value: 46.428999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.224 - type: map_at_10 value: 30.599999999999998 - type: map_at_100 value: 31.526 - type: map_at_1000 value: 31.629 - type: map_at_3 value: 27.491 - type: map_at_5 value: 29.212 - type: mrr_at_1 value: 24.214 - type: mrr_at_10 value: 32.632 - type: mrr_at_100 value: 33.482 - type: mrr_at_1000 value: 33.550000000000004 - type: mrr_at_3 value: 29.852 - type: mrr_at_5 value: 31.451 - type: ndcg_at_1 value: 24.214 - type: ndcg_at_10 value: 35.802 - type: ndcg_at_100 value: 40.502 - type: ndcg_at_1000 value: 43.052 - type: ndcg_at_3 value: 29.847 - type: ndcg_at_5 value: 32.732 - type: precision_at_1 value: 24.214 - type: precision_at_10 value: 5.804 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.692999999999998 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.224 - type: recall_at_10 value: 49.849 - type: recall_at_100 value: 71.45 - type: recall_at_1000 value: 90.583 - type: recall_at_3 value: 34.153 - type: recall_at_5 value: 41.004000000000005 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.386999999999999 - type: map_at_10 value: 20.182 - type: map_at_100 value: 21.86 - type: map_at_1000 value: 22.054000000000002 - type: map_at_3 value: 17.165 - type: map_at_5 value: 18.643 - type: mrr_at_1 value: 26.906000000000002 - type: mrr_at_10 value: 37.907999999999994 - type: mrr_at_100 value: 38.868 - type: mrr_at_1000 value: 38.913 - type: mrr_at_3 value: 34.853 - type: mrr_at_5 value: 36.567 - type: ndcg_at_1 value: 26.906000000000002 - type: ndcg_at_10 value: 28.103 - type: ndcg_at_100 value: 35.073 - type: ndcg_at_1000 value: 38.653 - type: ndcg_at_3 value: 23.345 - type: ndcg_at_5 value: 24.828 - type: precision_at_1 value: 26.906000000000002 - type: precision_at_10 value: 8.547 - type: precision_at_100 value: 1.617 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 17.025000000000002 - type: precision_at_5 value: 12.834000000000001 - type: recall_at_1 value: 12.386999999999999 - type: recall_at_10 value: 33.306999999999995 - type: recall_at_100 value: 57.516 - type: recall_at_1000 value: 77.74799999999999 - type: recall_at_3 value: 21.433 - type: recall_at_5 value: 25.915 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.322 - type: map_at_10 value: 20.469 - type: map_at_100 value: 28.638 - type: map_at_1000 value: 30.433 - type: map_at_3 value: 14.802000000000001 - type: map_at_5 value: 17.297 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.29599999999999 - type: mrr_at_100 value: 76.62400000000001 - type: mrr_at_1000 value: 76.633 - type: mrr_at_3 value: 75.083 - type: mrr_at_5 value: 75.771 - type: ndcg_at_1 value: 54.87499999999999 - type: ndcg_at_10 value: 41.185 - type: ndcg_at_100 value: 46.400000000000006 - type: ndcg_at_1000 value: 54.223 - type: ndcg_at_3 value: 45.489000000000004 - type: ndcg_at_5 value: 43.161 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.300000000000004 - type: precision_at_100 value: 10.607999999999999 - type: precision_at_1000 value: 2.237 - type: precision_at_3 value: 49.083 - type: precision_at_5 value: 41.6 - type: recall_at_1 value: 9.322 - type: recall_at_10 value: 25.696 - type: recall_at_100 value: 52.898 - type: recall_at_1000 value: 77.281 - type: recall_at_3 value: 15.943 - type: recall_at_5 value: 19.836000000000002 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.650000000000006 - type: f1 value: 43.528467245539396 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.56 - type: map_at_10 value: 76.767 - type: map_at_100 value: 77.054 - type: map_at_1000 value: 77.068 - type: map_at_3 value: 75.29299999999999 - type: map_at_5 value: 76.24 - type: mrr_at_1 value: 71.842 - type: mrr_at_10 value: 81.459 - type: mrr_at_100 value: 81.58800000000001 - type: mrr_at_1000 value: 81.59100000000001 - type: mrr_at_3 value: 80.188 - type: mrr_at_5 value: 81.038 - type: ndcg_at_1 value: 71.842 - type: ndcg_at_10 value: 81.51899999999999 - type: ndcg_at_100 value: 82.544 - type: ndcg_at_1000 value: 82.829 - type: ndcg_at_3 value: 78.92 - type: ndcg_at_5 value: 80.406 - type: precision_at_1 value: 71.842 - type: precision_at_10 value: 10.066 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.703000000000003 - type: precision_at_5 value: 19.301 - type: recall_at_1 value: 66.56 - type: recall_at_10 value: 91.55 - type: recall_at_100 value: 95.67099999999999 - type: recall_at_1000 value: 97.539 - type: recall_at_3 value: 84.46900000000001 - type: recall_at_5 value: 88.201 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.087 - type: map_at_10 value: 32.830999999999996 - type: map_at_100 value: 34.814 - type: map_at_1000 value: 34.999 - type: map_at_3 value: 28.198 - type: map_at_5 value: 30.779 - type: mrr_at_1 value: 38.889 - type: mrr_at_10 value: 48.415 - type: mrr_at_100 value: 49.187 - type: mrr_at_1000 value: 49.226 - type: mrr_at_3 value: 45.705 - type: mrr_at_5 value: 47.225 - type: ndcg_at_1 value: 38.889 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.671 - type: ndcg_at_1000 value: 50.744 - type: ndcg_at_3 value: 36.296 - type: ndcg_at_5 value: 37.852999999999994 - type: precision_at_1 value: 38.889 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.8499999999999999 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 18.21 - type: recall_at_1 value: 20.087 - type: recall_at_10 value: 48.042 - type: recall_at_100 value: 73.493 - type: recall_at_1000 value: 91.851 - type: recall_at_3 value: 32.694 - type: recall_at_5 value: 39.099000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 56.99999999999999 - type: map_at_100 value: 57.914 - type: map_at_1000 value: 57.984 - type: map_at_3 value: 53.900999999999996 - type: map_at_5 value: 55.827000000000005 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.955 - type: mrr_at_100 value: 82.164 - type: mrr_at_1000 value: 82.173 - type: mrr_at_3 value: 80.963 - type: mrr_at_5 value: 81.574 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 65.75 - type: ndcg_at_100 value: 68.949 - type: ndcg_at_1000 value: 70.342 - type: ndcg_at_3 value: 61.29 - type: ndcg_at_5 value: 63.747 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 13.571 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.663 - type: precision_at_5 value: 25.136999999999997 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 67.853 - type: recall_at_100 value: 80.365 - type: recall_at_1000 value: 89.629 - type: recall_at_3 value: 57.995 - type: recall_at_5 value: 62.843 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.95200000000001 - type: ap value: 80.73847277002109 - type: f1 value: 85.92406135678594 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.916999999999998 - type: map_at_10 value: 33.23 - type: map_at_100 value: 34.427 - type: map_at_1000 value: 34.477000000000004 - type: map_at_3 value: 29.292 - type: map_at_5 value: 31.6 - type: mrr_at_1 value: 21.547 - type: mrr_at_10 value: 33.839999999999996 - type: mrr_at_100 value: 34.979 - type: mrr_at_1000 value: 35.022999999999996 - type: mrr_at_3 value: 29.988 - type: mrr_at_5 value: 32.259 - type: ndcg_at_1 value: 21.519 - type: ndcg_at_10 value: 40.209 - type: ndcg_at_100 value: 45.954 - type: ndcg_at_1000 value: 47.187 - type: ndcg_at_3 value: 32.227 - type: ndcg_at_5 value: 36.347 - type: precision_at_1 value: 21.519 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.877999999999998 - type: precision_at_5 value: 10.404 - type: recall_at_1 value: 20.916999999999998 - type: recall_at_10 value: 61.7 - type: recall_at_100 value: 88.202 - type: recall_at_1000 value: 97.588 - type: recall_at_3 value: 40.044999999999995 - type: recall_at_5 value: 49.964999999999996 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.02781577747379 - type: f1 value: 92.83653922768306 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.04286365709075 - type: f1 value: 53.43867658525793 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.47276395427035 - type: f1 value: 69.77017399597342 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.3819771351715 - type: f1 value: 76.8484533435409 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.16515993299593 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.77145323314774 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53637706586391 - type: mrr value: 33.7312926288863 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.063999999999999 - type: map_at_10 value: 15.046999999999999 - type: map_at_100 value: 19.116 - type: map_at_1000 value: 20.702 - type: map_at_3 value: 10.932 - type: map_at_5 value: 12.751999999999999 - type: mrr_at_1 value: 50.464 - type: mrr_at_10 value: 58.189 - type: mrr_at_100 value: 58.733999999999995 - type: mrr_at_1000 value: 58.769000000000005 - type: mrr_at_3 value: 56.24400000000001 - type: mrr_at_5 value: 57.68299999999999 - type: ndcg_at_1 value: 48.142 - type: ndcg_at_10 value: 37.897 - type: ndcg_at_100 value: 35.264 - type: ndcg_at_1000 value: 44.033 - type: ndcg_at_3 value: 42.967 - type: ndcg_at_5 value: 40.815 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 28.235 - type: precision_at_100 value: 8.994 - type: precision_at_1000 value: 2.218 - type: precision_at_3 value: 40.041 - type: precision_at_5 value: 35.046 - type: recall_at_1 value: 7.063999999999999 - type: recall_at_10 value: 18.598 - type: recall_at_100 value: 35.577999999999996 - type: recall_at_1000 value: 67.43 - type: recall_at_3 value: 11.562999999999999 - type: recall_at_5 value: 14.771 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 29.046 - type: map_at_10 value: 44.808 - type: map_at_100 value: 45.898 - type: map_at_1000 value: 45.927 - type: map_at_3 value: 40.19 - type: map_at_5 value: 42.897 - type: mrr_at_1 value: 32.706 - type: mrr_at_10 value: 47.275 - type: mrr_at_100 value: 48.075 - type: mrr_at_1000 value: 48.095 - type: mrr_at_3 value: 43.463 - type: mrr_at_5 value: 45.741 - type: ndcg_at_1 value: 32.706 - type: ndcg_at_10 value: 52.835 - type: ndcg_at_100 value: 57.345 - type: ndcg_at_1000 value: 57.985 - type: ndcg_at_3 value: 44.171 - type: ndcg_at_5 value: 48.661 - type: precision_at_1 value: 32.706 - type: precision_at_10 value: 8.895999999999999 - type: precision_at_100 value: 1.143 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.238999999999997 - type: precision_at_5 value: 14.728 - type: recall_at_1 value: 29.046 - type: recall_at_10 value: 74.831 - type: recall_at_100 value: 94.192 - type: recall_at_1000 value: 98.897 - type: recall_at_3 value: 52.37500000000001 - type: recall_at_5 value: 62.732 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.38799999999999 - type: map_at_10 value: 84.315 - type: map_at_100 value: 84.955 - type: map_at_1000 value: 84.971 - type: map_at_3 value: 81.33399999999999 - type: map_at_5 value: 83.21300000000001 - type: mrr_at_1 value: 81.03 - type: mrr_at_10 value: 87.395 - type: mrr_at_100 value: 87.488 - type: mrr_at_1000 value: 87.48899999999999 - type: mrr_at_3 value: 86.41499999999999 - type: mrr_at_5 value: 87.074 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.151 - type: ndcg_at_100 value: 89.38199999999999 - type: ndcg_at_1000 value: 89.479 - type: ndcg_at_3 value: 85.24000000000001 - type: ndcg_at_5 value: 86.856 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.372 - type: precision_at_100 value: 1.526 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.217 - type: precision_at_5 value: 24.502 - type: recall_at_1 value: 70.38799999999999 - type: recall_at_10 value: 95.452 - type: recall_at_100 value: 99.59700000000001 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.11 - type: recall_at_5 value: 91.662 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.334991029213235 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.586500854616666 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.153 - type: map_at_10 value: 14.277000000000001 - type: map_at_100 value: 16.922 - type: map_at_1000 value: 17.302999999999997 - type: map_at_3 value: 9.961 - type: map_at_5 value: 12.257 - type: mrr_at_1 value: 25.4 - type: mrr_at_10 value: 37.458000000000006 - type: mrr_at_100 value: 38.681 - type: mrr_at_1000 value: 38.722 - type: mrr_at_3 value: 34.1 - type: mrr_at_5 value: 36.17 - type: ndcg_at_1 value: 25.4 - type: ndcg_at_10 value: 23.132 - type: ndcg_at_100 value: 32.908 - type: ndcg_at_1000 value: 38.754 - type: ndcg_at_3 value: 21.82 - type: ndcg_at_5 value: 19.353 - type: precision_at_1 value: 25.4 - type: precision_at_10 value: 12.1 - type: precision_at_100 value: 2.628 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.732999999999997 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.153 - type: recall_at_10 value: 24.54 - type: recall_at_100 value: 53.293 - type: recall_at_1000 value: 81.57 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.577 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.86284404925333 - type: cos_sim_spearman value: 78.85870555294795 - type: euclidean_pearson value: 82.20105295276093 - type: euclidean_spearman value: 78.92125617009592 - type: manhattan_pearson value: 82.15840025289069 - type: manhattan_spearman value: 78.85955732900803 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.98747423389027 - type: cos_sim_spearman value: 75.71298531799367 - type: euclidean_pearson value: 81.59709559192291 - type: euclidean_spearman value: 75.40622749225653 - type: manhattan_pearson value: 81.55553547608804 - type: manhattan_spearman value: 75.39380235424899 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.76861330695503 - type: cos_sim_spearman value: 85.72991921531624 - type: euclidean_pearson value: 84.84504307397536 - type: euclidean_spearman value: 86.02679162824732 - type: manhattan_pearson value: 84.79969439220142 - type: manhattan_spearman value: 85.99238837291625 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.31929747511796 - type: cos_sim_spearman value: 81.50806522502528 - type: euclidean_pearson value: 82.93936686512777 - type: euclidean_spearman value: 81.54403447993224 - type: manhattan_pearson value: 82.89696981900828 - type: manhattan_spearman value: 81.52817825470865 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.14413295332908 - type: cos_sim_spearman value: 88.81032027008195 - type: euclidean_pearson value: 88.19205563407645 - type: euclidean_spearman value: 88.89738339479216 - type: manhattan_pearson value: 88.11075942004189 - type: manhattan_spearman value: 88.8297061675564 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.15980075557017 - type: cos_sim_spearman value: 83.81896308594801 - type: euclidean_pearson value: 83.11195254311338 - type: euclidean_spearman value: 84.10479481755407 - type: manhattan_pearson value: 83.13915225100556 - type: manhattan_spearman value: 84.09895591027859 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.93669480147919 - type: cos_sim_spearman value: 87.89861394614361 - type: euclidean_pearson value: 88.37316413202339 - type: euclidean_spearman value: 88.18033817842569 - type: manhattan_pearson value: 88.39427578879469 - type: manhattan_spearman value: 88.09185009236847 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.62215083348255 - type: cos_sim_spearman value: 67.33243665716736 - type: euclidean_pearson value: 67.60871701996284 - type: euclidean_spearman value: 66.75929225238659 - type: manhattan_pearson value: 67.63907838970992 - type: manhattan_spearman value: 66.79313656754846 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.65549191934764 - type: cos_sim_spearman value: 85.73266847750143 - type: euclidean_pearson value: 85.75609932254318 - type: euclidean_spearman value: 85.9452287759371 - type: manhattan_pearson value: 85.69717413063573 - type: manhattan_spearman value: 85.86546318377046 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.08164129085783 - type: mrr value: 96.2877273416489 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 62.09400000000001 - type: map_at_10 value: 71.712 - type: map_at_100 value: 72.128 - type: map_at_1000 value: 72.14399999999999 - type: map_at_3 value: 68.93 - type: map_at_5 value: 70.694 - type: mrr_at_1 value: 65.0 - type: mrr_at_10 value: 72.572 - type: mrr_at_100 value: 72.842 - type: mrr_at_1000 value: 72.856 - type: mrr_at_3 value: 70.44399999999999 - type: mrr_at_5 value: 71.744 - type: ndcg_at_1 value: 65.0 - type: ndcg_at_10 value: 76.178 - type: ndcg_at_100 value: 77.887 - type: ndcg_at_1000 value: 78.227 - type: ndcg_at_3 value: 71.367 - type: ndcg_at_5 value: 73.938 - type: precision_at_1 value: 65.0 - type: precision_at_10 value: 10.033 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 18.4 - type: recall_at_1 value: 62.09400000000001 - type: recall_at_10 value: 89.022 - type: recall_at_100 value: 96.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 75.922 - type: recall_at_5 value: 82.428 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82178217821782 - type: cos_sim_ap value: 95.71282508220798 - type: cos_sim_f1 value: 90.73120494335737 - type: cos_sim_precision value: 93.52441613588111 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 92.98534606529098 - type: dot_f1 value: 86.83024536805209 - type: dot_precision value: 86.96088264794383 - type: dot_recall value: 86.7 - type: euclidean_accuracy value: 99.82475247524752 - type: euclidean_ap value: 95.72927039014849 - type: euclidean_f1 value: 90.89974293059126 - type: euclidean_precision value: 93.54497354497354 - type: euclidean_recall value: 88.4 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.72142177390405 - type: manhattan_f1 value: 91.00152516522625 - type: manhattan_precision value: 92.55429162357808 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.82574257425742 - type: max_ap value: 95.72927039014849 - type: max_f1 value: 91.00152516522625 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.63957663468679 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.003307257923964 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.005825525863905 - type: mrr value: 53.854683919022165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.503611569974098 - type: cos_sim_spearman value: 31.17155564248449 - type: dot_pearson value: 26.740428413981306 - type: dot_spearman value: 26.55727635469746 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.7670000000000001 - type: map_at_100 value: 10.208 - type: map_at_1000 value: 25.997999999999998 - type: map_at_3 value: 0.605 - type: map_at_5 value: 0.9560000000000001 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 90.167 - type: mrr_at_100 value: 90.167 - type: mrr_at_1000 value: 90.167 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 90.167 - type: ndcg_at_1 value: 77.0 - type: ndcg_at_10 value: 68.783 - type: ndcg_at_100 value: 54.196 - type: ndcg_at_1000 value: 52.077 - type: ndcg_at_3 value: 71.642 - type: ndcg_at_5 value: 70.45700000000001 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 23.102 - type: precision_at_3 value: 76.0 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 1.9869999999999999 - type: recall_at_100 value: 13.749 - type: recall_at_1000 value: 50.157 - type: recall_at_3 value: 0.633 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.437 - type: map_at_10 value: 8.791 - type: map_at_100 value: 15.001999999999999 - type: map_at_1000 value: 16.549 - type: map_at_3 value: 3.8080000000000003 - type: map_at_5 value: 5.632000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 36.96 - type: mrr_at_100 value: 37.912 - type: mrr_at_1000 value: 37.912 - type: mrr_at_3 value: 29.592000000000002 - type: mrr_at_5 value: 34.489999999999995 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 22.554 - type: ndcg_at_100 value: 35.197 - type: ndcg_at_1000 value: 46.58 - type: ndcg_at_3 value: 20.285 - type: ndcg_at_5 value: 21.924 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 21.837 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.437 - type: recall_at_10 value: 16.314999999999998 - type: recall_at_100 value: 47.635 - type: recall_at_1000 value: 82.963 - type: recall_at_3 value: 4.955 - type: recall_at_5 value: 8.805 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.6128 - type: ap value: 14.279639861175664 - type: f1 value: 54.922292491204274 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.01188455008489 - type: f1 value: 57.377953019225515 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.306769136544254 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.64701674912082 - type: cos_sim_ap value: 72.46600945328552 - type: cos_sim_f1 value: 67.96572367648784 - type: cos_sim_precision value: 61.21801649397336 - type: cos_sim_recall value: 76.38522427440633 - type: dot_accuracy value: 82.33295583238957 - type: dot_ap value: 62.54843443071716 - type: dot_f1 value: 60.38378562507096 - type: dot_precision value: 52.99980067769583 - type: dot_recall value: 70.15831134564644 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 72.76873850945174 - type: euclidean_f1 value: 68.23556960543262 - type: euclidean_precision value: 61.3344559040202 - type: euclidean_recall value: 76.88654353562005 - type: manhattan_accuracy value: 85.74834594981225 - type: manhattan_ap value: 72.66825372446462 - type: manhattan_f1 value: 68.21539194662853 - type: manhattan_precision value: 62.185056472632496 - type: manhattan_recall value: 75.54089709762533 - type: max_accuracy value: 85.74834594981225 - type: max_ap value: 72.76873850945174 - type: max_f1 value: 68.23556960543262 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.73171110334924 - type: cos_sim_ap value: 85.51855542063649 - type: cos_sim_f1 value: 77.95706775700934 - type: cos_sim_precision value: 74.12524298805887 - type: cos_sim_recall value: 82.20665229442562 - type: dot_accuracy value: 86.94842240074514 - type: dot_ap value: 80.90995345771762 - type: dot_f1 value: 74.20765027322403 - type: dot_precision value: 70.42594385285575 - type: dot_recall value: 78.41854019094548 - type: euclidean_accuracy value: 88.73753250281368 - type: euclidean_ap value: 85.54712254033734 - type: euclidean_f1 value: 78.07565728654365 - type: euclidean_precision value: 75.1120597652081 - type: euclidean_recall value: 81.282722513089 - type: manhattan_accuracy value: 88.72588970388482 - type: manhattan_ap value: 85.52118291594071 - type: manhattan_f1 value: 78.04428724070593 - type: manhattan_precision value: 74.83219105490002 - type: manhattan_recall value: 81.54450261780106 - type: max_accuracy value: 88.73753250281368 - type: max_ap value: 85.54712254033734 - type: max_f1 value: 78.07565728654365 language: - en license: mit --- # gte-base General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base") model = AutoModel.from_pretrained("thenlper/gte-base") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-base') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

intfloat/multilingual-e5-small

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-small results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 36.9996434842022 - type: f1 value: 67.95453679103099 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.64882226980728 - type: ap value: 82.11942130026586 - type: f1 value: 69.87963421606715 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8095952023988 - type: ap value: 24.46869495579561 - type: f1 value: 63.00108480037597 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 64.186295503212 - type: ap value: 15.496804690197042 - type: f1 value: 52.07153895475031 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.699325 - type: ap value: 85.27039559917269 - type: f1 value: 88.65556295032513 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.69799999999999 - type: f1 value: 43.73187348654165 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.245999999999995 - type: f1 value: 39.3863530637684 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.394 - type: f1 value: 39.301223469483446 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.864 - type: f1 value: 37.97974261868003 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.682 - type: f1 value: 37.07399369768313 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.504 - type: f1 value: 36.62317273874278 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 19.061 - type: map_at_10 value: 31.703 - type: map_at_100 value: 32.967 - type: map_at_1000 value: 33.001000000000005 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.564 - type: mrr_at_1 value: 19.559 - type: mrr_at_10 value: 31.874999999999996 - type: mrr_at_100 value: 33.146 - type: mrr_at_1000 value: 33.18 - type: mrr_at_3 value: 27.667 - type: mrr_at_5 value: 29.74 - type: ndcg_at_1 value: 19.061 - type: ndcg_at_10 value: 39.062999999999995 - type: ndcg_at_100 value: 45.184000000000005 - type: ndcg_at_1000 value: 46.115 - type: ndcg_at_3 value: 30.203000000000003 - type: ndcg_at_5 value: 33.953 - type: precision_at_1 value: 19.061 - type: precision_at_10 value: 6.279999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 12.706999999999999 - type: precision_at_5 value: 9.431000000000001 - type: recall_at_1 value: 19.061 - type: recall_at_10 value: 62.802 - type: recall_at_100 value: 91.323 - type: recall_at_1000 value: 98.72 - type: recall_at_3 value: 38.122 - type: recall_at_5 value: 47.155 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.22266660528253 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 30.79980849482483 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.8790068352054 - type: mrr value: 71.78791276436706 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.36328364043163 - type: cos_sim_spearman value: 82.26211536195868 - type: euclidean_pearson value: 80.3183865039173 - type: euclidean_spearman value: 79.88495276296132 - type: manhattan_pearson value: 80.14484480692127 - type: manhattan_spearman value: 80.39279565980743 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.0375782881002 - type: f1 value: 97.86012526096033 - type: precision value: 97.77139874739039 - type: recall value: 98.0375782881002 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 93.35241030156286 - type: f1 value: 92.66050333846944 - type: precision value: 92.3306919069631 - type: recall value: 93.35241030156286 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 94.0699688257707 - type: f1 value: 93.50236693222492 - type: precision value: 93.22791825424315 - type: recall value: 94.0699688257707 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 89.25750394944708 - type: f1 value: 88.79234684921889 - type: precision value: 88.57293312269616 - type: recall value: 89.25750394944708 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.41558441558442 - type: f1 value: 79.25886487487219 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.747820820329736 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 27.045143830596146 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.252999999999997 - type: map_at_10 value: 31.655916666666666 - type: map_at_100 value: 32.680749999999996 - type: map_at_1000 value: 32.79483333333334 - type: map_at_3 value: 29.43691666666666 - type: map_at_5 value: 30.717416666666665 - type: mrr_at_1 value: 28.602750000000004 - type: mrr_at_10 value: 35.56875 - type: mrr_at_100 value: 36.3595 - type: mrr_at_1000 value: 36.427749999999996 - type: mrr_at_3 value: 33.586166666666664 - type: mrr_at_5 value: 34.73641666666666 - type: ndcg_at_1 value: 28.602750000000004 - type: ndcg_at_10 value: 36.06933333333334 - type: ndcg_at_100 value: 40.70141666666667 - type: ndcg_at_1000 value: 43.24341666666667 - type: ndcg_at_3 value: 32.307916666666664 - type: ndcg_at_5 value: 34.129999999999995 - type: precision_at_1 value: 28.602750000000004 - type: precision_at_10 value: 6.097666666666667 - type: precision_at_100 value: 0.9809166666666668 - type: precision_at_1000 value: 0.13766666666666663 - type: precision_at_3 value: 14.628166666666667 - type: precision_at_5 value: 10.266916666666667 - type: recall_at_1 value: 24.252999999999997 - type: recall_at_10 value: 45.31916666666667 - type: recall_at_100 value: 66.03575000000001 - type: recall_at_1000 value: 83.94708333333334 - type: recall_at_3 value: 34.71941666666666 - type: recall_at_5 value: 39.46358333333333 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.024000000000001 - type: map_at_10 value: 15.644 - type: map_at_100 value: 17.154 - type: map_at_1000 value: 17.345 - type: map_at_3 value: 13.028 - type: map_at_5 value: 14.251 - type: mrr_at_1 value: 19.674 - type: mrr_at_10 value: 29.826999999999998 - type: mrr_at_100 value: 30.935000000000002 - type: mrr_at_1000 value: 30.987 - type: mrr_at_3 value: 26.645000000000003 - type: mrr_at_5 value: 28.29 - type: ndcg_at_1 value: 19.674 - type: ndcg_at_10 value: 22.545 - type: ndcg_at_100 value: 29.207 - type: ndcg_at_1000 value: 32.912 - type: ndcg_at_3 value: 17.952 - type: ndcg_at_5 value: 19.363 - type: precision_at_1 value: 19.674 - type: precision_at_10 value: 7.212000000000001 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 13.507 - type: precision_at_5 value: 10.397 - type: recall_at_1 value: 9.024000000000001 - type: recall_at_10 value: 28.077999999999996 - type: recall_at_100 value: 51.403 - type: recall_at_1000 value: 72.406 - type: recall_at_3 value: 16.768 - type: recall_at_5 value: 20.737 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.012 - type: map_at_10 value: 17.138 - type: map_at_100 value: 24.146 - type: map_at_1000 value: 25.622 - type: map_at_3 value: 12.552 - type: map_at_5 value: 14.435 - type: mrr_at_1 value: 62.25000000000001 - type: mrr_at_10 value: 71.186 - type: mrr_at_100 value: 71.504 - type: mrr_at_1000 value: 71.514 - type: mrr_at_3 value: 69.333 - type: mrr_at_5 value: 70.408 - type: ndcg_at_1 value: 49.75 - type: ndcg_at_10 value: 37.76 - type: ndcg_at_100 value: 42.071 - type: ndcg_at_1000 value: 49.309 - type: ndcg_at_3 value: 41.644 - type: ndcg_at_5 value: 39.812999999999995 - type: precision_at_1 value: 62.25000000000001 - type: precision_at_10 value: 30.15 - type: precision_at_100 value: 9.753 - type: precision_at_1000 value: 1.9189999999999998 - type: precision_at_3 value: 45.667 - type: precision_at_5 value: 39.15 - type: recall_at_1 value: 8.012 - type: recall_at_10 value: 22.599 - type: recall_at_100 value: 48.068 - type: recall_at_1000 value: 71.328 - type: recall_at_3 value: 14.043 - type: recall_at_5 value: 17.124 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 42.455 - type: f1 value: 37.59462649781862 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 58.092 - type: map_at_10 value: 69.586 - type: map_at_100 value: 69.968 - type: map_at_1000 value: 69.982 - type: map_at_3 value: 67.48100000000001 - type: map_at_5 value: 68.915 - type: mrr_at_1 value: 62.166 - type: mrr_at_10 value: 73.588 - type: mrr_at_100 value: 73.86399999999999 - type: mrr_at_1000 value: 73.868 - type: mrr_at_3 value: 71.6 - type: mrr_at_5 value: 72.99 - type: ndcg_at_1 value: 62.166 - type: ndcg_at_10 value: 75.27199999999999 - type: ndcg_at_100 value: 76.816 - type: ndcg_at_1000 value: 77.09700000000001 - type: ndcg_at_3 value: 71.36 - type: ndcg_at_5 value: 73.785 - type: precision_at_1 value: 62.166 - type: precision_at_10 value: 9.716 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 28.278 - type: precision_at_5 value: 18.343999999999998 - type: recall_at_1 value: 58.092 - type: recall_at_10 value: 88.73400000000001 - type: recall_at_100 value: 95.195 - type: recall_at_1000 value: 97.04599999999999 - type: recall_at_3 value: 78.45 - type: recall_at_5 value: 84.316 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 16.649 - type: map_at_10 value: 26.457000000000004 - type: map_at_100 value: 28.169 - type: map_at_1000 value: 28.352 - type: map_at_3 value: 23.305 - type: map_at_5 value: 25.169000000000004 - type: mrr_at_1 value: 32.407000000000004 - type: mrr_at_10 value: 40.922 - type: mrr_at_100 value: 41.931000000000004 - type: mrr_at_1000 value: 41.983 - type: mrr_at_3 value: 38.786 - type: mrr_at_5 value: 40.205999999999996 - type: ndcg_at_1 value: 32.407000000000004 - type: ndcg_at_10 value: 33.314 - type: ndcg_at_100 value: 40.312 - type: ndcg_at_1000 value: 43.685 - type: ndcg_at_3 value: 30.391000000000002 - type: ndcg_at_5 value: 31.525 - type: precision_at_1 value: 32.407000000000004 - type: precision_at_10 value: 8.966000000000001 - type: precision_at_100 value: 1.6019999999999999 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 20.165 - type: precision_at_5 value: 14.722 - type: recall_at_1 value: 16.649 - type: recall_at_10 value: 39.117000000000004 - type: recall_at_100 value: 65.726 - type: recall_at_1000 value: 85.784 - type: recall_at_3 value: 27.914 - type: recall_at_5 value: 33.289 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.253 - type: map_at_10 value: 56.16799999999999 - type: map_at_100 value: 57.06099999999999 - type: map_at_1000 value: 57.126 - type: map_at_3 value: 52.644999999999996 - type: map_at_5 value: 54.909 - type: mrr_at_1 value: 72.505 - type: mrr_at_10 value: 79.66 - type: mrr_at_100 value: 79.869 - type: mrr_at_1000 value: 79.88 - type: mrr_at_3 value: 78.411 - type: mrr_at_5 value: 79.19800000000001 - type: ndcg_at_1 value: 72.505 - type: ndcg_at_10 value: 65.094 - type: ndcg_at_100 value: 68.219 - type: ndcg_at_1000 value: 69.515 - type: ndcg_at_3 value: 59.99 - type: ndcg_at_5 value: 62.909000000000006 - type: precision_at_1 value: 72.505 - type: precision_at_10 value: 13.749 - type: precision_at_100 value: 1.619 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.357 - type: precision_at_5 value: 25.313000000000002 - type: recall_at_1 value: 36.253 - type: recall_at_10 value: 68.744 - type: recall_at_100 value: 80.925 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 57.535000000000004 - type: recall_at_5 value: 63.282000000000004 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 80.82239999999999 - type: ap value: 75.65895781725314 - type: f1 value: 80.75880969095746 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.624 - type: map_at_10 value: 34.075 - type: map_at_100 value: 35.229 - type: map_at_1000 value: 35.276999999999994 - type: map_at_3 value: 30.245 - type: map_at_5 value: 32.42 - type: mrr_at_1 value: 22.264 - type: mrr_at_10 value: 34.638000000000005 - type: mrr_at_100 value: 35.744 - type: mrr_at_1000 value: 35.787 - type: mrr_at_3 value: 30.891000000000002 - type: mrr_at_5 value: 33.042 - type: ndcg_at_1 value: 22.264 - type: ndcg_at_10 value: 40.991 - type: ndcg_at_100 value: 46.563 - type: ndcg_at_1000 value: 47.743 - type: ndcg_at_3 value: 33.198 - type: ndcg_at_5 value: 37.069 - type: precision_at_1 value: 22.264 - type: precision_at_10 value: 6.5089999999999995 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 14.216999999999999 - type: precision_at_5 value: 10.487 - type: recall_at_1 value: 21.624 - type: recall_at_10 value: 62.303 - type: recall_at_100 value: 88.124 - type: recall_at_1000 value: 97.08 - type: recall_at_3 value: 41.099999999999994 - type: recall_at_5 value: 50.381 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.06703146374831 - type: f1 value: 90.86867815863172 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.46970977740209 - type: f1 value: 86.36832872036588 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.26951300867245 - type: f1 value: 88.93561193959502 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.22799874725963 - type: f1 value: 84.30490069236556 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 86.02007888131948 - type: f1 value: 85.39376041027991 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.34900542495481 - type: f1 value: 85.39859673336713 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 71.078431372549 - type: f1 value: 53.45071102002276 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 65.85798816568047 - type: f1 value: 46.53112748993529 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.96864576384256 - type: f1 value: 45.966703022829506 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - 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type: euclidean_ap value: 84.80003825477253 - type: euclidean_f1 value: 77.67194179854496 - type: euclidean_precision value: 75.7128235122094 - type: euclidean_recall value: 79.73514012935017 - type: manhattan_accuracy value: 88.62692591298949 - type: manhattan_ap value: 84.80451408255276 - type: manhattan_f1 value: 77.69888949572183 - type: manhattan_precision value: 73.70311528631622 - type: manhattan_recall value: 82.15275639051433 - type: max_accuracy value: 88.6987231730508 - type: max_ap value: 84.82526278228542 - type: max_f1 value: 77.69888949572183 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-small [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-small') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

intfloat/e5-small-v2

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: e5-small-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.59701492537313 - type: ap value: 41.67064885731708 - type: f1 value: 71.86465946398573 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.265875 - type: ap value: 87.67633085349644 - type: f1 value: 91.24297521425744 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.882000000000005 - type: f1 value: 45.08058870381236 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 20.697 - type: map_at_10 value: 33.975 - type: map_at_100 value: 35.223 - type: map_at_1000 value: 35.260000000000005 - type: map_at_3 value: 29.776999999999997 - type: map_at_5 value: 32.035000000000004 - type: mrr_at_1 value: 20.982 - type: mrr_at_10 value: 34.094 - type: mrr_at_100 value: 35.343 - type: mrr_at_1000 value: 35.38 - type: mrr_at_3 value: 29.884 - type: mrr_at_5 value: 32.141999999999996 - type: ndcg_at_1 value: 20.697 - type: ndcg_at_10 value: 41.668 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 48.305 - type: ndcg_at_3 value: 32.928000000000004 - type: ndcg_at_5 value: 36.998999999999995 - type: precision_at_1 value: 20.697 - type: precision_at_10 value: 6.636 - type: precision_at_100 value: 0.924 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.035 - type: precision_at_5 value: 10.398 - type: recall_at_1 value: 20.697 - type: recall_at_10 value: 66.35799999999999 - type: recall_at_100 value: 92.39 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 42.105 - type: recall_at_5 value: 51.991 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.1169517447068 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 34.79553720107097 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.10811337308168 - type: mrr value: 71.56410763751482 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.46834918248696 - type: cos_sim_spearman value: 79.4289182755206 - type: euclidean_pearson value: 76.26662973727008 - type: euclidean_spearman value: 78.11744260952536 - type: manhattan_pearson value: 76.08175262609434 - type: manhattan_spearman value: 78.29395265552289 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 81.63636363636364 - type: f1 value: 81.55779952376953 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.88541137137571 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.05205685274407 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.293999999999997 - type: map_at_10 value: 39.876 - type: map_at_100 value: 41.315000000000005 - type: map_at_1000 value: 41.451 - type: map_at_3 value: 37.194 - type: map_at_5 value: 38.728 - type: mrr_at_1 value: 37.053000000000004 - type: mrr_at_10 value: 45.281 - type: mrr_at_100 value: 46.188 - type: mrr_at_1000 value: 46.245999999999995 - type: mrr_at_3 value: 43.228 - type: mrr_at_5 value: 44.366 - type: ndcg_at_1 value: 37.053000000000004 - type: ndcg_at_10 value: 45.086 - type: ndcg_at_100 value: 50.756 - type: ndcg_at_1000 value: 53.123 - type: ndcg_at_3 value: 41.416 - type: ndcg_at_5 value: 43.098 - type: precision_at_1 value: 37.053000000000004 - type: precision_at_10 value: 8.34 - type: precision_at_100 value: 1.346 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 19.647000000000002 - type: precision_at_5 value: 13.877 - type: recall_at_1 value: 30.293999999999997 - type: recall_at_10 value: 54.309 - type: recall_at_100 value: 78.59 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 43.168 - type: recall_at_5 value: 48.192 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.738000000000003 - type: map_at_10 value: 36.925999999999995 - type: map_at_100 value: 38.017 - type: map_at_1000 value: 38.144 - type: map_at_3 value: 34.446 - type: map_at_5 value: 35.704 - type: mrr_at_1 value: 35.478 - type: mrr_at_10 value: 42.786 - type: mrr_at_100 value: 43.458999999999996 - type: mrr_at_1000 value: 43.507 - type: mrr_at_3 value: 40.648 - type: mrr_at_5 value: 41.804 - type: ndcg_at_1 value: 35.478 - type: ndcg_at_10 value: 42.044 - type: ndcg_at_100 value: 46.249 - type: ndcg_at_1000 value: 48.44 - type: ndcg_at_3 value: 38.314 - type: ndcg_at_5 value: 39.798 - type: precision_at_1 value: 35.478 - type: precision_at_10 value: 7.764 - type: precision_at_100 value: 1.253 - type: precision_at_1000 value: 0.174 - type: precision_at_3 value: 18.047 - type: precision_at_5 value: 12.637 - type: recall_at_1 value: 28.738000000000003 - type: recall_at_10 value: 50.659 - type: recall_at_100 value: 68.76299999999999 - type: recall_at_1000 value: 82.811 - type: recall_at_3 value: 39.536 - type: recall_at_5 value: 43.763999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.565 - type: map_at_10 value: 50.168 - type: map_at_100 value: 51.11 - type: map_at_1000 value: 51.173 - type: map_at_3 value: 47.044000000000004 - type: map_at_5 value: 48.838 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 53.596999999999994 - type: mrr_at_100 value: 54.211 - type: mrr_at_1000 value: 54.247 - type: mrr_at_3 value: 51.202000000000005 - type: mrr_at_5 value: 52.608999999999995 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 55.694 - type: ndcg_at_100 value: 59.518 - type: ndcg_at_1000 value: 60.907 - type: ndcg_at_3 value: 50.395999999999994 - type: ndcg_at_5 value: 53.022999999999996 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 8.84 - type: precision_at_100 value: 1.162 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 22.153 - type: precision_at_5 value: 15.260000000000002 - type: recall_at_1 value: 38.565 - type: recall_at_10 value: 68.65 - type: recall_at_100 value: 85.37400000000001 - type: recall_at_1000 value: 95.37400000000001 - type: recall_at_3 value: 54.645999999999994 - type: recall_at_5 value: 60.958 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.945 - type: map_at_10 value: 30.641000000000002 - type: map_at_100 value: 31.599 - type: map_at_1000 value: 31.691000000000003 - type: map_at_3 value: 28.405 - type: map_at_5 value: 29.704000000000004 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 32.22 - type: mrr_at_100 value: 33.138 - type: mrr_at_1000 value: 33.214 - type: mrr_at_3 value: 30.151 - type: mrr_at_5 value: 31.298 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 34.638000000000005 - type: ndcg_at_100 value: 39.486 - type: ndcg_at_1000 value: 41.936 - type: ndcg_at_3 value: 30.333 - type: ndcg_at_5 value: 32.482 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 5.153 - type: precision_at_100 value: 0.7929999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.429 - type: precision_at_5 value: 8.723 - type: recall_at_1 value: 23.945 - type: recall_at_10 value: 45.412 - type: recall_at_100 value: 67.836 - type: recall_at_1000 value: 86.467 - type: recall_at_3 value: 34.031 - type: recall_at_5 value: 39.039 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.419 - type: map_at_10 value: 20.858999999999998 - type: map_at_100 value: 22.067999999999998 - type: map_at_1000 value: 22.192 - type: map_at_3 value: 18.673000000000002 - type: map_at_5 value: 19.968 - type: mrr_at_1 value: 17.785999999999998 - type: mrr_at_10 value: 24.878 - type: mrr_at_100 value: 26.021 - type: mrr_at_1000 value: 26.095000000000002 - type: mrr_at_3 value: 22.616 - type: mrr_at_5 value: 23.785 - type: ndcg_at_1 value: 17.785999999999998 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 31.05 - type: ndcg_at_1000 value: 34.052 - type: ndcg_at_3 value: 21.117 - type: ndcg_at_5 value: 23.048 - type: precision_at_1 value: 17.785999999999998 - type: precision_at_10 value: 4.590000000000001 - type: precision_at_100 value: 0.864 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.313 - type: recall_at_1 value: 14.419 - type: recall_at_10 value: 34.477999999999994 - type: recall_at_100 value: 60.02499999999999 - type: recall_at_1000 value: 81.646 - type: recall_at_3 value: 23.515 - type: recall_at_5 value: 28.266999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.268 - type: map_at_10 value: 35.114000000000004 - type: map_at_100 value: 36.212 - type: map_at_1000 value: 36.333 - type: map_at_3 value: 32.436 - type: map_at_5 value: 33.992 - type: mrr_at_1 value: 31.761 - type: mrr_at_10 value: 40.355999999999995 - type: mrr_at_100 value: 41.125 - type: mrr_at_1000 value: 41.186 - type: mrr_at_3 value: 37.937 - type: mrr_at_5 value: 39.463 - type: ndcg_at_1 value: 31.761 - type: ndcg_at_10 value: 40.422000000000004 - type: ndcg_at_100 value: 45.458999999999996 - type: ndcg_at_1000 value: 47.951 - type: ndcg_at_3 value: 35.972 - type: ndcg_at_5 value: 38.272 - type: precision_at_1 value: 31.761 - type: precision_at_10 value: 7.103 - type: precision_at_100 value: 1.133 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 16.779 - type: precision_at_5 value: 11.877 - type: recall_at_1 value: 26.268 - type: recall_at_10 value: 51.053000000000004 - type: recall_at_100 value: 72.702 - type: recall_at_1000 value: 89.521 - type: recall_at_3 value: 38.619 - type: recall_at_5 value: 44.671 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.230999999999998 - type: map_at_10 value: 34.227000000000004 - type: map_at_100 value: 35.370000000000005 - type: map_at_1000 value: 35.488 - type: map_at_3 value: 31.496000000000002 - type: map_at_5 value: 33.034 - type: mrr_at_1 value: 30.822 - type: mrr_at_10 value: 39.045 - type: mrr_at_100 value: 39.809 - type: mrr_at_1000 value: 39.873 - type: mrr_at_3 value: 36.663000000000004 - type: mrr_at_5 value: 37.964 - type: ndcg_at_1 value: 30.822 - type: ndcg_at_10 value: 39.472 - type: ndcg_at_100 value: 44.574999999999996 - type: ndcg_at_1000 value: 47.162 - type: ndcg_at_3 value: 34.929 - type: ndcg_at_5 value: 37.002 - type: precision_at_1 value: 30.822 - type: precision_at_10 value: 7.055 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 16.591 - type: precision_at_5 value: 11.667 - type: recall_at_1 value: 25.230999999999998 - type: recall_at_10 value: 50.42100000000001 - type: recall_at_100 value: 72.685 - type: recall_at_1000 value: 90.469 - type: recall_at_3 value: 37.503 - type: recall_at_5 value: 43.123 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.604166666666664 - type: map_at_10 value: 32.427166666666665 - type: map_at_100 value: 33.51474999999999 - type: map_at_1000 value: 33.6345 - type: map_at_3 value: 30.02366666666667 - type: map_at_5 value: 31.382333333333328 - type: mrr_at_1 value: 29.001166666666666 - type: mrr_at_10 value: 36.3315 - type: mrr_at_100 value: 37.16683333333333 - type: mrr_at_1000 value: 37.23341666666668 - type: mrr_at_3 value: 34.19916666666667 - type: mrr_at_5 value: 35.40458333333334 - type: ndcg_at_1 value: 29.001166666666666 - type: ndcg_at_10 value: 37.06883333333334 - type: ndcg_at_100 value: 41.95816666666666 - type: ndcg_at_1000 value: 44.501583333333336 - type: ndcg_at_3 value: 32.973499999999994 - type: ndcg_at_5 value: 34.90833333333334 - type: precision_at_1 value: 29.001166666666666 - type: precision_at_10 value: 6.336 - type: precision_at_100 value: 1.0282499999999999 - type: precision_at_1000 value: 0.14391666666666664 - type: precision_at_3 value: 14.932499999999996 - type: precision_at_5 value: 10.50825 - type: recall_at_1 value: 24.604166666666664 - type: recall_at_10 value: 46.9525 - type: recall_at_100 value: 68.67816666666667 - type: recall_at_1000 value: 86.59783333333334 - type: recall_at_3 value: 35.49783333333333 - type: recall_at_5 value: 40.52525000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.559 - type: map_at_10 value: 29.023 - type: map_at_100 value: 29.818 - type: map_at_1000 value: 29.909000000000002 - type: map_at_3 value: 27.037 - type: map_at_5 value: 28.225 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 31.962000000000003 - type: mrr_at_100 value: 32.726 - type: mrr_at_1000 value: 32.800000000000004 - type: mrr_at_3 value: 30.266 - type: mrr_at_5 value: 31.208999999999996 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 32.53 - type: ndcg_at_100 value: 36.758 - type: ndcg_at_1000 value: 39.362 - type: ndcg_at_3 value: 28.985 - type: ndcg_at_5 value: 30.757 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 4.968999999999999 - type: precision_at_100 value: 0.759 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 12.219 - type: precision_at_5 value: 8.527999999999999 - type: recall_at_1 value: 23.559 - type: recall_at_10 value: 40.585 - type: recall_at_100 value: 60.306000000000004 - type: recall_at_1000 value: 80.11 - type: recall_at_3 value: 30.794 - type: recall_at_5 value: 35.186 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.384999999999998 - type: map_at_10 value: 22.142 - type: map_at_100 value: 23.057 - type: map_at_1000 value: 23.177 - type: map_at_3 value: 20.29 - type: map_at_5 value: 21.332 - type: mrr_at_1 value: 19.89 - type: mrr_at_10 value: 25.771 - type: mrr_at_100 value: 26.599 - type: mrr_at_1000 value: 26.680999999999997 - type: mrr_at_3 value: 23.962 - type: mrr_at_5 value: 24.934 - type: ndcg_at_1 value: 19.89 - type: ndcg_at_10 value: 25.97 - type: ndcg_at_100 value: 30.605 - type: ndcg_at_1000 value: 33.619 - type: ndcg_at_3 value: 22.704 - type: ndcg_at_5 value: 24.199 - type: precision_at_1 value: 19.89 - type: precision_at_10 value: 4.553 - type: precision_at_100 value: 0.8049999999999999 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 10.541 - type: precision_at_5 value: 7.46 - type: recall_at_1 value: 16.384999999999998 - type: recall_at_10 value: 34.001 - type: recall_at_100 value: 55.17100000000001 - type: recall_at_1000 value: 77.125 - type: recall_at_3 value: 24.618000000000002 - type: recall_at_5 value: 28.695999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.726 - type: map_at_10 value: 31.227 - type: map_at_100 value: 32.311 - type: map_at_1000 value: 32.419 - type: map_at_3 value: 28.765 - type: map_at_5 value: 30.229 - type: mrr_at_1 value: 27.705000000000002 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 35.931000000000004 - type: mrr_at_1000 value: 36 - type: mrr_at_3 value: 32.603 - type: mrr_at_5 value: 34.117999999999995 - type: ndcg_at_1 value: 27.705000000000002 - type: ndcg_at_10 value: 35.968 - type: ndcg_at_100 value: 41.197 - type: ndcg_at_1000 value: 43.76 - type: ndcg_at_3 value: 31.304 - type: ndcg_at_5 value: 33.661 - type: precision_at_1 value: 27.705000000000002 - type: precision_at_10 value: 5.942 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 13.868 - type: precision_at_5 value: 9.944 - type: recall_at_1 value: 23.726 - type: recall_at_10 value: 46.786 - type: recall_at_100 value: 70.072 - type: recall_at_1000 value: 88.2 - type: recall_at_3 value: 33.981 - type: recall_at_5 value: 39.893 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.344 - type: map_at_10 value: 31.636999999999997 - type: map_at_100 value: 33.065 - type: map_at_1000 value: 33.300000000000004 - type: map_at_3 value: 29.351 - type: map_at_5 value: 30.432 - type: mrr_at_1 value: 27.866000000000003 - type: mrr_at_10 value: 35.587 - type: mrr_at_100 value: 36.52 - type: mrr_at_1000 value: 36.597 - type: mrr_at_3 value: 33.696 - type: mrr_at_5 value: 34.713 - type: ndcg_at_1 value: 27.866000000000003 - type: ndcg_at_10 value: 36.61 - type: ndcg_at_100 value: 41.88 - type: ndcg_at_1000 value: 45.105000000000004 - type: ndcg_at_3 value: 33.038000000000004 - type: ndcg_at_5 value: 34.331 - type: precision_at_1 value: 27.866000000000003 - type: precision_at_10 value: 6.917 - type: precision_at_100 value: 1.3599999999999999 - type: precision_at_1000 value: 0.233 - type: precision_at_3 value: 15.547 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.344 - type: recall_at_10 value: 45.782000000000004 - type: recall_at_100 value: 69.503 - type: recall_at_1000 value: 90.742 - type: recall_at_3 value: 35.160000000000004 - type: recall_at_5 value: 39.058 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.776 - type: map_at_10 value: 27.285999999999998 - type: map_at_100 value: 28.235 - type: map_at_1000 value: 28.337 - type: map_at_3 value: 25.147000000000002 - type: map_at_5 value: 26.401999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 29.409999999999997 - type: mrr_at_100 value: 30.275000000000002 - type: mrr_at_1000 value: 30.354999999999997 - type: mrr_at_3 value: 27.418 - type: mrr_at_5 value: 28.592000000000002 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 31.239 - type: ndcg_at_100 value: 35.965 - type: ndcg_at_1000 value: 38.602 - type: ndcg_at_3 value: 27.174 - type: ndcg_at_5 value: 29.229 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 4.806 - type: precision_at_100 value: 0.776 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.022 - type: recall_at_1 value: 20.776 - type: recall_at_10 value: 41.294 - type: recall_at_100 value: 63.111 - type: recall_at_1000 value: 82.88600000000001 - type: recall_at_3 value: 30.403000000000002 - type: recall_at_5 value: 35.455999999999996 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.376 - type: map_at_10 value: 15.926000000000002 - type: map_at_100 value: 17.585 - type: map_at_1000 value: 17.776 - type: map_at_3 value: 13.014000000000001 - type: map_at_5 value: 14.417 - type: mrr_at_1 value: 20.195 - type: mrr_at_10 value: 29.95 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.108000000000004 - type: mrr_at_3 value: 26.667 - type: mrr_at_5 value: 28.458 - type: ndcg_at_1 value: 20.195 - type: ndcg_at_10 value: 22.871 - type: ndcg_at_100 value: 29.921999999999997 - type: ndcg_at_1000 value: 33.672999999999995 - type: ndcg_at_3 value: 17.782999999999998 - type: ndcg_at_5 value: 19.544 - type: precision_at_1 value: 20.195 - type: precision_at_10 value: 7.394 - type: precision_at_100 value: 1.493 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 13.073 - type: precision_at_5 value: 10.436 - type: recall_at_1 value: 9.376 - type: recall_at_10 value: 28.544999999999998 - type: recall_at_100 value: 53.147999999999996 - type: recall_at_1000 value: 74.62 - type: recall_at_3 value: 16.464000000000002 - type: recall_at_5 value: 21.004 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.415000000000001 - type: map_at_10 value: 18.738 - type: map_at_100 value: 27.291999999999998 - type: map_at_1000 value: 28.992 - type: map_at_3 value: 13.196 - type: map_at_5 value: 15.539 - type: mrr_at_1 value: 66.5 - type: mrr_at_10 value: 74.518 - type: mrr_at_100 value: 74.86 - type: mrr_at_1000 value: 74.87 - type: mrr_at_3 value: 72.375 - type: mrr_at_5 value: 73.86200000000001 - type: ndcg_at_1 value: 54.37499999999999 - type: ndcg_at_10 value: 41.317 - type: ndcg_at_100 value: 45.845 - type: ndcg_at_1000 value: 52.92 - type: ndcg_at_3 value: 44.983000000000004 - type: ndcg_at_5 value: 42.989 - type: precision_at_1 value: 66.5 - type: precision_at_10 value: 33.6 - type: precision_at_100 value: 10.972999999999999 - type: precision_at_1000 value: 2.214 - type: precision_at_3 value: 48.583 - type: precision_at_5 value: 42.15 - type: recall_at_1 value: 8.415000000000001 - type: recall_at_10 value: 24.953 - type: recall_at_100 value: 52.48199999999999 - type: recall_at_1000 value: 75.093 - type: recall_at_3 value: 14.341000000000001 - type: recall_at_5 value: 18.468 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.06499999999999 - type: f1 value: 41.439327599975385 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.02 - type: map_at_10 value: 76.68599999999999 - type: map_at_100 value: 76.959 - type: map_at_1000 value: 76.972 - type: map_at_3 value: 75.024 - type: map_at_5 value: 76.153 - type: mrr_at_1 value: 71.197 - type: mrr_at_10 value: 81.105 - type: mrr_at_100 value: 81.232 - type: mrr_at_1000 value: 81.233 - type: mrr_at_3 value: 79.758 - type: mrr_at_5 value: 80.69 - type: ndcg_at_1 value: 71.197 - type: ndcg_at_10 value: 81.644 - type: ndcg_at_100 value: 82.645 - type: ndcg_at_1000 value: 82.879 - type: ndcg_at_3 value: 78.792 - type: ndcg_at_5 value: 80.528 - type: precision_at_1 value: 71.197 - type: precision_at_10 value: 10.206999999999999 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 30.868000000000002 - type: precision_at_5 value: 19.559 - type: recall_at_1 value: 66.02 - type: recall_at_10 value: 92.50699999999999 - type: recall_at_100 value: 96.497 - type: recall_at_1000 value: 97.956 - type: recall_at_3 value: 84.866 - type: recall_at_5 value: 89.16199999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 17.948 - type: map_at_10 value: 29.833 - type: map_at_100 value: 31.487 - type: map_at_1000 value: 31.674000000000003 - type: map_at_3 value: 26.029999999999998 - type: map_at_5 value: 28.038999999999998 - type: mrr_at_1 value: 34.721999999999994 - type: mrr_at_10 value: 44.214999999999996 - type: mrr_at_100 value: 44.994 - type: mrr_at_1000 value: 45.051 - type: mrr_at_3 value: 41.667 - type: mrr_at_5 value: 43.032 - type: ndcg_at_1 value: 34.721999999999994 - type: ndcg_at_10 value: 37.434 - type: ndcg_at_100 value: 43.702000000000005 - type: ndcg_at_1000 value: 46.993 - type: ndcg_at_3 value: 33.56 - type: ndcg_at_5 value: 34.687 - type: precision_at_1 value: 34.721999999999994 - type: precision_at_10 value: 10.401 - type: precision_at_100 value: 1.7049999999999998 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 22.531000000000002 - type: precision_at_5 value: 16.42 - type: recall_at_1 value: 17.948 - type: recall_at_10 value: 45.062999999999995 - type: recall_at_100 value: 68.191 - type: recall_at_1000 value: 87.954 - type: recall_at_3 value: 31.112000000000002 - type: recall_at_5 value: 36.823 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.644 - type: map_at_10 value: 57.658 - type: map_at_100 value: 58.562000000000005 - type: map_at_1000 value: 58.62500000000001 - type: map_at_3 value: 54.022999999999996 - type: map_at_5 value: 56.293000000000006 - type: mrr_at_1 value: 73.288 - type: mrr_at_10 value: 80.51700000000001 - type: mrr_at_100 value: 80.72 - type: mrr_at_1000 value: 80.728 - type: mrr_at_3 value: 79.33200000000001 - type: mrr_at_5 value: 80.085 - type: ndcg_at_1 value: 73.288 - type: ndcg_at_10 value: 66.61 - type: ndcg_at_100 value: 69.723 - type: ndcg_at_1000 value: 70.96000000000001 - type: ndcg_at_3 value: 61.358999999999995 - type: ndcg_at_5 value: 64.277 - type: precision_at_1 value: 73.288 - type: precision_at_10 value: 14.17 - type: precision_at_100 value: 1.659 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.487 - type: precision_at_5 value: 25.999 - type: recall_at_1 value: 36.644 - type: recall_at_10 value: 70.851 - type: recall_at_100 value: 82.94399999999999 - type: recall_at_1000 value: 91.134 - type: recall_at_3 value: 59.230000000000004 - type: recall_at_5 value: 64.997 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.00280000000001 - type: ap value: 80.46302061021223 - type: f1 value: 85.9592921596419 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.541 - type: map_at_10 value: 34.625 - type: map_at_100 value: 35.785 - type: map_at_1000 value: 35.831 - type: map_at_3 value: 30.823 - type: map_at_5 value: 32.967999999999996 - type: mrr_at_1 value: 23.180999999999997 - type: mrr_at_10 value: 35.207 - type: mrr_at_100 value: 36.315 - type: mrr_at_1000 value: 36.355 - type: mrr_at_3 value: 31.483 - type: mrr_at_5 value: 33.589999999999996 - type: ndcg_at_1 value: 23.195 - type: ndcg_at_10 value: 41.461 - type: ndcg_at_100 value: 47.032000000000004 - type: ndcg_at_1000 value: 48.199999999999996 - type: ndcg_at_3 value: 33.702 - type: ndcg_at_5 value: 37.522 - type: precision_at_1 value: 23.195 - type: precision_at_10 value: 6.526999999999999 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 14.308000000000002 - type: precision_at_5 value: 10.507 - type: recall_at_1 value: 22.541 - type: recall_at_10 value: 62.524 - type: recall_at_100 value: 88.228 - type: recall_at_1000 value: 97.243 - type: recall_at_3 value: 41.38 - type: recall_at_5 value: 50.55 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.69949840401279 - type: f1 value: 92.54141471311786 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.56041951664386 - type: f1 value: 55.88499977508287 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.62071284465365 - type: f1 value: 69.36717546572152 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.35843981170142 - type: f1 value: 76.15496453538884 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.33664956793118 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.883839621715524 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.096874986740758 - type: mrr value: 30.97300481932132 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.4 - type: map_at_10 value: 11.852 - type: map_at_100 value: 14.758 - type: map_at_1000 value: 16.134 - type: map_at_3 value: 8.558 - type: map_at_5 value: 10.087 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 52.05800000000001 - type: mrr_at_100 value: 52.689 - type: mrr_at_1000 value: 52.742999999999995 - type: mrr_at_3 value: 50.205999999999996 - type: mrr_at_5 value: 51.367 - type: ndcg_at_1 value: 42.57 - type: ndcg_at_10 value: 32.449 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 38.351 - type: ndcg_at_3 value: 37.044 - type: ndcg_at_5 value: 35.275 - type: precision_at_1 value: 44.272 - type: precision_at_10 value: 23.87 - type: precision_at_100 value: 7.625 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 34.365 - type: precision_at_5 value: 30.341 - type: recall_at_1 value: 5.4 - type: recall_at_10 value: 15.943999999999999 - type: recall_at_100 value: 29.805 - type: recall_at_1000 value: 61.695 - type: recall_at_3 value: 9.539 - type: recall_at_5 value: 12.127 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 36.047000000000004 - type: map_at_10 value: 51.6 - type: map_at_100 value: 52.449999999999996 - type: map_at_1000 value: 52.476 - type: map_at_3 value: 47.452 - type: map_at_5 value: 49.964 - type: mrr_at_1 value: 40.382 - type: mrr_at_10 value: 54.273 - type: mrr_at_100 value: 54.859 - type: mrr_at_1000 value: 54.876000000000005 - type: mrr_at_3 value: 51.014 - type: mrr_at_5 value: 52.983999999999995 - type: ndcg_at_1 value: 40.353 - type: ndcg_at_10 value: 59.11300000000001 - type: ndcg_at_100 value: 62.604000000000006 - type: ndcg_at_1000 value: 63.187000000000005 - type: ndcg_at_3 value: 51.513 - type: ndcg_at_5 value: 55.576 - type: precision_at_1 value: 40.353 - type: precision_at_10 value: 9.418 - type: precision_at_100 value: 1.1440000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.078000000000003 - type: precision_at_5 value: 16.250999999999998 - type: recall_at_1 value: 36.047000000000004 - type: recall_at_10 value: 79.22200000000001 - type: recall_at_100 value: 94.23 - type: recall_at_1000 value: 98.51100000000001 - type: recall_at_3 value: 59.678 - type: recall_at_5 value: 68.967 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 68.232 - type: map_at_10 value: 81.674 - type: map_at_100 value: 82.338 - type: map_at_1000 value: 82.36099999999999 - type: map_at_3 value: 78.833 - type: map_at_5 value: 80.58 - type: mrr_at_1 value: 78.64 - type: mrr_at_10 value: 85.164 - type: mrr_at_100 value: 85.317 - type: mrr_at_1000 value: 85.319 - type: mrr_at_3 value: 84.127 - type: mrr_at_5 value: 84.789 - type: ndcg_at_1 value: 78.63 - type: ndcg_at_10 value: 85.711 - type: ndcg_at_100 value: 87.238 - type: ndcg_at_1000 value: 87.444 - type: ndcg_at_3 value: 82.788 - type: ndcg_at_5 value: 84.313 - type: precision_at_1 value: 78.63 - type: precision_at_10 value: 12.977 - type: precision_at_100 value: 1.503 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.113 - type: precision_at_5 value: 23.71 - type: recall_at_1 value: 68.232 - type: recall_at_10 value: 93.30199999999999 - type: recall_at_100 value: 98.799 - type: recall_at_1000 value: 99.885 - type: recall_at_3 value: 84.827 - type: recall_at_5 value: 89.188 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.71879170816294 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 59.65866311751794 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.218 - type: map_at_10 value: 10.337 - type: map_at_100 value: 12.131 - type: map_at_1000 value: 12.411 - type: map_at_3 value: 7.4270000000000005 - type: map_at_5 value: 8.913 - type: mrr_at_1 value: 20.8 - type: mrr_at_10 value: 30.868000000000002 - type: mrr_at_100 value: 31.903 - type: mrr_at_1000 value: 31.972 - type: mrr_at_3 value: 27.367 - type: mrr_at_5 value: 29.372 - type: ndcg_at_1 value: 20.8 - type: ndcg_at_10 value: 17.765 - type: ndcg_at_100 value: 24.914 - type: ndcg_at_1000 value: 30.206 - type: ndcg_at_3 value: 16.64 - type: ndcg_at_5 value: 14.712 - type: precision_at_1 value: 20.8 - type: precision_at_10 value: 9.24 - type: precision_at_100 value: 1.9560000000000002 - type: precision_at_1000 value: 0.32299999999999995 - type: precision_at_3 value: 15.467 - type: precision_at_5 value: 12.94 - type: recall_at_1 value: 4.218 - type: recall_at_10 value: 18.752 - type: recall_at_100 value: 39.7 - type: recall_at_1000 value: 65.57300000000001 - type: recall_at_3 value: 9.428 - type: recall_at_5 value: 13.133000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.04338850207233 - type: cos_sim_spearman value: 78.5054651430423 - type: euclidean_pearson value: 80.30739451228612 - type: euclidean_spearman value: 78.48377464299097 - type: manhattan_pearson value: 80.40795049052781 - type: manhattan_spearman value: 78.49506205443114 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.11596224442962 - type: cos_sim_spearman value: 76.20997388935461 - type: euclidean_pearson value: 80.56858451349109 - type: euclidean_spearman value: 75.92659183871186 - type: manhattan_pearson value: 80.60246102203844 - type: manhattan_spearman value: 76.03018971432664 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.34691640755737 - type: cos_sim_spearman value: 82.4018369631579 - type: euclidean_pearson value: 81.87673092245366 - type: euclidean_spearman value: 82.3671489960678 - type: manhattan_pearson value: 81.88222387719948 - type: manhattan_spearman value: 82.3816590344736 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.2836092579524 - type: cos_sim_spearman value: 78.99982781772064 - type: euclidean_pearson value: 80.5184271010527 - type: euclidean_spearman value: 78.89777392101904 - type: manhattan_pearson value: 80.53585705018664 - type: manhattan_spearman value: 78.92898405472994 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.7349907750784 - type: cos_sim_spearman value: 87.7611234446225 - type: euclidean_pearson value: 86.98759326731624 - type: euclidean_spearman value: 87.58321319424618 - type: manhattan_pearson value: 87.03483090370842 - type: manhattan_spearman value: 87.63278333060288 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.75873694924825 - type: cos_sim_spearman value: 83.80237999094724 - type: euclidean_pearson value: 83.55023725861537 - type: euclidean_spearman value: 84.12744338577744 - type: manhattan_pearson value: 83.58816983036232 - type: manhattan_spearman value: 84.18520748676501 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.21630882940174 - type: cos_sim_spearman value: 87.72382883437031 - type: euclidean_pearson value: 88.69933350930333 - type: euclidean_spearman value: 88.24660814383081 - type: manhattan_pearson value: 88.77331018833499 - type: manhattan_spearman value: 88.26109989380632 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 61.11854063060489 - type: cos_sim_spearman value: 63.14678634195072 - type: euclidean_pearson value: 61.679090067000864 - type: euclidean_spearman value: 62.28876589509653 - type: manhattan_pearson value: 62.082324165511004 - type: manhattan_spearman value: 62.56030932816679 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.00319882832645 - type: cos_sim_spearman value: 85.94529772647257 - type: euclidean_pearson value: 85.6661390122756 - type: euclidean_spearman value: 85.97747815545827 - type: manhattan_pearson value: 85.58422770541893 - type: manhattan_spearman value: 85.9237139181532 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.16198731863916 - type: mrr value: 94.25202702163487 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 54.761 - type: map_at_10 value: 64.396 - type: map_at_100 value: 65.07 - type: map_at_1000 value: 65.09899999999999 - type: map_at_3 value: 61.846000000000004 - type: map_at_5 value: 63.284 - type: mrr_at_1 value: 57.667 - type: mrr_at_10 value: 65.83099999999999 - type: mrr_at_100 value: 66.36800000000001 - type: mrr_at_1000 value: 66.39399999999999 - type: mrr_at_3 value: 64.056 - type: mrr_at_5 value: 65.206 - type: ndcg_at_1 value: 57.667 - type: ndcg_at_10 value: 68.854 - type: ndcg_at_100 value: 71.59100000000001 - type: ndcg_at_1000 value: 72.383 - type: ndcg_at_3 value: 64.671 - type: ndcg_at_5 value: 66.796 - type: precision_at_1 value: 57.667 - type: precision_at_10 value: 9.167 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 16.667 - type: recall_at_1 value: 54.761 - type: recall_at_10 value: 80.9 - type: recall_at_100 value: 92.767 - type: recall_at_1000 value: 99 - type: recall_at_3 value: 69.672 - type: recall_at_5 value: 75.083 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8079207920792 - type: cos_sim_ap value: 94.88470927617445 - type: cos_sim_f1 value: 90.08179959100204 - type: cos_sim_precision value: 92.15481171548117 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.58613861386138 - type: dot_ap value: 82.94822578881316 - type: dot_f1 value: 77.33333333333333 - type: dot_precision value: 79.36842105263158 - type: dot_recall value: 75.4 - type: euclidean_accuracy value: 99.8069306930693 - type: euclidean_ap value: 94.81367858031837 - type: euclidean_f1 value: 90.01009081735621 - type: euclidean_precision value: 90.83503054989816 - type: euclidean_recall value: 89.2 - type: manhattan_accuracy value: 99.81188118811882 - type: manhattan_ap value: 94.91405337220161 - type: manhattan_f1 value: 90.2763561924258 - type: manhattan_precision value: 92.45283018867924 - type: manhattan_recall value: 88.2 - type: max_accuracy value: 99.81188118811882 - type: max_ap value: 94.91405337220161 - type: max_f1 value: 90.2763561924258 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 58.511599500053094 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 31.984728147814707 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.93428193939015 - type: mrr value: 50.916557911043206 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.562500894537145 - type: cos_sim_spearman value: 31.162587976726307 - type: dot_pearson value: 22.633662187735762 - type: dot_spearman value: 22.723000282378962 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.219 - type: map_at_10 value: 1.871 - type: map_at_100 value: 10.487 - type: map_at_1000 value: 25.122 - type: map_at_3 value: 0.657 - type: map_at_5 value: 1.0699999999999998 - type: mrr_at_1 value: 84 - type: mrr_at_10 value: 89.567 - type: mrr_at_100 value: 89.748 - type: mrr_at_1000 value: 89.748 - type: mrr_at_3 value: 88.667 - type: mrr_at_5 value: 89.567 - type: ndcg_at_1 value: 80 - type: ndcg_at_10 value: 74.533 - type: ndcg_at_100 value: 55.839000000000006 - type: ndcg_at_1000 value: 49.748 - type: ndcg_at_3 value: 79.53099999999999 - type: ndcg_at_5 value: 78.245 - type: precision_at_1 value: 84 - type: precision_at_10 value: 78.4 - type: precision_at_100 value: 56.99999999999999 - type: precision_at_1000 value: 21.98 - type: precision_at_3 value: 85.333 - type: precision_at_5 value: 84.8 - type: recall_at_1 value: 0.219 - type: recall_at_10 value: 2.02 - type: recall_at_100 value: 13.555 - type: recall_at_1000 value: 46.739999999999995 - type: recall_at_3 value: 0.685 - type: recall_at_5 value: 1.13 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.5029999999999997 - type: map_at_10 value: 11.042 - type: map_at_100 value: 16.326999999999998 - type: map_at_1000 value: 17.836 - type: map_at_3 value: 6.174 - type: map_at_5 value: 7.979 - type: mrr_at_1 value: 42.857 - type: mrr_at_10 value: 52.617000000000004 - type: mrr_at_100 value: 53.351000000000006 - type: mrr_at_1000 value: 53.351000000000006 - type: mrr_at_3 value: 46.939 - type: mrr_at_5 value: 50.714000000000006 - type: ndcg_at_1 value: 38.775999999999996 - type: ndcg_at_10 value: 27.125 - type: ndcg_at_100 value: 35.845 - type: ndcg_at_1000 value: 47.377 - type: ndcg_at_3 value: 29.633 - type: ndcg_at_5 value: 28.378999999999998 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 24.082 - type: precision_at_100 value: 6.877999999999999 - type: precision_at_1000 value: 1.463 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 28.571 - type: recall_at_1 value: 3.5029999999999997 - type: recall_at_10 value: 17.068 - type: recall_at_100 value: 43.361 - type: recall_at_1000 value: 78.835 - type: recall_at_3 value: 6.821000000000001 - type: recall_at_5 value: 10.357 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.0954 - type: ap value: 14.216844153511959 - type: f1 value: 54.63687418565117 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.46293152235427 - type: f1 value: 61.744177921638645 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.12708617788644 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.34252536948081 - type: cos_sim_f1 value: 67.53758935173774 - type: cos_sim_precision value: 63.3672525439408 - type: cos_sim_recall value: 72.29551451187335 - type: dot_accuracy value: 81.71305954580676 - type: dot_ap value: 59.5532209082386 - type: dot_f1 value: 56.18466898954705 - type: dot_precision value: 47.830923248053395 - type: dot_recall value: 68.07387862796834 - type: euclidean_accuracy value: 85.81987244441795 - type: euclidean_ap value: 73.34325409809446 - type: euclidean_f1 value: 67.83451360417443 - type: euclidean_precision value: 64.09955388588871 - type: euclidean_recall value: 72.0316622691293 - type: manhattan_accuracy value: 85.68277999642368 - type: manhattan_ap value: 73.1535450121903 - type: manhattan_f1 value: 67.928237896289 - type: manhattan_precision value: 63.56945722171113 - type: manhattan_recall value: 72.9287598944591 - type: max_accuracy value: 85.81987244441795 - type: max_ap value: 73.34325409809446 - type: max_f1 value: 67.928237896289 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.90441262079403 - type: cos_sim_ap value: 85.79331880741438 - type: cos_sim_f1 value: 78.31563529842548 - type: cos_sim_precision value: 74.6683424102779 - type: cos_sim_recall value: 82.33754234678165 - type: dot_accuracy value: 84.89928978926534 - type: dot_ap value: 75.25819218316 - type: dot_f1 value: 69.88730119720536 - type: dot_precision value: 64.23362374959665 - type: dot_recall value: 76.63227594702803 - type: euclidean_accuracy value: 89.01695967710637 - type: euclidean_ap value: 85.98986606038852 - type: euclidean_f1 value: 78.5277880014722 - type: euclidean_precision value: 75.22211253701876 - type: euclidean_recall value: 82.13735756082538 - type: manhattan_accuracy value: 88.99561454573679 - type: manhattan_ap value: 85.92262421793953 - type: manhattan_f1 value: 78.38866094740769 - type: manhattan_precision value: 76.02373028505282 - type: manhattan_recall value: 80.9054511857099 - type: max_accuracy value: 89.01695967710637 - type: max_ap value: 85.98986606038852 - type: max_f1 value: 78.5277880014722 language: - en license: mit --- # E5-small-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2') model = AutoModel.from_pretrained('intfloat/e5-small-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/e5-small-v2') input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens.

Salesforce/blip-image-captioning-base

Salesforce

transformers

image-to-text

--- pipeline_tag: image-to-text tags: - image-captioning languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone). |  | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

meta-llama/Llama-2-7b-chat-hf

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. 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You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. 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Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. 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Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

facebook/encodec_24khz

facebook

transformers

feature-extraction

--- inference: false ---  # Model Card for EnCodec This model card provides details and information about EnCodec, a state-of-the-art real-time audio codec developed by Meta AI. ## Model Details ### Model Description EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. - **Developed by:** Meta AI - **Model type:** Audio Codec ### Model Sources - **Repository:** [GitHub Repository](https://github.com/facebookresearch/encodec) - **Paper:** [EnCodec: End-to-End Neural Audio Codec](https://arxiv.org/abs/2210.13438) ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> EnCodec can be used directly as an audio codec for real-time compression and decompression of audio signals. It provides high-quality audio compression and efficient decoding. The model was trained on various bandwiths, which can be specified when encoding (compressing) and decoding (decompressing). Two different setup exist for EnCodec: - Non-streamable: the input audio is split into chunks of 1 seconds, with an overlap of 10 ms, which are then encoded. - Streamable: weight normalizationis used on the convolution layers, and the input is not split into chunks but rather padded on the left. ### Downstream Use EnCodec can be fine-tuned for specific audio tasks or integrated into larger audio processing pipelines for applications such as speech generation, music generation, or text to speech tasks. <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## How to Get Started with the Model Use the following code to get started with the EnCodec model using a dummy example from the LibriSpeech dataset (~9MB). First, install the required Python packages: ``` pip install --upgrade pip pip install --upgrade datasets[audio] pip install git+https://github.com/huggingface/transformers.git@main ``` Then load an audio sample, and run a forward pass of the model: ```python from datasets import load_dataset, Audio from transformers import EncodecModel, AutoProcessor # load a demonstration datasets librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # load the model + processor (for pre-processing the audio) model = EncodecModel.from_pretrained("facebook/encodec_24khz") processor = AutoProcessor.from_pretrained("facebook/encodec_24khz") # cast the audio data to the correct sampling rate for the model librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[0]["audio"]["array"] # pre-process the inputs inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt") # explicitly encode then decode the audio inputs encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"]) audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0] # or the equivalent with a forward pass audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values ``` ## Training Details The model was trained for 300 epochs, with one epoch being 2,000 updates with the Adam optimizer with a batch size of 64 examples of 1 second each, a learning rate of 3 · 10−4 , β1 = 0.5, and β2 = 0.9. All the models are traind using 8 A100 GPUs. ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> - For speech: - DNS Challenge 4 - [Common Voice](https://huggingface.co/datasets/common_voice) - For general audio: - [AudioSet](https://huggingface.co/datasets/Fhrozen/AudioSet2K22) - [FSD50K](https://huggingface.co/datasets/Fhrozen/FSD50k) - For music: - [Jamendo dataset](https://huggingface.co/datasets/rkstgr/mtg-jamendo) They used four different training strategies to sample for these datasets: - (s1) sample a single source from Jamendo with probability 0.32; - (s2) sample a single source from the other datasets with the same probability; - (s3) mix two sources from all datasets with a probability of 0.24; - (s4) mix three sources from all datasets except music with a probability of 0.12. The audio is normalized by file and a random gain between -10 and 6 dB id applied. ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Subjectif metric for restoration: This models was evalutated using the MUSHRA protocol (Series, 2014), using both a hidden reference and a low anchor. Annotators were recruited using a crowd-sourcing platform, in which they were asked to rate the perceptual quality of the provided samples in a range between 1 to 100. They randomly select 50 samples of 5 seconds from each category of the the test set and force at least 10 annotations per samples. To filter noisy annotations and outliers we remove annotators who rate the reference recordings less then 90 in at least 20% of the cases, or rate the low-anchor recording above 80 more than 50% of the time. ### Objective metric for restoration: The ViSQOL()ink) metric was used together with the Scale-Invariant Signal-to-Noise Ration (SI-SNR) (Luo & Mesgarani, 2019; Nachmani et al., 2020; Chazan et al., 2021). ### Results The results of the evaluation demonstrate the superiority of EnCodec compared to the baselines across different bandwidths (1.5, 3, 6, and 12 kbps). When comparing EnCodec with the baselines at the same bandwidth, EnCodec consistently outperforms them in terms of MUSHRA score. Notably, EnCodec achieves better performance, on average, at 3 kbps compared to Lyra-v2 at 6 kbps and Opus at 12 kbps. Additionally, by incorporating the language model over the codes, it is possible to achieve a bandwidth reduction of approximately 25-40%. For example, the bandwidth of the 3 kbps model can be reduced to 1.9 kbps. #### Summary EnCodec is a state-of-the-art real-time neural audio compression model that excels in producing high-fidelity audio samples at various sample rates and bandwidths. The model's performance was evaluated across different settings, ranging from 24kHz monophonic at 1.5 kbps to 48kHz stereophonic, showcasing both subjective and objective results. Notably, EnCodec incorporates a novel spectrogram-only adversarial loss, effectively reducing artifacts and enhancing sample quality. Training stability and interpretability were further enhanced through the introduction of a gradient balancer for the loss weights. Additionally, the study demonstrated that a compact Transformer model can be employed to achieve an additional bandwidth reduction of up to 40% without compromising quality, particularly in applications where low latency is not critical (e.g., music streaming). ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @misc{défossez2022high, title={High Fidelity Neural Audio Compression}, author={Alexandre Défossez and Jade Copet and Gabriel Synnaeve and Yossi Adi}, year={2022}, eprint={2210.13438}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

BAAI/bge-base-en-v1.5

BAAI

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-base-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.14925373134328 - type: ap value: 39.32336517995478 - type: f1 value: 70.16902252611425 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.386825 - type: ap value: 90.21276917991995 - type: f1 value: 93.37741030006174 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.846000000000004 - type: f1 value: 48.14646269778261 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 40.754000000000005 - type: map_at_10 value: 55.761 - type: map_at_100 value: 56.330999999999996 - type: map_at_1000 value: 56.333999999999996 - type: map_at_3 value: 51.92 - type: map_at_5 value: 54.010999999999996 - type: mrr_at_1 value: 41.181 - type: mrr_at_10 value: 55.967999999999996 - type: mrr_at_100 value: 56.538 - type: mrr_at_1000 value: 56.542 - type: mrr_at_3 value: 51.980000000000004 - type: mrr_at_5 value: 54.208999999999996 - type: ndcg_at_1 value: 40.754000000000005 - type: ndcg_at_10 value: 63.605000000000004 - type: ndcg_at_100 value: 66.05199999999999 - type: ndcg_at_1000 value: 66.12 - type: ndcg_at_3 value: 55.708 - type: ndcg_at_5 value: 59.452000000000005 - type: precision_at_1 value: 40.754000000000005 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.238 - type: precision_at_5 value: 15.149000000000001 - type: recall_at_1 value: 40.754000000000005 - type: recall_at_10 value: 88.407 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.714 - type: recall_at_5 value: 75.747 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.74884539679369 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.8075893810716 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.128470519187736 - type: mrr value: 74.28065778481289 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.24629081484655 - type: cos_sim_spearman value: 86.93752309911496 - type: euclidean_pearson value: 87.58589628573816 - type: euclidean_spearman value: 88.05622328825284 - type: manhattan_pearson value: 87.5594959805773 - type: manhattan_spearman value: 88.19658793233961 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.9512987012987 - type: f1 value: 86.92515357973708 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10263762928872 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.69711517426737 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.327 - type: map_at_10 value: 44.099 - type: map_at_100 value: 45.525 - type: map_at_1000 value: 45.641999999999996 - type: map_at_3 value: 40.47 - type: map_at_5 value: 42.36 - type: mrr_at_1 value: 39.199 - type: mrr_at_10 value: 49.651 - type: mrr_at_100 value: 50.29 - type: mrr_at_1000 value: 50.329 - type: mrr_at_3 value: 46.924 - type: mrr_at_5 value: 48.548 - type: ndcg_at_1 value: 39.199 - type: ndcg_at_10 value: 50.773 - type: ndcg_at_100 value: 55.67999999999999 - type: ndcg_at_1000 value: 57.495 - type: ndcg_at_3 value: 45.513999999999996 - type: ndcg_at_5 value: 47.703 - type: precision_at_1 value: 39.199 - type: precision_at_10 value: 9.914000000000001 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.984 - type: precision_at_5 value: 15.737000000000002 - type: recall_at_1 value: 32.327 - type: recall_at_10 value: 63.743 - type: recall_at_100 value: 84.538 - type: recall_at_1000 value: 96.089 - type: recall_at_3 value: 48.065000000000005 - type: recall_at_5 value: 54.519 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.671 - type: map_at_10 value: 42.954 - type: map_at_100 value: 44.151 - type: map_at_1000 value: 44.287 - type: map_at_3 value: 39.912 - type: map_at_5 value: 41.798 - type: mrr_at_1 value: 41.465 - type: mrr_at_10 value: 49.351 - type: mrr_at_100 value: 49.980000000000004 - type: mrr_at_1000 value: 50.016000000000005 - type: mrr_at_3 value: 47.144000000000005 - type: mrr_at_5 value: 48.592999999999996 - type: ndcg_at_1 value: 41.465 - type: ndcg_at_10 value: 48.565999999999995 - type: ndcg_at_100 value: 52.76499999999999 - type: ndcg_at_1000 value: 54.749 - type: ndcg_at_3 value: 44.57 - type: ndcg_at_5 value: 46.759 - type: precision_at_1 value: 41.465 - type: precision_at_10 value: 9.107999999999999 - type: precision_at_100 value: 1.433 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 21.423000000000002 - type: precision_at_5 value: 15.414 - type: recall_at_1 value: 32.671 - type: recall_at_10 value: 57.738 - type: recall_at_100 value: 75.86500000000001 - type: recall_at_1000 value: 88.36 - type: recall_at_3 value: 45.626 - type: recall_at_5 value: 51.812000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 41.185 - type: map_at_10 value: 53.929 - type: map_at_100 value: 54.92 - type: map_at_1000 value: 54.967999999999996 - type: map_at_3 value: 50.70400000000001 - type: map_at_5 value: 52.673 - type: mrr_at_1 value: 47.398 - type: mrr_at_10 value: 57.303000000000004 - type: mrr_at_100 value: 57.959 - type: mrr_at_1000 value: 57.985 - type: mrr_at_3 value: 54.932 - type: mrr_at_5 value: 56.464999999999996 - type: ndcg_at_1 value: 47.398 - type: ndcg_at_10 value: 59.653 - type: ndcg_at_100 value: 63.627 - type: ndcg_at_1000 value: 64.596 - type: ndcg_at_3 value: 54.455 - type: ndcg_at_5 value: 57.245000000000005 - type: precision_at_1 value: 47.398 - type: precision_at_10 value: 9.524000000000001 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.389 - type: precision_at_5 value: 16.752 - type: recall_at_1 value: 41.185 - type: recall_at_10 value: 73.193 - type: recall_at_100 value: 90.357 - type: recall_at_1000 value: 97.253 - type: recall_at_3 value: 59.199999999999996 - type: recall_at_5 value: 66.118 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.27 - type: map_at_10 value: 36.223 - type: map_at_100 value: 37.218 - type: map_at_1000 value: 37.293 - type: map_at_3 value: 33.503 - type: map_at_5 value: 35.097 - type: mrr_at_1 value: 29.492 - type: mrr_at_10 value: 38.352000000000004 - type: mrr_at_100 value: 39.188 - type: mrr_at_1000 value: 39.247 - type: mrr_at_3 value: 35.876000000000005 - type: mrr_at_5 value: 37.401 - type: ndcg_at_1 value: 29.492 - type: ndcg_at_10 value: 41.239 - type: ndcg_at_100 value: 46.066 - type: ndcg_at_1000 value: 47.992000000000004 - type: ndcg_at_3 value: 36.11 - type: ndcg_at_5 value: 38.772 - type: precision_at_1 value: 29.492 - type: precision_at_10 value: 6.260000000000001 - type: precision_at_100 value: 0.914 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 15.104000000000001 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 27.27 - type: recall_at_10 value: 54.589 - type: recall_at_100 value: 76.70700000000001 - type: recall_at_1000 value: 91.158 - type: recall_at_3 value: 40.974 - type: recall_at_5 value: 47.327000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.848 - type: map_at_10 value: 26.207 - type: map_at_100 value: 27.478 - type: map_at_1000 value: 27.602 - type: map_at_3 value: 23.405 - type: map_at_5 value: 24.98 - type: mrr_at_1 value: 21.891 - type: mrr_at_10 value: 31.041999999999998 - type: mrr_at_100 value: 32.092 - type: mrr_at_1000 value: 32.151999999999994 - type: mrr_at_3 value: 28.358 - type: mrr_at_5 value: 29.969 - type: ndcg_at_1 value: 21.891 - type: ndcg_at_10 value: 31.585 - type: ndcg_at_100 value: 37.531 - type: ndcg_at_1000 value: 40.256 - type: ndcg_at_3 value: 26.508 - type: ndcg_at_5 value: 28.894 - type: precision_at_1 value: 21.891 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.769 - type: precision_at_5 value: 9.279 - type: recall_at_1 value: 17.848 - type: recall_at_10 value: 43.452 - type: recall_at_100 value: 69.216 - type: recall_at_1000 value: 88.102 - type: recall_at_3 value: 29.18 - type: recall_at_5 value: 35.347 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.94 - type: map_at_10 value: 41.248000000000005 - type: map_at_100 value: 42.495 - type: map_at_1000 value: 42.602000000000004 - type: map_at_3 value: 37.939 - type: map_at_5 value: 39.924 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.041 - type: mrr_at_100 value: 47.83 - type: mrr_at_1000 value: 47.878 - type: mrr_at_3 value: 44.466 - type: mrr_at_5 value: 46.111999999999995 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 47.223 - type: ndcg_at_100 value: 52.394 - type: ndcg_at_1000 value: 54.432 - type: ndcg_at_3 value: 42.032000000000004 - type: ndcg_at_5 value: 44.772 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.393 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 19.698 - type: precision_at_5 value: 14.013 - type: recall_at_1 value: 30.94 - type: recall_at_10 value: 59.316 - type: recall_at_100 value: 80.783 - type: recall_at_1000 value: 94.15400000000001 - type: recall_at_3 value: 44.712 - type: recall_at_5 value: 51.932 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.104 - type: map_at_10 value: 36.675999999999995 - type: map_at_100 value: 38.076 - type: map_at_1000 value: 38.189 - type: map_at_3 value: 33.733999999999995 - type: map_at_5 value: 35.287 - type: mrr_at_1 value: 33.904 - type: mrr_at_10 value: 42.55 - type: mrr_at_100 value: 43.434 - type: mrr_at_1000 value: 43.494 - type: mrr_at_3 value: 40.126 - type: mrr_at_5 value: 41.473 - type: ndcg_at_1 value: 33.904 - type: ndcg_at_10 value: 42.414 - type: ndcg_at_100 value: 48.203 - type: ndcg_at_1000 value: 50.437 - type: ndcg_at_3 value: 37.633 - type: ndcg_at_5 value: 39.67 - type: precision_at_1 value: 33.904 - type: precision_at_10 value: 7.82 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 17.884 - type: precision_at_5 value: 12.648000000000001 - type: recall_at_1 value: 27.104 - type: recall_at_10 value: 53.563 - type: recall_at_100 value: 78.557 - type: recall_at_1000 value: 93.533 - type: recall_at_3 value: 39.92 - type: recall_at_5 value: 45.457 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.707749999999997 - type: map_at_10 value: 36.961 - type: map_at_100 value: 38.158833333333334 - type: map_at_1000 value: 38.270333333333326 - type: map_at_3 value: 34.07183333333334 - type: map_at_5 value: 35.69533333333334 - type: mrr_at_1 value: 32.81875 - type: mrr_at_10 value: 41.293 - type: mrr_at_100 value: 42.116499999999995 - type: mrr_at_1000 value: 42.170249999999996 - type: mrr_at_3 value: 38.83983333333333 - type: mrr_at_5 value: 40.29775 - type: ndcg_at_1 value: 32.81875 - type: ndcg_at_10 value: 42.355 - type: ndcg_at_100 value: 47.41374999999999 - type: ndcg_at_1000 value: 49.5805 - type: ndcg_at_3 value: 37.52825 - type: ndcg_at_5 value: 39.83266666666667 - type: precision_at_1 value: 32.81875 - type: precision_at_10 value: 7.382416666666666 - type: precision_at_100 value: 1.1640833333333334 - type: precision_at_1000 value: 0.15383333333333335 - type: precision_at_3 value: 17.134166666666665 - type: precision_at_5 value: 12.174833333333336 - type: recall_at_1 value: 27.707749999999997 - type: recall_at_10 value: 53.945 - type: recall_at_100 value: 76.191 - type: recall_at_1000 value: 91.101 - type: recall_at_3 value: 40.39083333333334 - type: recall_at_5 value: 46.40083333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.482 - type: map_at_10 value: 33.201 - type: map_at_100 value: 34.107 - type: map_at_1000 value: 34.197 - type: map_at_3 value: 31.174000000000003 - type: map_at_5 value: 32.279 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 36.235 - type: mrr_at_100 value: 37.04 - type: mrr_at_1000 value: 37.105 - type: mrr_at_3 value: 34.355999999999995 - type: mrr_at_5 value: 35.382999999999996 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 37.325 - type: ndcg_at_100 value: 41.795 - type: ndcg_at_1000 value: 44.105 - type: ndcg_at_3 value: 33.555 - type: ndcg_at_5 value: 35.266999999999996 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.721 - type: precision_at_100 value: 0.8630000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 14.008000000000001 - type: precision_at_5 value: 9.754999999999999 - type: recall_at_1 value: 26.482 - type: recall_at_10 value: 47.072 - type: recall_at_100 value: 67.27 - type: recall_at_1000 value: 84.371 - type: recall_at_3 value: 36.65 - type: recall_at_5 value: 40.774 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.815 - type: map_at_10 value: 26.369999999999997 - type: map_at_100 value: 27.458 - type: map_at_1000 value: 27.588 - type: map_at_3 value: 23.990000000000002 - type: map_at_5 value: 25.345000000000002 - type: mrr_at_1 value: 22.953000000000003 - type: mrr_at_10 value: 30.342999999999996 - type: mrr_at_100 value: 31.241000000000003 - type: mrr_at_1000 value: 31.319000000000003 - type: mrr_at_3 value: 28.16 - type: mrr_at_5 value: 29.406 - type: ndcg_at_1 value: 22.953000000000003 - type: ndcg_at_10 value: 31.151 - type: ndcg_at_100 value: 36.309000000000005 - type: ndcg_at_1000 value: 39.227000000000004 - type: ndcg_at_3 value: 26.921 - type: ndcg_at_5 value: 28.938000000000002 - type: precision_at_1 value: 22.953000000000003 - type: precision_at_10 value: 5.602 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.606 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 18.815 - type: recall_at_10 value: 41.574 - type: recall_at_100 value: 64.84400000000001 - type: recall_at_1000 value: 85.406 - type: recall_at_3 value: 29.694 - type: recall_at_5 value: 34.935 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.840999999999998 - type: map_at_10 value: 36.797999999999995 - type: map_at_100 value: 37.993 - type: map_at_1000 value: 38.086999999999996 - type: map_at_3 value: 34.050999999999995 - type: map_at_5 value: 35.379 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.025 - type: mrr_at_100 value: 41.878 - type: mrr_at_1000 value: 41.929 - type: mrr_at_3 value: 38.573 - type: mrr_at_5 value: 39.715 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.142 - type: ndcg_at_100 value: 47.558 - type: ndcg_at_1000 value: 49.643 - type: ndcg_at_3 value: 37.12 - type: ndcg_at_5 value: 38.983000000000004 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.08 - type: precision_at_100 value: 1.1039999999999999 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.698 - type: precision_at_5 value: 11.511000000000001 - type: recall_at_1 value: 27.840999999999998 - type: recall_at_10 value: 54.245 - type: recall_at_100 value: 77.947 - type: recall_at_1000 value: 92.36999999999999 - type: recall_at_3 value: 40.146 - type: recall_at_5 value: 44.951 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.529000000000003 - type: map_at_10 value: 35.010000000000005 - type: map_at_100 value: 36.647 - type: map_at_1000 value: 36.857 - type: map_at_3 value: 31.968000000000004 - type: map_at_5 value: 33.554 - type: mrr_at_1 value: 31.818 - type: mrr_at_10 value: 39.550999999999995 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.596 - type: mrr_at_3 value: 36.726 - type: mrr_at_5 value: 38.416 - type: ndcg_at_1 value: 31.818 - type: ndcg_at_10 value: 40.675 - type: ndcg_at_100 value: 46.548 - type: ndcg_at_1000 value: 49.126 - type: ndcg_at_3 value: 35.829 - type: ndcg_at_5 value: 38.0 - type: precision_at_1 value: 31.818 - type: precision_at_10 value: 7.826 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.601 - type: precision_at_5 value: 12.095 - type: recall_at_1 value: 26.529000000000003 - type: recall_at_10 value: 51.03 - type: recall_at_100 value: 77.556 - type: recall_at_1000 value: 93.804 - type: recall_at_3 value: 36.986000000000004 - type: recall_at_5 value: 43.096000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.480999999999998 - type: map_at_10 value: 30.817 - type: map_at_100 value: 31.838 - type: map_at_1000 value: 31.932 - type: map_at_3 value: 28.011999999999997 - type: map_at_5 value: 29.668 - type: mrr_at_1 value: 25.323 - type: mrr_at_10 value: 33.072 - type: mrr_at_100 value: 33.926 - type: mrr_at_1000 value: 33.993 - type: mrr_at_3 value: 30.436999999999998 - type: mrr_at_5 value: 32.092 - type: ndcg_at_1 value: 25.323 - type: ndcg_at_10 value: 35.514 - type: ndcg_at_100 value: 40.489000000000004 - type: ndcg_at_1000 value: 42.908 - type: ndcg_at_3 value: 30.092000000000002 - type: ndcg_at_5 value: 32.989000000000004 - type: precision_at_1 value: 25.323 - type: precision_at_10 value: 5.545 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.446 - type: precision_at_5 value: 9.131 - type: recall_at_1 value: 23.480999999999998 - type: recall_at_10 value: 47.825 - type: recall_at_100 value: 70.652 - type: recall_at_1000 value: 88.612 - type: recall_at_3 value: 33.537 - type: recall_at_5 value: 40.542 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.333999999999998 - type: map_at_10 value: 22.524 - type: map_at_100 value: 24.506 - type: map_at_1000 value: 24.715 - type: map_at_3 value: 19.022 - type: map_at_5 value: 20.693 - type: mrr_at_1 value: 29.186 - type: mrr_at_10 value: 41.22 - type: mrr_at_100 value: 42.16 - type: mrr_at_1000 value: 42.192 - type: mrr_at_3 value: 38.013000000000005 - type: mrr_at_5 value: 39.704 - type: ndcg_at_1 value: 29.186 - type: ndcg_at_10 value: 31.167 - type: ndcg_at_100 value: 38.879000000000005 - type: ndcg_at_1000 value: 42.376000000000005 - type: ndcg_at_3 value: 25.817 - type: ndcg_at_5 value: 27.377000000000002 - type: precision_at_1 value: 29.186 - type: precision_at_10 value: 9.693999999999999 - type: precision_at_100 value: 1.8030000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 19.11 - type: precision_at_5 value: 14.344999999999999 - type: recall_at_1 value: 13.333999999999998 - type: recall_at_10 value: 37.092000000000006 - type: recall_at_100 value: 63.651 - type: recall_at_1000 value: 83.05 - type: recall_at_3 value: 23.74 - type: recall_at_5 value: 28.655 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.151 - type: map_at_10 value: 19.653000000000002 - type: map_at_100 value: 28.053 - type: map_at_1000 value: 29.709000000000003 - type: map_at_3 value: 14.191 - type: map_at_5 value: 16.456 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.4 - type: mrr_at_100 value: 74.715 - type: mrr_at_1000 value: 74.726 - type: mrr_at_3 value: 72.417 - type: mrr_at_5 value: 73.667 - type: ndcg_at_1 value: 54.25 - type: ndcg_at_10 value: 40.77 - type: ndcg_at_100 value: 46.359 - type: ndcg_at_1000 value: 54.193000000000005 - type: ndcg_at_3 value: 44.832 - type: ndcg_at_5 value: 42.63 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 32.175 - type: precision_at_100 value: 10.668 - type: precision_at_1000 value: 2.067 - type: precision_at_3 value: 47.667 - type: precision_at_5 value: 41.3 - type: recall_at_1 value: 9.151 - type: recall_at_10 value: 25.003999999999998 - type: recall_at_100 value: 52.976 - type: recall_at_1000 value: 78.315 - type: recall_at_3 value: 15.487 - type: recall_at_5 value: 18.999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.89999999999999 - type: f1 value: 46.47777925067403 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 73.706 - type: map_at_10 value: 82.423 - type: map_at_100 value: 82.67999999999999 - type: map_at_1000 value: 82.694 - type: map_at_3 value: 81.328 - type: map_at_5 value: 82.001 - type: mrr_at_1 value: 79.613 - type: mrr_at_10 value: 87.07000000000001 - type: mrr_at_100 value: 87.169 - type: mrr_at_1000 value: 87.17 - type: mrr_at_3 value: 86.404 - type: mrr_at_5 value: 86.856 - type: ndcg_at_1 value: 79.613 - type: ndcg_at_10 value: 86.289 - type: ndcg_at_100 value: 87.201 - type: ndcg_at_1000 value: 87.428 - type: ndcg_at_3 value: 84.625 - type: ndcg_at_5 value: 85.53699999999999 - type: precision_at_1 value: 79.613 - type: precision_at_10 value: 10.399 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.473 - type: precision_at_5 value: 20.132 - type: recall_at_1 value: 73.706 - type: recall_at_10 value: 93.559 - type: recall_at_100 value: 97.188 - type: recall_at_1000 value: 98.555 - type: recall_at_3 value: 88.98700000000001 - type: recall_at_5 value: 91.373 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.841 - type: map_at_10 value: 32.643 - type: map_at_100 value: 34.575 - type: map_at_1000 value: 34.736 - type: map_at_3 value: 28.317999999999998 - type: map_at_5 value: 30.964000000000002 - type: mrr_at_1 value: 39.660000000000004 - type: mrr_at_10 value: 48.620000000000005 - type: mrr_at_100 value: 49.384 - type: mrr_at_1000 value: 49.415 - type: mrr_at_3 value: 45.988 - type: mrr_at_5 value: 47.361 - type: ndcg_at_1 value: 39.660000000000004 - type: ndcg_at_10 value: 40.646 - type: ndcg_at_100 value: 47.657 - type: ndcg_at_1000 value: 50.428 - type: ndcg_at_3 value: 36.689 - type: ndcg_at_5 value: 38.211 - type: precision_at_1 value: 39.660000000000004 - type: precision_at_10 value: 11.235000000000001 - type: precision_at_100 value: 1.8530000000000002 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.587999999999997 - type: precision_at_5 value: 18.395 - type: recall_at_1 value: 19.841 - type: recall_at_10 value: 48.135 - type: recall_at_100 value: 74.224 - type: recall_at_1000 value: 90.826 - type: recall_at_3 value: 33.536 - type: recall_at_5 value: 40.311 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.358 - type: map_at_10 value: 64.497 - type: map_at_100 value: 65.362 - type: map_at_1000 value: 65.41900000000001 - type: map_at_3 value: 61.06700000000001 - type: map_at_5 value: 63.317 - type: mrr_at_1 value: 80.716 - type: mrr_at_10 value: 86.10799999999999 - type: mrr_at_100 value: 86.265 - type: mrr_at_1000 value: 86.27 - type: mrr_at_3 value: 85.271 - type: mrr_at_5 value: 85.82499999999999 - type: ndcg_at_1 value: 80.716 - type: ndcg_at_10 value: 72.597 - type: ndcg_at_100 value: 75.549 - type: ndcg_at_1000 value: 76.61 - type: ndcg_at_3 value: 67.874 - type: ndcg_at_5 value: 70.655 - type: precision_at_1 value: 80.716 - type: precision_at_10 value: 15.148 - type: precision_at_100 value: 1.745 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 43.597 - type: precision_at_5 value: 28.351 - type: recall_at_1 value: 40.358 - type: recall_at_10 value: 75.739 - type: recall_at_100 value: 87.259 - type: recall_at_1000 value: 94.234 - type: recall_at_3 value: 65.39500000000001 - type: recall_at_5 value: 70.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.80799999999998 - type: ap value: 86.81350378180757 - type: f1 value: 90.79901248314215 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.096 - type: map_at_10 value: 34.384 - type: map_at_100 value: 35.541 - type: map_at_1000 value: 35.589999999999996 - type: map_at_3 value: 30.496000000000002 - type: map_at_5 value: 32.718 - type: mrr_at_1 value: 22.750999999999998 - type: mrr_at_10 value: 35.024 - type: mrr_at_100 value: 36.125 - type: mrr_at_1000 value: 36.168 - type: mrr_at_3 value: 31.225 - type: mrr_at_5 value: 33.416000000000004 - type: ndcg_at_1 value: 22.750999999999998 - type: ndcg_at_10 value: 41.351 - type: ndcg_at_100 value: 46.92 - type: ndcg_at_1000 value: 48.111 - type: ndcg_at_3 value: 33.439 - type: ndcg_at_5 value: 37.407000000000004 - type: precision_at_1 value: 22.750999999999998 - type: precision_at_10 value: 6.564 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.288 - type: precision_at_5 value: 10.581999999999999 - type: recall_at_1 value: 22.096 - type: recall_at_10 value: 62.771 - type: recall_at_100 value: 88.529 - type: recall_at_1000 value: 97.55 - type: recall_at_3 value: 41.245 - type: recall_at_5 value: 50.788 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.16780665754673 - type: f1 value: 93.96331194859894 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.90606475148198 - type: f1 value: 58.58344986604187 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.14660390047075 - type: f1 value: 74.31533923533614 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.16139878950908 - type: f1 value: 80.18532656824924 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.949880906135085 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.56300351524862 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.196521894371315 - type: mrr value: 32.22644231694389 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.783 - type: map_at_10 value: 14.549000000000001 - type: map_at_100 value: 18.433 - type: map_at_1000 value: 19.949 - type: map_at_3 value: 10.936 - type: map_at_5 value: 12.514 - type: mrr_at_1 value: 47.368 - type: mrr_at_10 value: 56.42 - type: mrr_at_100 value: 56.908 - type: mrr_at_1000 value: 56.95 - type: mrr_at_3 value: 54.283 - type: mrr_at_5 value: 55.568 - type: ndcg_at_1 value: 45.666000000000004 - type: ndcg_at_10 value: 37.389 - type: ndcg_at_100 value: 34.253 - type: ndcg_at_1000 value: 43.059999999999995 - type: ndcg_at_3 value: 42.725 - type: ndcg_at_5 value: 40.193 - type: precision_at_1 value: 47.368 - type: precision_at_10 value: 27.988000000000003 - type: precision_at_100 value: 8.672 - type: precision_at_1000 value: 2.164 - type: precision_at_3 value: 40.248 - type: precision_at_5 value: 34.737 - type: recall_at_1 value: 6.783 - type: recall_at_10 value: 17.838 - type: recall_at_100 value: 33.672000000000004 - type: recall_at_1000 value: 66.166 - type: recall_at_3 value: 11.849 - type: recall_at_5 value: 14.205000000000002 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.698999999999998 - type: map_at_10 value: 46.556 - type: map_at_100 value: 47.652 - type: map_at_1000 value: 47.68 - type: map_at_3 value: 42.492000000000004 - type: map_at_5 value: 44.763999999999996 - type: mrr_at_1 value: 35.747 - type: mrr_at_10 value: 49.242999999999995 - type: mrr_at_100 value: 50.052 - type: mrr_at_1000 value: 50.068 - type: mrr_at_3 value: 45.867000000000004 - type: mrr_at_5 value: 47.778999999999996 - type: ndcg_at_1 value: 35.717999999999996 - type: ndcg_at_10 value: 54.14600000000001 - type: ndcg_at_100 value: 58.672999999999995 - type: ndcg_at_1000 value: 59.279 - type: ndcg_at_3 value: 46.407 - type: ndcg_at_5 value: 50.181 - type: precision_at_1 value: 35.717999999999996 - type: precision_at_10 value: 8.844000000000001 - type: precision_at_100 value: 1.139 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.993000000000002 - type: precision_at_5 value: 14.791000000000002 - type: recall_at_1 value: 31.698999999999998 - type: recall_at_10 value: 74.693 - type: recall_at_100 value: 94.15299999999999 - type: recall_at_1000 value: 98.585 - type: recall_at_3 value: 54.388999999999996 - type: recall_at_5 value: 63.08200000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.283 - type: map_at_10 value: 85.24000000000001 - type: map_at_100 value: 85.882 - type: map_at_1000 value: 85.897 - type: map_at_3 value: 82.326 - type: map_at_5 value: 84.177 - type: mrr_at_1 value: 82.21000000000001 - type: mrr_at_10 value: 88.228 - type: mrr_at_100 value: 88.32 - type: mrr_at_1000 value: 88.32 - type: mrr_at_3 value: 87.323 - type: mrr_at_5 value: 87.94800000000001 - type: ndcg_at_1 value: 82.17999999999999 - type: ndcg_at_10 value: 88.9 - type: ndcg_at_100 value: 90.079 - type: ndcg_at_1000 value: 90.158 - type: ndcg_at_3 value: 86.18299999999999 - type: ndcg_at_5 value: 87.71799999999999 - type: precision_at_1 value: 82.17999999999999 - type: precision_at_10 value: 13.464 - type: precision_at_100 value: 1.533 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.693 - type: precision_at_5 value: 24.792 - type: recall_at_1 value: 71.283 - type: recall_at_10 value: 95.742 - type: recall_at_100 value: 99.67200000000001 - type: recall_at_1000 value: 99.981 - type: recall_at_3 value: 87.888 - type: recall_at_5 value: 92.24 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.24267063669042 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.88056988932578 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.903 - type: map_at_10 value: 13.202 - type: map_at_100 value: 15.5 - type: map_at_1000 value: 15.870999999999999 - type: map_at_3 value: 9.407 - type: map_at_5 value: 11.238 - type: mrr_at_1 value: 24.2 - type: mrr_at_10 value: 35.867 - type: mrr_at_100 value: 37.001 - type: mrr_at_1000 value: 37.043 - type: mrr_at_3 value: 32.5 - type: mrr_at_5 value: 34.35 - type: ndcg_at_1 value: 24.2 - type: ndcg_at_10 value: 21.731 - type: ndcg_at_100 value: 30.7 - type: ndcg_at_1000 value: 36.618 - type: ndcg_at_3 value: 20.72 - type: ndcg_at_5 value: 17.954 - type: precision_at_1 value: 24.2 - type: precision_at_10 value: 11.33 - type: precision_at_100 value: 2.4410000000000003 - type: precision_at_1000 value: 0.386 - type: precision_at_3 value: 19.667 - type: precision_at_5 value: 15.86 - type: recall_at_1 value: 4.903 - type: recall_at_10 value: 22.962 - type: recall_at_100 value: 49.563 - type: recall_at_1000 value: 78.238 - type: recall_at_3 value: 11.953 - type: recall_at_5 value: 16.067999999999998 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.12694254604078 - type: cos_sim_spearman value: 80.30141815181918 - type: euclidean_pearson value: 81.34015449877128 - type: euclidean_spearman value: 80.13984197010849 - type: manhattan_pearson value: 81.31767068124086 - type: manhattan_spearman value: 80.11720513114103 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.13112984010417 - type: cos_sim_spearman value: 78.03063573402875 - type: euclidean_pearson value: 83.51928418844804 - type: euclidean_spearman value: 78.4045235411144 - type: manhattan_pearson value: 83.49981637388689 - type: manhattan_spearman value: 78.4042575139372 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.50327987379504 - type: cos_sim_spearman value: 84.18556767756205 - type: euclidean_pearson value: 82.69684424327679 - type: euclidean_spearman value: 83.5368106038335 - type: manhattan_pearson value: 82.57967581007374 - type: manhattan_spearman value: 83.43009053133697 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.50756863007814 - type: cos_sim_spearman value: 82.27204331279108 - type: euclidean_pearson value: 81.39535251429741 - type: euclidean_spearman value: 81.84386626336239 - type: manhattan_pearson value: 81.34281737280695 - type: manhattan_spearman value: 81.81149375673166 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.8727714856726 - type: cos_sim_spearman value: 87.95738287792312 - type: euclidean_pearson value: 86.62920602795887 - type: euclidean_spearman value: 87.05207355381243 - type: manhattan_pearson value: 86.53587918472225 - type: manhattan_spearman value: 86.95382961029586 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.52240359769479 - type: cos_sim_spearman value: 85.47685776238286 - type: euclidean_pearson value: 84.25815333483058 - type: euclidean_spearman value: 85.27415639683198 - type: manhattan_pearson value: 84.29127757025637 - type: manhattan_spearman value: 85.30226224917351 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.42501708915708 - type: cos_sim_spearman value: 86.42276182795041 - type: euclidean_pearson value: 86.5408207354761 - type: euclidean_spearman value: 85.46096321750838 - type: manhattan_pearson value: 86.54177303026881 - type: manhattan_spearman value: 85.50313151916117 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.86521089250766 - type: cos_sim_spearman value: 65.94868540323003 - type: euclidean_pearson value: 67.16569626533084 - type: euclidean_spearman value: 66.37667004134917 - type: manhattan_pearson value: 67.1482365102333 - type: manhattan_spearman value: 66.53240122580029 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.64746265365318 - type: cos_sim_spearman value: 86.41888825906786 - type: euclidean_pearson value: 85.27453642725811 - type: euclidean_spearman value: 85.94095796602544 - type: manhattan_pearson value: 85.28643660505334 - type: manhattan_spearman value: 85.95028003260744 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.48903153618527 - type: mrr value: 96.41081503826601 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 58.594 - type: map_at_10 value: 69.296 - type: map_at_100 value: 69.782 - type: map_at_1000 value: 69.795 - type: map_at_3 value: 66.23 - type: map_at_5 value: 68.293 - type: mrr_at_1 value: 61.667 - type: mrr_at_10 value: 70.339 - type: mrr_at_100 value: 70.708 - type: mrr_at_1000 value: 70.722 - type: mrr_at_3 value: 68.0 - type: mrr_at_5 value: 69.56700000000001 - type: ndcg_at_1 value: 61.667 - type: ndcg_at_10 value: 74.039 - type: ndcg_at_100 value: 76.103 - type: ndcg_at_1000 value: 76.47800000000001 - type: ndcg_at_3 value: 68.967 - type: ndcg_at_5 value: 71.96900000000001 - type: precision_at_1 value: 61.667 - type: precision_at_10 value: 9.866999999999999 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.111 - type: precision_at_5 value: 18.2 - type: recall_at_1 value: 58.594 - type: recall_at_10 value: 87.422 - type: recall_at_100 value: 96.667 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 74.217 - type: recall_at_5 value: 81.539 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85049504950496 - type: cos_sim_ap value: 96.33111544137081 - type: cos_sim_f1 value: 92.35443037974684 - type: cos_sim_precision value: 93.53846153846153 - type: cos_sim_recall value: 91.2 - type: dot_accuracy value: 99.82376237623762 - type: dot_ap value: 95.38082527310888 - type: dot_f1 value: 90.90909090909092 - type: dot_precision value: 92.90187891440502 - type: dot_recall value: 89.0 - type: euclidean_accuracy value: 99.84851485148515 - type: euclidean_ap value: 96.32316003996347 - type: euclidean_f1 value: 92.2071392659628 - type: euclidean_precision value: 92.71991911021233 - type: euclidean_recall value: 91.7 - type: manhattan_accuracy value: 99.84851485148515 - type: manhattan_ap value: 96.3655668249217 - type: manhattan_f1 value: 92.18356026222895 - type: manhattan_precision value: 92.98067141403867 - type: manhattan_recall value: 91.4 - type: max_accuracy value: 99.85049504950496 - type: max_ap value: 96.3655668249217 - type: max_f1 value: 92.35443037974684 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.94861371629051 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.009430451385 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.61164066427969 - type: mrr value: 55.49710603938544 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.622620124907662 - type: cos_sim_spearman value: 31.0678351356163 - type: dot_pearson value: 30.863727693306814 - type: dot_spearman value: 31.230306567021255 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 2.011 - type: map_at_100 value: 10.974 - type: map_at_1000 value: 25.819 - type: map_at_3 value: 0.6649999999999999 - type: map_at_5 value: 1.076 - type: mrr_at_1 value: 86.0 - type: mrr_at_10 value: 91.8 - type: mrr_at_100 value: 91.8 - type: mrr_at_1000 value: 91.8 - type: mrr_at_3 value: 91.0 - type: mrr_at_5 value: 91.8 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 78.07300000000001 - type: ndcg_at_100 value: 58.231 - type: ndcg_at_1000 value: 51.153000000000006 - type: ndcg_at_3 value: 81.123 - type: ndcg_at_5 value: 81.059 - type: precision_at_1 value: 86.0 - type: precision_at_10 value: 83.0 - type: precision_at_100 value: 59.38 - type: precision_at_1000 value: 22.55 - type: precision_at_3 value: 87.333 - type: precision_at_5 value: 86.8 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.2079999999999997 - type: recall_at_100 value: 14.069 - type: recall_at_1000 value: 47.678 - type: recall_at_3 value: 0.7040000000000001 - type: recall_at_5 value: 1.161 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.809 - type: map_at_10 value: 10.394 - type: map_at_100 value: 16.598 - type: map_at_1000 value: 18.142 - type: map_at_3 value: 5.572 - type: map_at_5 value: 7.1370000000000005 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 46.564 - type: mrr_at_100 value: 47.469 - type: mrr_at_1000 value: 47.469 - type: mrr_at_3 value: 42.177 - type: mrr_at_5 value: 44.524 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 25.701 - type: ndcg_at_100 value: 37.532 - type: ndcg_at_1000 value: 48.757 - type: ndcg_at_3 value: 28.199999999999996 - type: ndcg_at_5 value: 25.987 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 23.469 - type: precision_at_100 value: 7.9799999999999995 - type: precision_at_1000 value: 1.5350000000000001 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.809 - type: recall_at_10 value: 16.887 - type: recall_at_100 value: 48.67 - type: recall_at_1000 value: 82.89699999999999 - type: recall_at_3 value: 6.521000000000001 - type: recall_at_5 value: 9.609 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.57860000000001 - type: ap value: 13.82629211536393 - type: f1 value: 54.59860966183956 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.38030560271647 - type: f1 value: 59.69685552567865 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.4736717043405 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.92853311080646 - type: cos_sim_ap value: 77.67872502591382 - type: cos_sim_f1 value: 70.33941236068895 - type: cos_sim_precision value: 67.63273258645884 - type: cos_sim_recall value: 73.27176781002639 - type: dot_accuracy value: 85.79603027954938 - type: dot_ap value: 73.73786190233379 - type: dot_f1 value: 67.3437901774235 - type: dot_precision value: 65.67201604814443 - type: dot_recall value: 69.10290237467018 - type: euclidean_accuracy value: 86.94045419324074 - type: euclidean_ap value: 77.6687791535167 - type: euclidean_f1 value: 70.47209214023542 - type: euclidean_precision value: 67.7207492094381 - type: euclidean_recall value: 73.45646437994723 - type: manhattan_accuracy value: 86.87488823985218 - type: manhattan_ap value: 77.63373392430728 - type: manhattan_f1 value: 70.40920716112532 - type: manhattan_precision value: 68.31265508684864 - type: manhattan_recall value: 72.63852242744063 - type: max_accuracy value: 86.94045419324074 - type: max_ap value: 77.67872502591382 - type: max_f1 value: 70.47209214023542 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.67155664221679 - type: cos_sim_ap value: 85.64591703003417 - type: cos_sim_f1 value: 77.59531005352656 - type: cos_sim_precision value: 73.60967184801382 - type: cos_sim_recall value: 82.03726516784724 - type: dot_accuracy value: 88.41541506578181 - type: dot_ap value: 84.6482788957769 - type: dot_f1 value: 77.04748541466657 - type: dot_precision value: 74.02440754931176 - type: dot_recall value: 80.3279950723745 - type: euclidean_accuracy value: 88.63080684596576 - type: euclidean_ap value: 85.44570045321562 - type: euclidean_f1 value: 77.28769403336106 - type: euclidean_precision value: 72.90600040958427 - type: euclidean_recall value: 82.22975053895904 - type: manhattan_accuracy value: 88.59393798269105 - type: manhattan_ap value: 85.40271361038187 - type: manhattan_f1 value: 77.17606419344392 - type: manhattan_precision value: 72.4447747078295 - type: manhattan_recall value: 82.5685247921158 - type: max_accuracy value: 88.67155664221679 - type: max_ap value: 85.64591703003417 - type: max_f1 value: 77.59531005352656 license: mit language: - en --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

intfloat/multilingual-e5-large

intfloat

sentence-transformers

feature-extraction

--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - type: recall_at_5 value: 67.71000000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.30960650674069 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.427074197498996 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.05942144105269 - type: cos_sim_spearman value: 82.51212105850809 - type: euclidean_pearson value: 81.95639829909122 - type: euclidean_spearman value: 82.3717564144213 - type: manhattan_pearson value: 81.79273425468256 - type: manhattan_spearman value: 82.20066817871039 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.46764091858039 - type: f1 value: 99.37717466945023 - type: precision value: 99.33194154488518 - type: recall value: 99.46764091858039 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.29407880255337 - type: f1 value: 98.11248073959938 - type: precision value: 98.02443319392472 - type: recall value: 98.29407880255337 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - 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type: v_measure value: 33.4987096128766 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.558249999999997 - type: map_at_10 value: 34.44425000000001 - type: map_at_100 value: 35.59833333333333 - type: map_at_1000 value: 35.706916666666665 - type: map_at_3 value: 31.691749999999995 - type: map_at_5 value: 33.252916666666664 - type: mrr_at_1 value: 30.252666666666666 - type: mrr_at_10 value: 38.60675 - type: mrr_at_100 value: 39.42666666666666 - type: mrr_at_1000 value: 39.48408333333334 - type: mrr_at_3 value: 36.17441666666665 - type: mrr_at_5 value: 37.56275 - type: ndcg_at_1 value: 30.252666666666666 - type: ndcg_at_10 value: 39.683 - type: ndcg_at_100 value: 44.68541666666667 - type: ndcg_at_1000 value: 46.94316666666668 - type: ndcg_at_3 value: 34.961749999999995 - type: ndcg_at_5 value: 37.215666666666664 - type: precision_at_1 value: 30.252666666666666 - type: precision_at_10 value: 6.904166666666667 - 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ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-large [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 24 layers and the embedding size is 1024. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-large') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

facebook/esm2_t12_35M_UR50D

facebook

transformers

fill-mask

--- license: mit widget: - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG" --- ## ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train: | Checkpoint name | Num layers | Num parameters | |------------------------------|----|----------| | [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B | | [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B | | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M | | [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M | | [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M | | [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M |

cardiffnlp/twitter-roberta-base-irony

cardiffnlp

transformers

text-classification

--- datasets: - tweet_eval language: - en --- # Twitter-roBERTa-base for Irony Detection This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. This model has integrated into the [TweetNLP Python library](https://github.com/cardiffnlp/tweetnlp/). - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [ ] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='irony' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Great, it broke the first day..." text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Great, it broke the first day..." # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) irony 0.914 2) non_irony 0.086 ``` ### Reference Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model. ```bibtex @inproceedings{barbieri-etal-2020-tweeteval, title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification", author = "Barbieri, Francesco and Camacho-Collados, Jose and Espinosa Anke, Luis and Neves, Leonardo", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.148", doi = "10.18653/v1/2020.findings-emnlp.148", pages = "1644--1650" } ```

prajjwal1/bert-tiny

prajjwal1

transformers

--- language: - en license: - mit tags: - BERT - MNLI - NLI - transformer - pre-training --- The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) Other models to check out: - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)

BAAI/bge-reranker-v2-m3

BAAI

sentence-transformers

text-classification

--- license: apache-2.0 pipeline_tag: text-classification tags: - transformers - sentence-transformers - text-embeddings-inference language: - multilingual --- # Reranker **More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).** - [Model List](#model-list) - [Usage](#usage) - [Fine-tuning](#fine-tune) - [Evaluation](#evaluation) - [Citation](#citation) Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. ## Model List | Model | Base model | Language | layerwise | feature | |:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:| | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | | [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | | [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | [gemma-2b](https://huggingface.co/google/gemma-2b) | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | | [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | You can select the model according your senario and resource. - For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) - For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) ## Usage ### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) # -5.65234375 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['query', 'passage'], normalize=True) print(score) # 0.003497010252573502 scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) # [-8.1875, 5.26171875] # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True) print(scores) # [0.00027803096387751553, 0.9948403768236574] ``` #### For LLM-based reranker ```python from FlagEmbedding import FlagLLMReranker reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### For LLM-based layerwise reranker ```python from FlagEmbedding import LayerWiseFlagLLMReranker reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score. print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28]) print(scores) ``` ### Using Huggingface transformers #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### For LLM-based reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length * 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma') model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma') yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0] model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer) scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float() print(scores) ``` #### For LLM-based layerwise reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length * 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16) model = model.to('cuda') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer).to(model.device) all_scores = model(**inputs, return_dict=True, cutoff_layers=[28]) all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]] print(all_scores) ``` ## Fine-tune ### Data Format Train data should be a json file, where each line is a dict like this: ``` {"query": str, "pos": List[str], "neg":List[str], "prompt": str} ``` `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives. See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file. ### Train You can fine-tune the reranker with the following code: **For llm-based reranker** ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_instruction.run \ --output_dir {path to save model} \ --model_name_or_path google/gemma-2b \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj ``` **For llm-based layerwise reranker** ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \ --output_dir {path to save model} \ --model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj \ --start_layer 8 \ --head_multi True \ --head_type simple \ --lora_extra_parameters linear_head ``` Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets: - [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) - [quora train data](https://huggingface.co/datasets/quora) - [fever train data](https://fever.ai/dataset/fever.html) ## Evaluation - llama-index.  - BEIR. rereank the top 100 results from bge-en-v1.5 large.  rereank the top 100 results from e5 mistral 7b instruct.  - CMTEB-retrieval. It rereank the top 100 results from bge-zh-v1.5 large.  - miracl (multi-language). It rereank the top 100 results from bge-m3.  ## Citation If you find this repository useful, please consider giving a star and citation ```bibtex @misc{li2023making, title={Making Large Language Models A Better Foundation For Dense Retrieval}, author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, year={2023}, eprint={2312.15503}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{chen2024bge, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

oliverguhr/fullstop-punctuation-multilang-large

oliverguhr

transformers

token-classification

--- language: - en - de - fr - it - multilingual tags: - punctuation prediction - punctuation datasets: wmt/europarl license: mit widget: - text: "Ho sentito che ti sei laureata il che mi fa molto piacere" example_title: "Italian" - text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre" example_title: "French" - text: "Ist das eine Frage Frau Müller" example_title: "German" - text: "Yet she blushed as if with guilt when Cynthia reading her thoughts said to her one day Molly you're very glad to get rid of us are not you" example_title: "English" metrics: - f1 --- This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl) provided by the [SEPP-NLG Shared Task](https://sites.google.com/view/sentence-segmentation). *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** ## Sample Code We provide a simple python package that allows you to process text of any length. ## Install To get started install the package from [pypi](https://pypi.org/project/deepmultilingualpunctuation/): ```bash pip install deepmultilingualpunctuation ``` ### Restore Punctuation ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel() text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" result = model.restore_punctuation(text) print(result) ``` **output** > My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller? ### Predict Labels ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel() text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" clean_text = model.preprocess(text) labled_words = model.predict(clean_text) print(labled_words) ``` **output** > [['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['Müller', '?', 0.99863917]] ## Results The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for the different languages: | Label | EN | DE | FR | IT | | ------------- | ----- | ----- | ----- | ----- | | 0 | 0.991 | 0.997 | 0.992 | 0.989 | | . | 0.948 | 0.961 | 0.945 | 0.942 | | ? | 0.890 | 0.893 | 0.871 | 0.832 | | , | 0.819 | 0.945 | 0.831 | 0.798 | | : | 0.575 | 0.652 | 0.620 | 0.588 | | - | 0.425 | 0.435 | 0.431 | 0.421 | | macro average | 0.775 | 0.814 | 0.782 | 0.762 | ## Languages ### Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | | English, Italian, French and German | [oliverguhr/fullstop-punctuation-multilang-large](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large) | | English, Italian, French, German and Dutch | [oliverguhr/fullstop-punctuation-multilingual-sonar-base](https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-sonar-base) | | Dutch | [oliverguhr/fullstop-dutch-sonar-punctuation-prediction](https://huggingface.co/oliverguhr/fullstop-dutch-sonar-punctuation-prediction) | ### Community Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | |English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian| [kredor/punctuate-all](https://huggingface.co/kredor/punctuate-all) | | Catalan | [softcatala/fullstop-catalan-punctuation-prediction](https://huggingface.co/softcatala/fullstop-catalan-punctuation-prediction) | | Welsh | [techiaith/fullstop-welsh-punctuation-prediction](https://huggingface.co/techiaith/fullstop-welsh-punctuation-prediction) | You can use different models by setting the model parameter: ```python model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction") ``` ## Where do I find the code and can I train my own model? Yes you can! For complete code of the reareach project take a look at [this repository](https://github.com/oliverguhr/fullstop-deep-punctuation-prediction). There is also an guide on [how to fine tune this model for you data / language](https://github.com/oliverguhr/fullstop-deep-punctuation-prediction/blob/main/other_languages/readme.md). ## References ``` @article{guhr-EtAl:2021:fullstop, title={FullStop: Multilingual Deep Models for Punctuation Prediction}, author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim}, booktitle = {Proceedings of the Swiss Text Analytics Conference 2021}, month = {June}, year = {2021}, address = {Winterthur, Switzerland}, publisher = {CEUR Workshop Proceedings}, url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf} } ```

davebulaval/MeaningBERT

davebulaval

transformers

text-classification

--- title: MeaningBERT emoji: 🦀 colorFrom: purple colorTo: indigo sdk: gradio sdk_version: 4.2.0 app_file: app.py pinned: false --- # Here is MeaningBERT MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was proposed in our article [MeaningBERT: assessing meaning preservation between sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full). Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article. > This public version of our model uses the best model trained (where in our article, we present the performance results > of an average of 10 models) for a more extended period (500 epochs instead of 250). We have observed later that the > model can further reduce dev loss and increase performance. Also, we have changed the data augmentation technique used > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a). - [HuggingFace Model Card](https://huggingface.co/davebulaval/MeaningBERT) - [HuggingFace Metric Card](https://huggingface.co/spaces/davebulaval/meaningbert) ## Sanity Check Correlation to human judgment is one way to evaluate the quality of a meaning preservation metric. However, it is inherently subjective, since it uses human judgment as a gold standard, and expensive since it requires a large dataset annotated by several humans. As an alternative, we designed two automated tests: evaluating meaning preservation between identical sentences (which should be 100% preserving) and between unrelated sentences (which should be 0% preserving). In these tests, the meaning preservation target value is not subjective and does not require human annotation to be measured. They represent a trivial and minimal threshold a good automatic meaning preservation metric should be able to achieve. Namely, a metric should be minimally able to return a perfect score (i.e., 100%) if two identical sentences are compared and return a null score (i.e., 0%) if two sentences are completely unrelated. ### Identical Sentences The first test evaluates meaning preservation between identical sentences. To analyze the metrics' capabilities to pass this test, we count the number of times a metric rating was greater or equal to a threshold value X∈[95, 99] and divide It is calculated by the number of sentences to create a ratio of the number of times the metric gives the expected rating. To account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a threshold value of 100%. ### Unrelated Sentences Our second test evaluates meaning preservation between a source sentence and an unrelated sentence generated by a large language model.3 The idea is to verify that the metric finds a meaning preservation rating of 0 when given a completely irrelevant sentence mainly composed of irrelevant words (also known as word soup). Since this test's expected rating is 0, we check that the metric rating is lower or equal to a threshold value X∈[5, 1]. Again, to account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a threshold value of 0%. ## Use MeaningBERT You can use MeaningBERT as a [model](https://huggingface.co/davebulaval/MeaningBERT) that you can retrain or use for inference using the following with HuggingFace ```python # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") model = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT") ``` or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") scorer = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT") scorer.eval() documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."] simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.", "Whatever, whenever, this is a sentence."] # We tokenize the text as a pair and return Pytorch Tensors tokenize_text = tokenizer(documents, simplifications, truncation=True, padding=True, return_tensors="pt") with torch.no_grad(): # We process the text scores = scorer(**tokenize_text) print(scores.logits.tolist()) ``` or using our HuggingFace Metric module ```python import evaluate documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."] simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.", "Whatever, whenever, this is a sentence."] meaning_bert = evaluate.load("davebulaval/meaningbert") print(meaning_bert.compute(documents=documents, simplifications=simplifications)) ``` ------------------ ## Cite Use the following citation to cite MeaningBERT ``` @ARTICLE{10.3389/frai.2023.1223924, AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard}, TITLE={MeaningBERT: assessing meaning preservation between sentences}, JOURNAL={Frontiers in Artificial Intelligence}, VOLUME={6}, YEAR={2023}, URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924}, DOI={10.3389/frai.2023.1223924}, ISSN={2624-8212}, } ``` ------------------ ## Contributing to MeaningBERT We welcome user input, whether it regards bugs found in the library or feature propositions! Make sure to have a look at our [contributing guidelines](https://github.com/GRAAL-Research/MeaningBERT/blob/main/.github/CONTRIBUTING.md) for more details on this matter. ## License MeaningBERT is MIT licensed, as found in the [LICENSE file](https://github.com/GRAAL-Research/risc/blob/main/LICENSE). ------------------

trl-internal-testing/tiny-random-LlamaForCausalLM

trl-internal-testing

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lengyue233/content-vec-best

lengyue233

transformers

--- license: mit --- # Content Vec Best Official Repo: [ContentVec](https://github.com/auspicious3000/contentvec) This repo brings fairseq ContentVec model to HuggingFace Transformers. ## How to use To use this model, you need to define ```python class HubertModelWithFinalProj(HubertModel): def __init__(self, config): super().__init__(config) # The final projection layer is only used for backward compatibility. # Following https://github.com/auspicious3000/contentvec/issues/6 # Remove this layer is necessary to achieve the desired outcome. self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size) ``` and then load the model with ```python model = HubertModelWithFinalProj.from_pretrained("lengyue233/content-vec-best") x = model(audio)["last_hidden_state"] ``` ## How to convert You need to download the ContentVec_legacy model from the official repo, and then run ```bash python convert.py ```

microsoft/resnet-50

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k --- # ResNet-50 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") inputs = processor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```

autogluon/chronos-t5-tiny

autogluon

transformers

time-series-forecasting

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Tiny) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-tiny", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

patrickjohncyh/fashion-clip

patrickjohncyh

transformers

zero-shot-image-classification

--- license: mit tags: - vision - language - fashion - ecommerce library_name: transformers language: - en widget: - src: https://cdn-images.farfetch-contents.com/19/76/05/56/19760556_44221665_1000.jpg candidate_labels: black shoe, red shoe, a cat example_title: Black Shoe --- [](https://www.youtube.com/watch?v=uqRSc-KSA1Y) [](https://huggingface.co/patrickjohncyh/fashion-clip) [](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW?usp=sharing) [](https://towardsdatascience.com/teaching-clip-some-fashion-3005ac3fdcc3) [](https://huggingface.co/spaces/vinid/fashion-clip-app) # Model Card: Fashion CLIP Disclaimer: The model card adapts the model card from [here](https://huggingface.co/openai/clip-vit-base-patch32). ## Model Details UPDATE (10/03/23): We have updated the model! We found that [laion/CLIP-ViT-B-32-laion2B-s34B-b79K](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) checkpoint (thanks [Bin](https://www.linkedin.com/in/bin-duan-56205310/)!) worked better than original OpenAI CLIP on Fashion. We thus fine-tune a newer (and better!) version of FashionCLIP (henceforth FashionCLIP 2.0), while keeping the architecture the same. We postulate that the perofrmance gains afforded by `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` are due to the increased training data (5x OpenAI CLIP data). Our [thesis](https://www.nature.com/articles/s41598-022-23052-9), however, remains the same -- fine-tuning `laion/CLIP` on our fashion dataset improved zero-shot perofrmance across our benchmarks. See the below table comparing weighted macro F1 score across models. | Model | FMNIST | KAGL | DEEP | | ------------- | ------------- | ------------- | ------------- | | OpenAI CLIP | 0.66 | 0.63 | 0.45 | | FashionCLIP | 0.74 | 0.67 | 0.48 | | Laion CLIP | 0.78 | 0.71 | 0.58 | | FashionCLIP 2.0 | __0.83__ | __0.73__ | __0.62__ | --- FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by [OpenAI](https://github.com/openai/CLIP), we train FashionCLIP on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks. FashionCLIP was not developed for model deplyoment - to do so, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. ### Model Date March 2023 ### Model Type The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained, starting from a pre-trained checkpoint, to maximize the similarity of (image, text) pairs via a contrastive loss on a fashion dataset containing 800K products. ### Documents - [FashionCLIP Github Repo](https://github.com/patrickjohncyh/fashion-clip) - [FashionCLIP Paper](https://www.nature.com/articles/s41598-022-23052-9) ## Data The model was trained on (image, text) pairs obtained from the Farfecth dataset[^1 Awaiting official release.], an English dataset comprising over 800K fashion products, with more than 3K brands across dozens of object types. The image used for encoding is the standard product image, which is a picture of the item over a white background, with no humans. The text used is a concatenation of the _highlight_ (e.g., “stripes”, “long sleeves”, “Armani”) and _short description_ (“80s styled t-shirt”)) available in the Farfetch dataset. ## Limitations, Bias and Fiarness We acknowledge certain limitations of FashionCLIP and expect that it inherits certain limitations and biases present in the original CLIP model. We do not expect our fine-tuning to significantly augment these limitations: we acknowledge that the fashion data we use makes explicit assumptions about the notion of gender as in "blue shoes for a woman" that inevitably associate aspects of clothing with specific people. Our investigations also suggest that the data used introduces certain limitations in FashionCLIP. From the textual modality, given that most captions derived from the Farfetch dataset are long, we observe that FashionCLIP may be more performant in longer queries than shorter ones. From the image modality, FashionCLIP is also biased towards standard product images (centered, white background). Model selection, i.e. selecting an appropariate stopping critera during fine-tuning, remains an open challenge. We observed that using loss on an in-domain (i.e. same distribution as test) validation dataset is a poor selection critera when out-of-domain generalization (i.e. across different datasets) is desired, even when the dataset used is relatively diverse and large. ## Citation ``` @Article{Chia2022, title="Contrastive language and vision learning of general fashion concepts", author="Chia, Patrick John and Attanasio, Giuseppe and Bianchi, Federico and Terragni, Silvia and Magalh{\~a}es, Ana Rita and Goncalves, Diogo and Greco, Ciro and Tagliabue, Jacopo", journal="Scientific Reports", year="2022", month="Nov", day="08", volume="12", number="1", abstract="The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.", issn="2045-2322", doi="10.1038/s41598-022-23052-9", url="https://doi.org/10.1038/s41598-022-23052-9" } ```

BAAI/bge-large-en-v1.5

BAAI

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-large-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8507462686567 - type: ap value: 38.566457320228245 - type: f1 value: 69.69386648043475 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.416675 - type: ap value: 89.1928861155922 - type: f1 value: 92.39477019574215 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.175999999999995 - type: f1 value: 47.80712792870253 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 40.184999999999995 - type: map_at_10 value: 55.654 - type: map_at_100 value: 56.25 - type: map_at_1000 value: 56.255 - type: map_at_3 value: 51.742999999999995 - type: map_at_5 value: 54.129000000000005 - type: mrr_at_1 value: 40.967 - type: mrr_at_10 value: 55.96 - type: mrr_at_100 value: 56.54900000000001 - type: mrr_at_1000 value: 56.554 - type: mrr_at_3 value: 51.980000000000004 - type: mrr_at_5 value: 54.44 - type: ndcg_at_1 value: 40.184999999999995 - type: ndcg_at_10 value: 63.542 - type: ndcg_at_100 value: 65.96499999999999 - type: ndcg_at_1000 value: 66.08699999999999 - type: ndcg_at_3 value: 55.582 - type: ndcg_at_5 value: 59.855000000000004 - type: precision_at_1 value: 40.184999999999995 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.238 - type: precision_at_5 value: 15.405 - type: recall_at_1 value: 40.184999999999995 - type: recall_at_10 value: 88.407 - type: recall_at_100 value: 98.72 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.714 - type: recall_at_5 value: 77.027 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.567077926750066 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.19453389182364 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.46555939623092 - type: mrr value: 77.82361605768807 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.9554128814735 - type: cos_sim_spearman value: 84.65373612172036 - type: euclidean_pearson value: 83.2905059954138 - type: euclidean_spearman value: 84.52240782811128 - type: manhattan_pearson value: 82.99533802997436 - type: manhattan_spearman value: 84.20673798475734 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.78896103896103 - type: f1 value: 87.77189310964883 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.714538337650495 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.90108349284447 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.795 - type: map_at_10 value: 43.669000000000004 - type: map_at_100 value: 45.151 - type: map_at_1000 value: 45.278 - type: map_at_3 value: 40.006 - type: map_at_5 value: 42.059999999999995 - type: mrr_at_1 value: 39.771 - type: mrr_at_10 value: 49.826 - type: mrr_at_100 value: 50.504000000000005 - type: mrr_at_1000 value: 50.549 - type: mrr_at_3 value: 47.115 - type: mrr_at_5 value: 48.832 - type: ndcg_at_1 value: 39.771 - type: ndcg_at_10 value: 50.217999999999996 - type: ndcg_at_100 value: 55.454 - type: ndcg_at_1000 value: 57.37 - type: ndcg_at_3 value: 44.885000000000005 - type: ndcg_at_5 value: 47.419 - type: precision_at_1 value: 39.771 - type: precision_at_10 value: 9.642000000000001 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 15.536 - type: recall_at_1 value: 32.795 - type: recall_at_10 value: 62.580999999999996 - type: recall_at_100 value: 84.438 - type: recall_at_1000 value: 96.492 - type: recall_at_3 value: 47.071000000000005 - type: recall_at_5 value: 54.079 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.671 - type: map_at_10 value: 43.334 - type: map_at_100 value: 44.566 - type: map_at_1000 value: 44.702999999999996 - type: map_at_3 value: 40.343 - type: map_at_5 value: 41.983 - type: mrr_at_1 value: 40.764 - type: mrr_at_10 value: 49.382 - type: mrr_at_100 value: 49.988 - type: mrr_at_1000 value: 50.03300000000001 - type: mrr_at_3 value: 47.293 - type: mrr_at_5 value: 48.51 - type: ndcg_at_1 value: 40.764 - type: ndcg_at_10 value: 49.039 - type: ndcg_at_100 value: 53.259 - type: ndcg_at_1000 value: 55.253 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 46.839999999999996 - type: precision_at_1 value: 40.764 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 21.72 - type: precision_at_5 value: 15.299 - type: recall_at_1 value: 32.671 - type: recall_at_10 value: 58.816 - type: recall_at_100 value: 76.654 - type: recall_at_1000 value: 89.05999999999999 - type: recall_at_3 value: 46.743 - type: recall_at_5 value: 51.783 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.328 - type: map_at_10 value: 53.32599999999999 - type: map_at_100 value: 54.37499999999999 - type: map_at_1000 value: 54.429 - type: map_at_3 value: 49.902 - type: map_at_5 value: 52.002 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 56.858 - type: mrr_at_100 value: 57.522 - type: mrr_at_1000 value: 57.54899999999999 - type: mrr_at_3 value: 54.472 - type: mrr_at_5 value: 55.996 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 59.313 - type: ndcg_at_100 value: 63.266999999999996 - type: ndcg_at_1000 value: 64.36 - type: ndcg_at_3 value: 53.815000000000005 - type: ndcg_at_5 value: 56.814 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.53 - type: precision_at_100 value: 1.238 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.054000000000002 - type: precision_at_5 value: 16.589000000000002 - type: recall_at_1 value: 40.328 - type: recall_at_10 value: 73.421 - type: recall_at_100 value: 90.059 - type: recall_at_1000 value: 97.81 - type: recall_at_3 value: 59.009 - type: recall_at_5 value: 66.352 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.424 - type: map_at_10 value: 36.332 - type: map_at_100 value: 37.347 - type: map_at_1000 value: 37.422 - type: map_at_3 value: 33.743 - type: map_at_5 value: 35.176 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.233 - type: mrr_at_100 value: 39.109 - type: mrr_at_1000 value: 39.164 - type: mrr_at_3 value: 35.876000000000005 - type: mrr_at_5 value: 37.169000000000004 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 41.439 - type: ndcg_at_100 value: 46.42 - type: ndcg_at_1000 value: 48.242000000000004 - type: ndcg_at_3 value: 36.362 - type: ndcg_at_5 value: 38.743 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.315999999999999 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.443000000000001 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 27.424 - type: recall_at_10 value: 55.364000000000004 - type: recall_at_100 value: 78.211 - type: recall_at_1000 value: 91.74600000000001 - type: recall_at_3 value: 41.379 - type: recall_at_5 value: 47.14 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.601 - type: map_at_10 value: 27.826 - type: map_at_100 value: 29.017 - type: map_at_1000 value: 29.137 - type: map_at_3 value: 25.125999999999998 - type: map_at_5 value: 26.765 - type: mrr_at_1 value: 24.005000000000003 - type: mrr_at_10 value: 32.716 - type: mrr_at_100 value: 33.631 - type: mrr_at_1000 value: 33.694 - type: mrr_at_3 value: 29.934 - type: mrr_at_5 value: 31.630999999999997 - type: ndcg_at_1 value: 24.005000000000003 - type: ndcg_at_10 value: 33.158 - type: ndcg_at_100 value: 38.739000000000004 - type: ndcg_at_1000 value: 41.495 - type: ndcg_at_3 value: 28.185 - type: ndcg_at_5 value: 30.796 - type: precision_at_1 value: 24.005000000000003 - type: precision_at_10 value: 5.908 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.876 - type: recall_at_1 value: 19.601 - type: recall_at_10 value: 44.746 - type: recall_at_100 value: 68.82300000000001 - type: recall_at_1000 value: 88.215 - type: recall_at_3 value: 31.239 - type: recall_at_5 value: 37.695 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.130000000000003 - type: map_at_10 value: 40.96 - type: map_at_100 value: 42.282 - type: map_at_1000 value: 42.392 - type: map_at_3 value: 37.889 - type: map_at_5 value: 39.661 - type: mrr_at_1 value: 36.958999999999996 - type: mrr_at_10 value: 46.835 - type: mrr_at_100 value: 47.644 - type: mrr_at_1000 value: 47.688 - type: mrr_at_3 value: 44.562000000000005 - type: mrr_at_5 value: 45.938 - type: ndcg_at_1 value: 36.958999999999996 - type: ndcg_at_10 value: 47.06 - type: ndcg_at_100 value: 52.345 - type: ndcg_at_1000 value: 54.35 - type: ndcg_at_3 value: 42.301 - type: ndcg_at_5 value: 44.635999999999996 - type: precision_at_1 value: 36.958999999999996 - type: precision_at_10 value: 8.479000000000001 - type: precision_at_100 value: 1.284 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 20.244 - type: precision_at_5 value: 14.224999999999998 - type: recall_at_1 value: 30.130000000000003 - type: recall_at_10 value: 59.27 - type: recall_at_100 value: 81.195 - type: recall_at_1000 value: 94.21199999999999 - type: recall_at_3 value: 45.885 - type: recall_at_5 value: 52.016 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.169999999999998 - type: map_at_10 value: 36.451 - type: map_at_100 value: 37.791000000000004 - type: map_at_1000 value: 37.897 - type: map_at_3 value: 33.109 - type: map_at_5 value: 34.937000000000005 - type: mrr_at_1 value: 32.877 - type: mrr_at_10 value: 42.368 - type: mrr_at_100 value: 43.201 - type: mrr_at_1000 value: 43.259 - type: mrr_at_3 value: 39.763999999999996 - type: mrr_at_5 value: 41.260000000000005 - type: ndcg_at_1 value: 32.877 - type: ndcg_at_10 value: 42.659000000000006 - type: ndcg_at_100 value: 48.161 - type: ndcg_at_1000 value: 50.345 - type: ndcg_at_3 value: 37.302 - type: ndcg_at_5 value: 39.722 - type: precision_at_1 value: 32.877 - type: precision_at_10 value: 7.9 - type: precision_at_100 value: 1.236 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.846 - type: precision_at_5 value: 12.9 - type: recall_at_1 value: 26.169999999999998 - type: recall_at_10 value: 55.35 - type: recall_at_100 value: 78.755 - type: recall_at_1000 value: 93.518 - type: recall_at_3 value: 40.176 - type: recall_at_5 value: 46.589000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.15516666666667 - type: map_at_10 value: 36.65741666666667 - type: map_at_100 value: 37.84991666666666 - type: map_at_1000 value: 37.96316666666667 - type: map_at_3 value: 33.74974999999999 - type: map_at_5 value: 35.3765 - type: mrr_at_1 value: 32.08233333333334 - type: mrr_at_10 value: 41.033833333333334 - type: mrr_at_100 value: 41.84524999999999 - type: mrr_at_1000 value: 41.89983333333333 - type: mrr_at_3 value: 38.62008333333333 - type: mrr_at_5 value: 40.03441666666666 - type: ndcg_at_1 value: 32.08233333333334 - type: ndcg_at_10 value: 42.229 - type: ndcg_at_100 value: 47.26716666666667 - type: ndcg_at_1000 value: 49.43466666666667 - type: ndcg_at_3 value: 37.36408333333333 - type: ndcg_at_5 value: 39.6715 - type: precision_at_1 value: 32.08233333333334 - type: precision_at_10 value: 7.382583333333334 - type: precision_at_100 value: 1.16625 - type: precision_at_1000 value: 0.15408333333333332 - type: precision_at_3 value: 17.218 - type: precision_at_5 value: 12.21875 - type: recall_at_1 value: 27.15516666666667 - type: recall_at_10 value: 54.36683333333333 - type: recall_at_100 value: 76.37183333333333 - type: recall_at_1000 value: 91.26183333333333 - type: recall_at_3 value: 40.769916666666674 - type: recall_at_5 value: 46.702333333333335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.749 - type: map_at_10 value: 33.001999999999995 - type: map_at_100 value: 33.891 - type: map_at_1000 value: 33.993 - type: map_at_3 value: 30.703999999999997 - type: map_at_5 value: 31.959 - type: mrr_at_1 value: 28.834 - type: mrr_at_10 value: 35.955 - type: mrr_at_100 value: 36.709 - type: mrr_at_1000 value: 36.779 - type: mrr_at_3 value: 33.947 - type: mrr_at_5 value: 35.089 - type: ndcg_at_1 value: 28.834 - type: ndcg_at_10 value: 37.329 - type: ndcg_at_100 value: 41.79 - type: ndcg_at_1000 value: 44.169000000000004 - type: ndcg_at_3 value: 33.184999999999995 - type: ndcg_at_5 value: 35.107 - type: precision_at_1 value: 28.834 - type: precision_at_10 value: 5.7669999999999995 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.213000000000001 - type: precision_at_5 value: 9.754999999999999 - type: recall_at_1 value: 25.749 - type: recall_at_10 value: 47.791 - type: recall_at_100 value: 68.255 - type: recall_at_1000 value: 85.749 - type: recall_at_3 value: 36.199 - type: recall_at_5 value: 41.071999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.777 - type: map_at_10 value: 25.201 - type: map_at_100 value: 26.423999999999996 - type: map_at_1000 value: 26.544 - type: map_at_3 value: 22.869 - type: map_at_5 value: 24.023 - type: mrr_at_1 value: 21.473 - type: mrr_at_10 value: 29.12 - type: mrr_at_100 value: 30.144 - type: mrr_at_1000 value: 30.215999999999998 - type: mrr_at_3 value: 26.933 - type: mrr_at_5 value: 28.051 - type: ndcg_at_1 value: 21.473 - type: ndcg_at_10 value: 30.003 - type: ndcg_at_100 value: 35.766 - type: ndcg_at_1000 value: 38.501000000000005 - type: ndcg_at_3 value: 25.773000000000003 - type: ndcg_at_5 value: 27.462999999999997 - type: precision_at_1 value: 21.473 - type: precision_at_10 value: 5.482 - type: precision_at_100 value: 0.975 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.205 - type: precision_at_5 value: 8.692 - type: recall_at_1 value: 17.777 - type: recall_at_10 value: 40.582 - type: recall_at_100 value: 66.305 - type: recall_at_1000 value: 85.636 - type: recall_at_3 value: 28.687 - type: recall_at_5 value: 33.089 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.677 - type: map_at_10 value: 36.309000000000005 - type: map_at_100 value: 37.403999999999996 - type: map_at_1000 value: 37.496 - type: map_at_3 value: 33.382 - type: map_at_5 value: 34.98 - type: mrr_at_1 value: 31.343 - type: mrr_at_10 value: 40.549 - type: mrr_at_100 value: 41.342 - type: mrr_at_1000 value: 41.397 - type: mrr_at_3 value: 38.029 - type: mrr_at_5 value: 39.451 - type: ndcg_at_1 value: 31.343 - type: ndcg_at_10 value: 42.1 - type: ndcg_at_100 value: 47.089999999999996 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 36.836999999999996 - type: ndcg_at_5 value: 39.21 - type: precision_at_1 value: 31.343 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.0959999999999999 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.915 - type: precision_at_5 value: 11.940000000000001 - type: recall_at_1 value: 26.677 - type: recall_at_10 value: 55.54599999999999 - type: recall_at_100 value: 77.094 - type: recall_at_1000 value: 92.01 - type: recall_at_3 value: 41.191 - type: recall_at_5 value: 47.006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.501 - type: map_at_10 value: 33.102 - type: map_at_100 value: 34.676 - type: map_at_1000 value: 34.888000000000005 - type: map_at_3 value: 29.944 - type: map_at_5 value: 31.613999999999997 - type: mrr_at_1 value: 29.447000000000003 - type: mrr_at_10 value: 37.996 - type: mrr_at_100 value: 38.946 - type: mrr_at_1000 value: 38.995000000000005 - type: mrr_at_3 value: 35.079 - type: mrr_at_5 value: 36.69 - type: ndcg_at_1 value: 29.447000000000003 - type: ndcg_at_10 value: 39.232 - type: ndcg_at_100 value: 45.247 - type: ndcg_at_1000 value: 47.613 - type: ndcg_at_3 value: 33.922999999999995 - type: ndcg_at_5 value: 36.284 - type: precision_at_1 value: 29.447000000000003 - type: precision_at_10 value: 7.648000000000001 - type: precision_at_100 value: 1.516 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 51.18899999999999 - type: recall_at_100 value: 78.437 - type: recall_at_1000 value: 92.842 - type: recall_at_3 value: 35.808 - type: recall_at_5 value: 42.197 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.039 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.275 - type: map_at_1000 value: 31.379 - type: map_at_3 value: 27.98 - type: map_at_5 value: 29.358 - type: mrr_at_1 value: 24.03 - type: mrr_at_10 value: 32.568000000000005 - type: mrr_at_100 value: 33.403 - type: mrr_at_1000 value: 33.475 - type: mrr_at_3 value: 30.436999999999998 - type: mrr_at_5 value: 31.796000000000003 - type: ndcg_at_1 value: 24.03 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 39.668 - type: ndcg_at_1000 value: 42.296 - type: ndcg_at_3 value: 30.709999999999997 - type: ndcg_at_5 value: 33.024 - type: precision_at_1 value: 24.03 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 13.309000000000001 - type: precision_at_5 value: 9.39 - type: recall_at_1 value: 22.039 - type: recall_at_10 value: 47.746 - type: recall_at_100 value: 68.23599999999999 - type: recall_at_1000 value: 87.852 - type: recall_at_3 value: 35.852000000000004 - type: recall_at_5 value: 41.410000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.692999999999998 - type: map_at_10 value: 26.903 - type: map_at_100 value: 28.987000000000002 - type: map_at_1000 value: 29.176999999999996 - type: map_at_3 value: 22.137 - type: map_at_5 value: 24.758 - type: mrr_at_1 value: 35.57 - type: mrr_at_10 value: 47.821999999999996 - type: mrr_at_100 value: 48.608000000000004 - type: mrr_at_1000 value: 48.638999999999996 - type: mrr_at_3 value: 44.452000000000005 - type: mrr_at_5 value: 46.546 - type: ndcg_at_1 value: 35.57 - type: ndcg_at_10 value: 36.567 - type: ndcg_at_100 value: 44.085 - type: ndcg_at_1000 value: 47.24 - type: ndcg_at_3 value: 29.964000000000002 - type: ndcg_at_5 value: 32.511 - type: precision_at_1 value: 35.57 - type: precision_at_10 value: 11.485 - type: precision_at_100 value: 1.9619999999999997 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 22.237000000000002 - type: precision_at_5 value: 17.471999999999998 - type: recall_at_1 value: 15.692999999999998 - type: recall_at_10 value: 43.056 - type: recall_at_100 value: 68.628 - type: recall_at_1000 value: 86.075 - type: recall_at_3 value: 26.918999999999997 - type: recall_at_5 value: 34.14 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.53 - type: map_at_10 value: 20.951 - type: map_at_100 value: 30.136000000000003 - type: map_at_1000 value: 31.801000000000002 - type: map_at_3 value: 15.021 - type: map_at_5 value: 17.471999999999998 - type: mrr_at_1 value: 71.0 - type: mrr_at_10 value: 79.176 - type: mrr_at_100 value: 79.418 - type: mrr_at_1000 value: 79.426 - type: mrr_at_3 value: 78.125 - type: mrr_at_5 value: 78.61200000000001 - type: ndcg_at_1 value: 58.5 - type: ndcg_at_10 value: 44.106 - type: ndcg_at_100 value: 49.268 - type: ndcg_at_1000 value: 56.711999999999996 - type: ndcg_at_3 value: 48.934 - type: ndcg_at_5 value: 45.826 - type: precision_at_1 value: 71.0 - type: precision_at_10 value: 35.0 - type: precision_at_100 value: 11.360000000000001 - type: precision_at_1000 value: 2.046 - type: precision_at_3 value: 52.833 - type: precision_at_5 value: 44.15 - type: recall_at_1 value: 9.53 - type: recall_at_10 value: 26.811 - type: recall_at_100 value: 55.916999999999994 - type: recall_at_1000 value: 79.973 - type: recall_at_3 value: 16.413 - type: recall_at_5 value: 19.980999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.519999999999996 - type: f1 value: 46.36601294761231 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 74.413 - type: map_at_10 value: 83.414 - type: map_at_100 value: 83.621 - type: map_at_1000 value: 83.635 - type: map_at_3 value: 82.337 - type: map_at_5 value: 83.039 - type: mrr_at_1 value: 80.19800000000001 - type: mrr_at_10 value: 87.715 - type: mrr_at_100 value: 87.778 - type: mrr_at_1000 value: 87.779 - type: mrr_at_3 value: 87.106 - type: mrr_at_5 value: 87.555 - type: ndcg_at_1 value: 80.19800000000001 - type: ndcg_at_10 value: 87.182 - type: ndcg_at_100 value: 87.90299999999999 - type: ndcg_at_1000 value: 88.143 - type: ndcg_at_3 value: 85.60600000000001 - type: ndcg_at_5 value: 86.541 - type: precision_at_1 value: 80.19800000000001 - type: precision_at_10 value: 10.531 - type: precision_at_100 value: 1.113 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.933 - type: precision_at_5 value: 20.429 - type: recall_at_1 value: 74.413 - type: recall_at_10 value: 94.363 - type: recall_at_100 value: 97.165 - type: recall_at_1000 value: 98.668 - type: recall_at_3 value: 90.108 - type: recall_at_5 value: 92.52 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.701 - type: map_at_10 value: 37.122 - type: map_at_100 value: 39.178000000000004 - type: map_at_1000 value: 39.326 - type: map_at_3 value: 32.971000000000004 - type: map_at_5 value: 35.332 - type: mrr_at_1 value: 44.753 - type: mrr_at_10 value: 53.452 - type: mrr_at_100 value: 54.198 - type: mrr_at_1000 value: 54.225 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.464 - type: ndcg_at_1 value: 44.753 - type: ndcg_at_10 value: 45.021 - type: ndcg_at_100 value: 52.028 - type: ndcg_at_1000 value: 54.596000000000004 - type: ndcg_at_3 value: 41.622 - type: ndcg_at_5 value: 42.736000000000004 - type: precision_at_1 value: 44.753 - type: precision_at_10 value: 12.284 - type: precision_at_100 value: 1.955 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 27.828999999999997 - type: precision_at_5 value: 20.061999999999998 - type: recall_at_1 value: 22.701 - type: recall_at_10 value: 51.432 - type: recall_at_100 value: 77.009 - type: recall_at_1000 value: 92.511 - type: recall_at_3 value: 37.919000000000004 - type: recall_at_5 value: 44.131 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.189 - type: map_at_10 value: 66.24600000000001 - type: map_at_100 value: 67.098 - type: map_at_1000 value: 67.149 - type: map_at_3 value: 62.684 - type: map_at_5 value: 64.974 - type: mrr_at_1 value: 80.378 - type: mrr_at_10 value: 86.127 - type: mrr_at_100 value: 86.29299999999999 - type: mrr_at_1000 value: 86.297 - type: mrr_at_3 value: 85.31400000000001 - type: mrr_at_5 value: 85.858 - type: ndcg_at_1 value: 80.378 - type: ndcg_at_10 value: 74.101 - type: ndcg_at_100 value: 76.993 - type: ndcg_at_1000 value: 77.948 - type: ndcg_at_3 value: 69.232 - type: ndcg_at_5 value: 72.04599999999999 - type: precision_at_1 value: 80.378 - type: precision_at_10 value: 15.595999999999998 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 44.884 - type: precision_at_5 value: 29.145 - type: recall_at_1 value: 40.189 - type: recall_at_10 value: 77.981 - type: recall_at_100 value: 89.21 - type: recall_at_1000 value: 95.48299999999999 - type: recall_at_3 value: 67.326 - type: recall_at_5 value: 72.863 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.84599999999999 - type: ap value: 89.4710787567357 - type: f1 value: 92.83752676932258 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.132 - type: map_at_10 value: 35.543 - type: map_at_100 value: 36.702 - type: map_at_1000 value: 36.748999999999995 - type: map_at_3 value: 31.737 - type: map_at_5 value: 33.927 - type: mrr_at_1 value: 23.782 - type: mrr_at_10 value: 36.204 - type: mrr_at_100 value: 37.29 - type: mrr_at_1000 value: 37.330999999999996 - type: mrr_at_3 value: 32.458999999999996 - type: mrr_at_5 value: 34.631 - type: ndcg_at_1 value: 23.782 - type: ndcg_at_10 value: 42.492999999999995 - type: ndcg_at_100 value: 47.985 - type: ndcg_at_1000 value: 49.141 - type: ndcg_at_3 value: 34.748000000000005 - type: ndcg_at_5 value: 38.651 - type: precision_at_1 value: 23.782 - type: precision_at_10 value: 6.665 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.776 - type: precision_at_5 value: 10.84 - type: recall_at_1 value: 23.132 - type: recall_at_10 value: 63.794 - type: recall_at_100 value: 89.027 - type: recall_at_1000 value: 97.807 - type: recall_at_3 value: 42.765 - type: recall_at_5 value: 52.11 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.59188326493388 - type: f1 value: 94.3842594786827 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.49384404924761 - type: f1 value: 59.7580539534629 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.56220578345663 - type: f1 value: 75.27228165561478 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.53463349024884 - type: f1 value: 80.4893958236536 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.56100273484962 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.470380028839607 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.06102792457849 - type: mrr value: 33.30709199672238 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.776999999999999 - type: map_at_10 value: 14.924000000000001 - type: map_at_100 value: 18.955 - type: map_at_1000 value: 20.538999999999998 - type: map_at_3 value: 10.982 - type: map_at_5 value: 12.679000000000002 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.232000000000006 - type: mrr_at_100 value: 57.818999999999996 - type: mrr_at_1000 value: 57.847 - type: mrr_at_3 value: 54.901999999999994 - type: mrr_at_5 value: 56.481 - type: ndcg_at_1 value: 46.594 - type: ndcg_at_10 value: 38.129000000000005 - type: ndcg_at_100 value: 35.54 - type: ndcg_at_1000 value: 44.172 - type: ndcg_at_3 value: 43.025999999999996 - type: ndcg_at_5 value: 41.052 - type: precision_at_1 value: 47.988 - type: precision_at_10 value: 28.111000000000004 - type: precision_at_100 value: 8.929 - type: precision_at_1000 value: 2.185 - type: precision_at_3 value: 40.144000000000005 - type: precision_at_5 value: 35.232 - type: recall_at_1 value: 6.776999999999999 - type: recall_at_10 value: 19.289 - type: recall_at_100 value: 36.359 - type: recall_at_1000 value: 67.54 - type: recall_at_3 value: 11.869 - type: recall_at_5 value: 14.999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.108000000000004 - type: map_at_10 value: 47.126000000000005 - type: map_at_100 value: 48.171 - type: map_at_1000 value: 48.199 - type: map_at_3 value: 42.734 - type: map_at_5 value: 45.362 - type: mrr_at_1 value: 34.936 - type: mrr_at_10 value: 49.571 - type: mrr_at_100 value: 50.345 - type: mrr_at_1000 value: 50.363 - type: mrr_at_3 value: 45.959 - type: mrr_at_5 value: 48.165 - type: ndcg_at_1 value: 34.936 - type: ndcg_at_10 value: 55.028999999999996 - type: ndcg_at_100 value: 59.244 - type: ndcg_at_1000 value: 59.861 - type: ndcg_at_3 value: 46.872 - type: ndcg_at_5 value: 51.217999999999996 - type: precision_at_1 value: 34.936 - type: precision_at_10 value: 9.099 - type: precision_at_100 value: 1.145 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.456 - type: precision_at_5 value: 15.411 - type: recall_at_1 value: 31.108000000000004 - type: recall_at_10 value: 76.53999999999999 - type: recall_at_100 value: 94.39 - type: recall_at_1000 value: 98.947 - type: recall_at_3 value: 55.572 - type: recall_at_5 value: 65.525 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.56400000000001 - type: map_at_10 value: 85.482 - type: map_at_100 value: 86.114 - type: map_at_1000 value: 86.13 - type: map_at_3 value: 82.607 - type: map_at_5 value: 84.405 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.304 - type: mrr_at_100 value: 88.399 - type: mrr_at_1000 value: 88.399 - type: mrr_at_3 value: 87.37 - type: mrr_at_5 value: 88.024 - type: ndcg_at_1 value: 82.45 - type: ndcg_at_10 value: 89.06500000000001 - type: ndcg_at_100 value: 90.232 - type: ndcg_at_1000 value: 90.305 - type: ndcg_at_3 value: 86.375 - type: ndcg_at_5 value: 87.85300000000001 - type: precision_at_1 value: 82.45 - type: precision_at_10 value: 13.486999999999998 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.813 - type: precision_at_5 value: 24.773999999999997 - type: recall_at_1 value: 71.56400000000001 - type: recall_at_10 value: 95.812 - type: recall_at_100 value: 99.7 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 87.966 - type: recall_at_5 value: 92.268 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.241876648614145 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.66212576446223 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.308 - type: map_at_10 value: 13.803 - type: map_at_100 value: 16.176 - type: map_at_1000 value: 16.561 - type: map_at_3 value: 9.761000000000001 - type: map_at_5 value: 11.802 - type: mrr_at_1 value: 26.200000000000003 - type: mrr_at_10 value: 37.621 - type: mrr_at_100 value: 38.767 - type: mrr_at_1000 value: 38.815 - type: mrr_at_3 value: 34.117 - type: mrr_at_5 value: 36.107 - type: ndcg_at_1 value: 26.200000000000003 - type: ndcg_at_10 value: 22.64 - type: ndcg_at_100 value: 31.567 - type: ndcg_at_1000 value: 37.623 - type: ndcg_at_3 value: 21.435000000000002 - type: ndcg_at_5 value: 18.87 - type: precision_at_1 value: 26.200000000000003 - type: precision_at_10 value: 11.74 - type: precision_at_100 value: 2.465 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 20.033 - type: precision_at_5 value: 16.64 - type: recall_at_1 value: 5.308 - type: recall_at_10 value: 23.794999999999998 - type: recall_at_100 value: 50.015 - type: recall_at_1000 value: 79.283 - type: recall_at_3 value: 12.178 - type: recall_at_5 value: 16.882 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.93231134675553 - type: cos_sim_spearman value: 81.68319292603205 - type: euclidean_pearson value: 81.8396814380367 - type: euclidean_spearman value: 81.24641903349945 - type: manhattan_pearson value: 81.84698799204274 - type: manhattan_spearman value: 81.24269997904105 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.73241671587446 - type: cos_sim_spearman value: 79.05091082971826 - type: euclidean_pearson value: 83.91146869578044 - type: euclidean_spearman value: 79.87978465370936 - type: manhattan_pearson value: 83.90888338917678 - type: manhattan_spearman value: 79.87482848584241 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.14970731146177 - type: cos_sim_spearman value: 86.37363490084627 - type: euclidean_pearson value: 83.02154218530433 - type: euclidean_spearman value: 83.80258761957367 - type: manhattan_pearson value: 83.01664495119347 - type: manhattan_spearman value: 83.77567458007952 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.40474139886784 - type: cos_sim_spearman value: 82.77768789165984 - type: euclidean_pearson value: 80.7065877443695 - type: euclidean_spearman value: 81.375940662505 - type: manhattan_pearson value: 80.6507552270278 - type: manhattan_spearman value: 81.32782179098741 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.08585968722274 - type: cos_sim_spearman value: 88.03110031451399 - type: euclidean_pearson value: 85.74012019602384 - type: euclidean_spearman value: 86.13592849438209 - type: manhattan_pearson value: 85.74404842369206 - type: manhattan_spearman value: 86.14492318960154 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.95069052788875 - type: cos_sim_spearman value: 86.4867991595147 - type: euclidean_pearson value: 84.31013325754635 - type: euclidean_spearman value: 85.01529258006482 - type: manhattan_pearson value: 84.26995570085374 - type: manhattan_spearman value: 84.96982104986162 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.54617647971897 - type: cos_sim_spearman value: 87.49834181751034 - type: euclidean_pearson value: 86.01015322577122 - type: euclidean_spearman value: 84.63362652063199 - type: manhattan_pearson value: 86.13807574475706 - type: manhattan_spearman value: 84.7772370721132 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.20047755786615 - type: cos_sim_spearman value: 67.05324077987636 - type: euclidean_pearson value: 66.91930642976601 - type: euclidean_spearman value: 65.21491856099105 - type: manhattan_pearson value: 66.78756851976624 - type: manhattan_spearman value: 65.12356257740728 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.19852871539686 - type: cos_sim_spearman value: 87.5161895296395 - type: euclidean_pearson value: 84.59848645207485 - type: euclidean_spearman value: 85.26427328757919 - type: manhattan_pearson value: 84.59747366996524 - type: manhattan_spearman value: 85.24045855146915 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.63320317811032 - type: mrr value: 96.26242947321379 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 60.928000000000004 - type: map_at_10 value: 70.112 - type: map_at_100 value: 70.59299999999999 - type: map_at_1000 value: 70.623 - type: map_at_3 value: 66.846 - type: map_at_5 value: 68.447 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 71.212 - type: mrr_at_100 value: 71.616 - type: mrr_at_1000 value: 71.64500000000001 - type: mrr_at_3 value: 68.77799999999999 - type: mrr_at_5 value: 70.094 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 74.607 - type: ndcg_at_100 value: 76.416 - type: ndcg_at_1000 value: 77.102 - type: ndcg_at_3 value: 69.126 - type: ndcg_at_5 value: 71.41300000000001 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 9.933 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.556 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 60.928000000000004 - type: recall_at_10 value: 87.322 - type: recall_at_100 value: 94.833 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.428 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.86237623762376 - type: cos_sim_ap value: 96.72586477206649 - type: cos_sim_f1 value: 93.01858362631845 - type: cos_sim_precision value: 93.4409687184662 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.78019801980199 - type: dot_ap value: 93.72748205246228 - type: dot_f1 value: 89.04109589041096 - type: dot_precision value: 87.16475095785441 - type: dot_recall value: 91.0 - type: euclidean_accuracy value: 99.85445544554456 - type: euclidean_ap value: 96.6661459876145 - type: euclidean_f1 value: 92.58337481333997 - type: euclidean_precision value: 92.17046580773042 - type: euclidean_recall value: 93.0 - type: manhattan_accuracy value: 99.85445544554456 - type: manhattan_ap value: 96.6883549244056 - type: manhattan_f1 value: 92.57598405580468 - type: manhattan_precision value: 92.25422045680239 - type: manhattan_recall value: 92.9 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.72586477206649 - type: max_f1 value: 93.01858362631845 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.39930057069995 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.96398659903402 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.946944700355395 - type: mrr value: 56.97151398438164 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.541657650692905 - type: cos_sim_spearman value: 31.605804192286303 - type: dot_pearson value: 28.26905996736398 - type: dot_spearman value: 27.864801765851187 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.8870000000000002 - type: map_at_100 value: 9.78 - type: map_at_1000 value: 22.514 - type: map_at_3 value: 0.6669999999999999 - type: map_at_5 value: 1.077 - type: mrr_at_1 value: 82.0 - type: mrr_at_10 value: 89.86699999999999 - type: mrr_at_100 value: 89.86699999999999 - type: mrr_at_1000 value: 89.86699999999999 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 89.667 - type: ndcg_at_1 value: 79.0 - type: ndcg_at_10 value: 74.818 - type: ndcg_at_100 value: 53.715999999999994 - type: ndcg_at_1000 value: 47.082 - type: ndcg_at_3 value: 82.134 - type: ndcg_at_5 value: 79.81899999999999 - type: precision_at_1 value: 82.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 54.48 - type: precision_at_1000 value: 20.518 - type: precision_at_3 value: 87.333 - type: precision_at_5 value: 85.2 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 2.072 - type: recall_at_100 value: 13.013 - type: recall_at_1000 value: 43.462 - type: recall_at_3 value: 0.695 - type: recall_at_5 value: 1.139 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.328 - type: map_at_10 value: 9.795 - type: map_at_100 value: 15.801000000000002 - type: map_at_1000 value: 17.23 - type: map_at_3 value: 4.734 - type: map_at_5 value: 6.644 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 46.902 - type: mrr_at_100 value: 47.495 - type: mrr_at_1000 value: 47.495 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 44.218 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 24.806 - type: ndcg_at_100 value: 36.419000000000004 - type: ndcg_at_1000 value: 47.272999999999996 - type: ndcg_at_3 value: 25.666 - type: ndcg_at_5 value: 25.448999999999998 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 23.061 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.484 - type: precision_at_3 value: 26.531 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.328 - type: recall_at_10 value: 16.524 - type: recall_at_100 value: 47.179 - type: recall_at_1000 value: 81.22200000000001 - type: recall_at_3 value: 5.745 - type: recall_at_5 value: 9.339 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.9142 - type: ap value: 14.335574772555415 - type: f1 value: 54.62839595194111 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.94340690435768 - type: f1 value: 60.286487936731916 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.26597708987974 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.48882398521786 - type: cos_sim_ap value: 79.04326607602204 - type: cos_sim_f1 value: 71.64566826860633 - type: cos_sim_precision value: 70.55512918905092 - type: cos_sim_recall value: 72.77044854881267 - type: dot_accuracy value: 84.19264469213805 - type: dot_ap value: 67.96360043562528 - type: dot_f1 value: 64.06418393006827 - type: dot_precision value: 58.64941898706424 - type: dot_recall value: 70.58047493403694 - type: euclidean_accuracy value: 87.45902127913214 - type: euclidean_ap value: 78.9742237648272 - type: euclidean_f1 value: 71.5553235908142 - type: euclidean_precision value: 70.77955601445535 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.41729749061214 - type: manhattan_ap value: 78.90073137580596 - type: manhattan_f1 value: 71.3942611553533 - type: manhattan_precision value: 68.52705653967483 - type: manhattan_recall value: 74.51187335092348 - type: max_accuracy value: 87.48882398521786 - type: max_ap value: 79.04326607602204 - type: max_f1 value: 71.64566826860633 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.68125897465751 - type: cos_sim_ap value: 85.6003454431979 - type: cos_sim_f1 value: 77.6957163958641 - type: cos_sim_precision value: 73.0110366307807 - type: cos_sim_recall value: 83.02279026793964 - type: dot_accuracy value: 87.7672992587418 - type: dot_ap value: 82.4971301112899 - type: dot_f1 value: 75.90528233151184 - type: dot_precision value: 72.0370626469368 - type: dot_recall value: 80.21250384970742 - type: euclidean_accuracy value: 88.4503434625684 - type: euclidean_ap value: 84.91949884748384 - type: euclidean_f1 value: 76.92365018444684 - type: euclidean_precision value: 74.53245721712759 - type: euclidean_recall value: 79.47336002463813 - type: manhattan_accuracy value: 88.47556952691427 - type: manhattan_ap value: 84.8963689101517 - type: manhattan_f1 value: 76.85901249256395 - type: manhattan_precision value: 74.31693989071039 - type: manhattan_recall value: 79.58115183246073 - type: max_accuracy value: 88.68125897465751 - type: max_ap value: 85.6003454431979 - type: max_f1 value: 77.6957163958641 license: mit language: - en --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

xlnet/xlnet-base-cased

xlnet

transformers

text-generation

--- language: en license: mit datasets: - bookcorpus - wikipedia --- # XLNet (base-sized model) XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/). Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking. ## Intended uses & limitations The model is mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlnet) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2. ## Usage Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import XLNetTokenizer, XLNetModel tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') model = XLNetModel.from_pretrained('xlnet-base-cased') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1906-08237, author = {Zhilin Yang and Zihang Dai and Yiming Yang and Jaime G. Carbonell and Ruslan Salakhutdinov and Quoc V. Le}, title = {XLNet: Generalized Autoregressive Pretraining for Language Understanding}, journal = {CoRR}, volume = {abs/1906.08237}, year = {2019}, url = {http://arxiv.org/abs/1906.08237}, eprinttype = {arXiv}, eprint = {1906.08237}, timestamp = {Mon, 24 Jun 2019 17:28:45 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-08237.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

bvanaken/clinical-assertion-negation-bert

bvanaken

transformers

text-classification

--- language: "en" tags: - bert - medical - clinical - assertion - negation - text-classification widget: - text: "Patient denies [entity] SOB [entity]." --- # Clinical Assertion / Negation Classification BERT ## Model description The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? ](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE. The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/). #### How to use the model You can load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert") model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert") ``` The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it. Example input and inference: ``` input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]." classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer) classification = classifier(input) # [{'label': 'ABSENT', 'score': 0.9842607378959656}] ``` ### Cite When working with the model, please cite our paper as follows: ```bibtex @inproceedings{van-aken-2021-assertion, title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?", author = "van Aken, Betty and Trajanovska, Ivana and Siu, Amy and Mayrdorfer, Manuel and Budde, Klemens and Loeser, Alexander", booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.nlpmc-1.5", doi = "10.18653/v1/2021.nlpmc-1.5" } ```

coqui/XTTS-v2

coqui

coqui

text-to-speech

--- license: other license_name: coqui-public-model-license license_link: https://coqui.ai/cpml library_name: coqui pipeline_tag: text-to-speech widget: - text: "Once when I was six years old I saw a magnificent picture" --- # ⓍTTS ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. There is no need for an excessive amount of training data that spans countless hours. This is the same or similar model to what powers [Coqui Studio](https://coqui.ai/) and [Coqui API](https://docs.coqui.ai/docs). ### Features - Supports 17 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. ### Updates over XTTS-v1 - 2 new languages; Hungarian and Korean - Architectural improvements for speaker conditioning. - Enables the use of multiple speaker references and interpolation between speakers. - Stability improvements. - Better prosody and audio quality across the board. ### Languages XTTS-v2 supports 17 languages: **English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu), Korean (ko) Hindi (hi)**. Stay tuned as we continue to add support for more languages. If you have any language requests, feel free to reach out! ### Code The [code-base](https://github.com/coqui-ai/TTS) supports inference and [fine-tuning](https://tts.readthedocs.io/en/latest/models/xtts.html#training). ### Demo Spaces - [XTTS Space](https://huggingface.co/spaces/coqui/xtts) : You can see how model performs on supported languages, and try with your own reference or microphone input - [XTTS Voice Chat with Mistral or Zephyr](https://huggingface.co/spaces/coqui/voice-chat-with-mistral) : You can experience streaming voice chat with Mistral 7B Instruct or Zephyr 7B Beta | | | | ------------------------------- | --------------------------------------- | | 🐸💬 **CoquiTTS** | [coqui/TTS on Github](https://github.com/coqui-ai/TTS)| | 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/) | 👩‍💻 **Questions** | [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions) | | 🗯 **Community** | [Discord](https://discord.gg/5eXr5seRrv) | ### License This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml). ### Contact Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai). You can also mail us at [email protected]. Using 🐸TTS API: ```python from TTS.api import TTS tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True) # generate speech by cloning a voice using default settings tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", file_path="output.wav", speaker_wav="/path/to/target/speaker.wav", language="en") ``` Using 🐸TTS Command line: ```console tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \ --text "Bugün okula gitmek istemiyorum." \ --speaker_wav /path/to/target/speaker.wav \ --language_idx tr \ --use_cuda true ``` Using the model directly: ```python from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts config = XttsConfig() config.load_json("/path/to/xtts/config.json") model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", eval=True) model.cuda() outputs = model.synthesize( "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", config, speaker_wav="/data/TTS-public/_refclips/3.wav", gpt_cond_len=3, language="en", ) ```

nlpaueb/legal-bert-base-uncased

nlpaueb

transformers

fill-mask

--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. A light-weight model (33% the size of BERT-BASE) pre-trained from scratch on legal data with competitive performance is also available. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! * Part of LEGAL-BERT is a light-weight model pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. ## Models list | Model name | Model Path | Training corpora | | ------------------- | ------------------------------------ | ------------------- | | CONTRACTS-BERT-BASE | `nlpaueb/bert-base-uncased-contracts` | US contracts | | EURLEX-BERT-BASE | `nlpaueb/bert-base-uncased-eurlex` | EU legislation | | ECHR-BERT-BASE | `nlpaueb/bert-base-uncased-echr` | ECHR cases | | LEGAL-BERT-BASE * | `nlpaueb/legal-bert-base-uncased` | All | | LEGAL-BERT-SMALL | `nlpaueb/legal-bert-small-uncased` | All | \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \*\* As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased") model = AutoModel.from_pretrained("nlpaueb/legal-bert-base-uncased") ``` ## Use LEGAL-BERT variants as Language Models | Corpus | Model | Masked token | Predictions | | --------------------------------- | ---------------------------------- | ------------ | ------------ | | | **BERT-BASE-UNCASED** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') | | **CONTRACTS-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') | | **EURLEX-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') | | **ECHR-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') | | **LEGAL-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') | | **LEGAL-BERT-SMALL** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) | Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |

laion/CLIP-ViT-H-14-laion2B-s32B-b79K

laion

open_clip

zero-shot-image-classification

--- license: mit widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-H/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT-H/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/H-14--VmlldzoyNDAxODQ3). # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 78.0 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model. # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

google-bert/bert-large-uncased

google-bert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1886913776397705, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a professional model. [SEP]", 'score': 0.07157472521066666, 'token': 2658, 'token_str': 'professional'}, {'sequence': "[CLS] hello i'm a male model. [SEP]", 'score': 0.04053466394543648, 'token': 3287, 'token_str': 'male'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.03891477733850479, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fitness model. [SEP]", 'score': 0.03038121573626995, 'token': 10516, 'token_str': 'fitness'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased') model = BertModel.from_pretrained("bert-large-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased') model = TFBertModel.from_pretrained("bert-large-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a bartender. [SEP]', 'score': 0.10426565259695053, 'token': 15812, 'token_str': 'bartender'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.10232779383659363, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.06281787157058716, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a lawyer. [SEP]', 'score': 0.050936125218868256, 'token': 5160, 'token_str': 'lawyer'}, {'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.041034240275621414, 'token': 10533, 'token_str': 'carpenter'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.28473711013793945, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.11336520314216614, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a bartender. [SEP]', 'score': 0.09574324637651443, 'token': 15812, 'token_str': 'bartender'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.06351090222597122, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a secretary. [SEP]', 'score': 0.048970773816108704, 'token': 3187, 'token_str': 'secretary'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Uncased (Original) | 91.0/84.3 | 86.05 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

facebook/hubert-large-ls960-ft

facebook

transformers

automatic-speech-recognition

--- language: en datasets: - libri-light - librispeech_asr tags: - speech - audio - automatic-speech-recognition - hf-asr-leaderboard license: apache-2.0 model-index: - name: hubert-large-ls960-ft results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 1.9 --- # Hubert-Large-Finetuned [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine-tuned version of [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k). [Paper](https://arxiv.org/abs/2106.07447) Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed **Abstract** Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert . # Usage The model can be used for automatic-speech-recognition as follows: ```python import torch from transformers import Wav2Vec2Processor, HubertForCTC from datasets import load_dataset processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft") model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1 logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]) # ->"A MAN SAID TO THE UNIVERSE SIR I EXIST" ```

BAAI/bge-large-zh-v1.5

BAAI

sentence-transformers

feature-extraction

--- license: mit language: - zh tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

ggml-org/models

ggml-org

Note: this repo will be removed soon - do not use

kha-white/manga-ocr-base

kha-white

transformers

image-to-text

--- language: ja tags: - image-to-text license: apache-2.0 datasets: - manga109s --- # Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: - both vertical and horizontal text - text with furigana - text overlaid on images - wide variety of fonts and font styles - low quality images Code is available [here](https://github.com/kha-white/manga_ocr).

tner/roberta-large-ontonotes5

tner

transformers

token-classification

--- datasets: - tner/ontonotes5 metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-ontonotes5 results: - task: name: Token Classification type: token-classification dataset: name: tner/ontonotes5 type: tner/ontonotes5 args: tner/ontonotes5 metrics: - name: F1 type: f1 value: 0.908632361399938 - name: Precision type: precision value: 0.905148095909732 - name: Recall type: recall value: 0.9121435551212579 - name: F1 (macro) type: f1_macro value: 0.8265477704565624 - name: Precision (macro) type: precision_macro value: 0.8170668848546687 - name: Recall (macro) type: recall_macro value: 0.8387672780349001 - name: F1 (entity span) type: f1_entity_span value: 0.9284544931640193 - name: Precision (entity span) type: precision_entity_span value: 0.9248942172073342 - name: Recall (entity span) type: recall_entity_span value: 0.9320422848005685 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/roberta-large-ontonotes5 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.908632361399938 - Precision (micro): 0.905148095909732 - Recall (micro): 0.9121435551212579 - F1 (macro): 0.8265477704565624 - Precision (macro): 0.8170668848546687 - Recall (macro): 0.8387672780349001 The per-entity breakdown of the F1 score on the test set are below: - cardinal_number: 0.8605277329025309 - date: 0.872996300863132 - event: 0.7424242424242424 - facility: 0.7732342007434945 - geopolitical_area: 0.9687148323205043 - group: 0.9470588235294117 - language: 0.7499999999999999 - law: 0.6666666666666666 - location: 0.7593582887700535 - money: 0.901098901098901 - ordinal_number: 0.85785536159601 - organization: 0.9227360841872057 - percent: 0.9171428571428571 - person: 0.9556004036326943 - product: 0.7857142857142858 - quantity: 0.7945205479452055 - time: 0.6870588235294116 - work_of_art: 0.7151515151515151 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9039454247544766, 0.9128956119702822] - 95%: [0.9030263216115454, 0.9138350859566045] - F1 (macro): - 90%: [0.9039454247544766, 0.9128956119702822] - 95%: [0.9030263216115454, 0.9138350859566045] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-ontonotes5") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/ontonotes5'] - dataset_split: train - dataset_name: None - local_dataset: None - model: roberta-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 64 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 1 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```

Helsinki-NLP/opus-mt-fr-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-fr-en * source languages: fr * target languages: en * OPUS readme: [fr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdiscussdev2015-enfr.fr.en | 33.1 | 0.580 | | newsdiscusstest2015-enfr.fr.en | 38.7 | 0.614 | | newssyscomb2009.fr.en | 30.3 | 0.569 | | news-test2008.fr.en | 26.2 | 0.542 | | newstest2009.fr.en | 30.2 | 0.570 | | newstest2010.fr.en | 32.2 | 0.590 | | newstest2011.fr.en | 33.0 | 0.597 | | newstest2012.fr.en | 32.8 | 0.591 | | newstest2013.fr.en | 33.9 | 0.591 | | newstest2014-fren.fr.en | 37.8 | 0.633 | | Tatoeba.fr.en | 57.5 | 0.720 |

LanguageBind/LanguageBind_Video_merge

LanguageBind

transformers

zero-shot-image-classification

--- license: mit --- <p align="center"> <img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> ## 📰 News * **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy&noteId=OgsxQxAleA). * **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo). * **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months. * **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results). * **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md). * **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo). * **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training. * **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data. * **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders. * **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)! * **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub! * **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described. * **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind). * **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights ### 💡 High performance, but NO intermediate modality required LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ⚡️ A multimodal, fully aligned and voluminous dataset We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### 🔥 Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language. ## 🤗 Demo * **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. ```bash python gradio_app.py ``` * **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## 🛠️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/LanguageBind cd LanguageBind pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt ``` ## 🐳 Model Zoo The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version. You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td> </tr> <tr align="center"> <td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td> </tr> <tr align="center"> <td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td> </tr> <tr align="center"> <td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td> </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td> </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## 🤖 API **We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in [assets](assets) to quickly see how languagebind works. ```python import torch from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer if __name__ == '__main__': device = 'cuda:0' device = torch.device(device) clip_type = { 'video': 'LanguageBind_Video_FT', # also LanguageBind_Video 'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio 'thermal': 'LanguageBind_Thermal', 'image': 'LanguageBind_Image', 'depth': 'LanguageBind_Depth', } model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir') model = model.to(device) model.eval() pretrained_ckpt = f'lb203/LanguageBind_Image' tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir') modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()} image = ['assets/image/0.jpg', 'assets/image/1.jpg'] audio = ['assets/audio/0.wav', 'assets/audio/1.wav'] video = ['assets/video/0.mp4', 'assets/video/1.mp4'] depth = ['assets/depth/0.png', 'assets/depth/1.png'] thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg'] language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.'] inputs = { 'image': to_device(modality_transform['image'](image), device), 'video': to_device(modality_transform['video'](video), device), 'audio': to_device(modality_transform['audio'](audio), device), 'depth': to_device(modality_transform['depth'](depth), device), 'thermal': to_device(modality_transform['thermal'](thermal), device), } inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length', truncation=True, return_tensors='pt'), device) with torch.no_grad(): embeddings = model(inputs) print("Video x Text: \n", torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Image x Text: \n", torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Depth x Text: \n", torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Audio x Text: \n", torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Thermal x Text: \n", torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) ``` Then returns the following result. ```bash Video x Text: [[9.9989331e-01 1.0667283e-04] [1.3255903e-03 9.9867439e-01]] Image x Text: [[9.9990666e-01 9.3292067e-05] [4.6132666e-08 1.0000000e+00]] Depth x Text: [[0.9954276 0.00457235] [0.12042473 0.8795753 ]] Audio x Text: [[0.97634876 0.02365119] [0.02917843 0.97082156]] Thermal x Text: [[0.9482511 0.0517489 ] [0.48746133 0.5125386 ]] ``` ### Emergency zero-shot Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use. ```python print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) ``` Then, you will get: ``` Video x Audio: [[1.0000000e+00 0.0000000e+00] [3.1150486e-32 1.0000000e+00]] Image x Depth: [[1. 0.] [0. 1.]] Image x Thermal: [[1. 0.] [0. 1.]] ``` ### Different branches for X-Language task Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal ```python import torch from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal' model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') thermal_process = LanguageBindThermalProcessor(model.config, tokenizer) model.eval() data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Depth ```python import torch from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Depth' model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') depth_process = LanguageBindDepthProcessor(model.config, tokenizer) model.eval() data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Video ```python import torch from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video' model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') video_process = LanguageBindVideoProcessor(model.config, tokenizer) model.eval() data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Audio ```python import torch from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio' model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') audio_process = LanguageBindAudioProcessor(model.config, tokenizer) model.eval() data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Image Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities. ```python import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer) model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` ## 💥 VIDAL-10M The datasets is in [DATASETS.md](DATASETS.md). ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework. * [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework. * [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images. * [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images. ## 🔒 License * The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @misc{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan}, year={2023}, eprint={2310.01852}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" /> </a>

microsoft/table-transformer-detection

microsoft

transformers

object-detection

--- license: mit widget: - src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png example_title: Invoice --- # Table Transformer (fine-tuned for Table Detection) Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. ## Usage You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.

thenlper/gte-small

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-small results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.22388059701493 - type: ap value: 36.09895941426988 - type: f1 value: 67.3205651539195 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.81894999999999 - type: ap value: 88.5240138417305 - type: f1 value: 91.80367382706962 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.032 - type: f1 value: 47.4490665674719 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.604 - type: map_at_100 value: 47.535 - type: map_at_1000 value: 47.538000000000004 - type: map_at_3 value: 41.833 - type: map_at_5 value: 44.61 - type: mrr_at_1 value: 31.223 - type: mrr_at_10 value: 46.794000000000004 - type: mrr_at_100 value: 47.725 - type: mrr_at_1000 value: 47.727000000000004 - type: mrr_at_3 value: 42.07 - type: mrr_at_5 value: 44.812000000000005 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 55.440999999999995 - type: ndcg_at_100 value: 59.134 - type: ndcg_at_1000 value: 59.199 - type: ndcg_at_3 value: 45.599000000000004 - type: ndcg_at_5 value: 50.637 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.364 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.848000000000003 - type: precision_at_5 value: 13.77 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 83.64200000000001 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 56.543 - type: recall_at_5 value: 68.848 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.90178078197678 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.25728393431922 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.720297062897764 - type: mrr value: 75.24139295607439 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.43527309184616 - type: cos_sim_spearman value: 88.17128615100206 - type: euclidean_pearson value: 87.89922623089282 - type: euclidean_spearman value: 87.96104039655451 - type: manhattan_pearson value: 87.9818290932077 - type: manhattan_spearman value: 88.00923426576885 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.0844155844156 - type: f1 value: 84.01485017302213 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.36574769259432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.4857033165287 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.261 - type: map_at_10 value: 42.419000000000004 - type: map_at_100 value: 43.927 - type: map_at_1000 value: 44.055 - type: map_at_3 value: 38.597 - type: map_at_5 value: 40.701 - type: mrr_at_1 value: 36.91 - type: mrr_at_10 value: 48.02 - type: mrr_at_100 value: 48.658 - type: mrr_at_1000 value: 48.708 - type: mrr_at_3 value: 44.945 - type: mrr_at_5 value: 46.705000000000005 - type: ndcg_at_1 value: 36.91 - type: ndcg_at_10 value: 49.353 - type: ndcg_at_100 value: 54.456 - type: ndcg_at_1000 value: 56.363 - type: ndcg_at_3 value: 43.483 - type: ndcg_at_5 value: 46.150999999999996 - type: precision_at_1 value: 36.91 - type: precision_at_10 value: 9.700000000000001 - type: precision_at_100 value: 1.557 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.078 - type: precision_at_5 value: 15.421999999999999 - type: recall_at_1 value: 30.261 - type: recall_at_10 value: 63.242 - type: recall_at_100 value: 84.09100000000001 - type: recall_at_1000 value: 96.143 - type: recall_at_3 value: 46.478 - type: recall_at_5 value: 53.708 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.145 - type: map_at_10 value: 40.996 - type: map_at_100 value: 42.266999999999996 - type: map_at_1000 value: 42.397 - type: map_at_3 value: 38.005 - type: map_at_5 value: 39.628 - type: mrr_at_1 value: 38.344 - type: mrr_at_10 value: 46.827000000000005 - type: mrr_at_100 value: 47.446 - type: mrr_at_1000 value: 47.489 - type: mrr_at_3 value: 44.448 - type: mrr_at_5 value: 45.747 - type: ndcg_at_1 value: 38.344 - type: ndcg_at_10 value: 46.733000000000004 - type: ndcg_at_100 value: 51.103 - type: ndcg_at_1000 value: 53.075 - type: ndcg_at_3 value: 42.366 - type: ndcg_at_5 value: 44.242 - type: precision_at_1 value: 38.344 - type: precision_at_10 value: 8.822000000000001 - type: precision_at_100 value: 1.417 - type: precision_at_1000 value: 0.187 - type: precision_at_3 value: 20.403 - type: precision_at_5 value: 14.306 - type: recall_at_1 value: 31.145 - type: recall_at_10 value: 56.909 - type: recall_at_100 value: 75.274 - type: recall_at_1000 value: 87.629 - type: recall_at_3 value: 43.784 - type: recall_at_5 value: 49.338 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.83 - type: map_at_10 value: 51.553000000000004 - type: map_at_100 value: 52.581 - type: map_at_1000 value: 52.638 - type: map_at_3 value: 48.112 - type: map_at_5 value: 50.095 - type: mrr_at_1 value: 44.513999999999996 - type: mrr_at_10 value: 54.998000000000005 - type: mrr_at_100 value: 55.650999999999996 - type: mrr_at_1000 value: 55.679 - type: mrr_at_3 value: 52.602000000000004 - type: mrr_at_5 value: 53.931 - type: ndcg_at_1 value: 44.513999999999996 - type: ndcg_at_10 value: 57.67400000000001 - type: ndcg_at_100 value: 61.663999999999994 - type: ndcg_at_1000 value: 62.743 - type: ndcg_at_3 value: 51.964 - type: ndcg_at_5 value: 54.773 - type: precision_at_1 value: 44.513999999999996 - type: precision_at_10 value: 9.423 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 23.323 - type: precision_at_5 value: 16.163 - type: recall_at_1 value: 38.83 - type: recall_at_10 value: 72.327 - type: recall_at_100 value: 89.519 - type: recall_at_1000 value: 97.041 - type: recall_at_3 value: 57.206 - type: recall_at_5 value: 63.88399999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.484 - type: map_at_10 value: 34.527 - type: map_at_100 value: 35.661 - type: map_at_1000 value: 35.739 - type: map_at_3 value: 32.199 - type: map_at_5 value: 33.632 - type: mrr_at_1 value: 27.458 - type: mrr_at_10 value: 36.543 - type: mrr_at_100 value: 37.482 - type: mrr_at_1000 value: 37.543 - type: mrr_at_3 value: 34.256 - type: mrr_at_5 value: 35.618 - type: ndcg_at_1 value: 27.458 - type: ndcg_at_10 value: 39.396 - type: ndcg_at_100 value: 44.742 - type: ndcg_at_1000 value: 46.708 - type: ndcg_at_3 value: 34.817 - type: ndcg_at_5 value: 37.247 - type: precision_at_1 value: 27.458 - type: precision_at_10 value: 5.976999999999999 - type: precision_at_100 value: 0.907 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 14.878 - type: precision_at_5 value: 10.35 - type: recall_at_1 value: 25.484 - type: recall_at_10 value: 52.317 - type: recall_at_100 value: 76.701 - type: recall_at_1000 value: 91.408 - type: recall_at_3 value: 40.043 - type: recall_at_5 value: 45.879 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.719 - type: map_at_10 value: 25.269000000000002 - type: map_at_100 value: 26.442 - type: map_at_1000 value: 26.557 - type: map_at_3 value: 22.56 - type: map_at_5 value: 24.082 - type: mrr_at_1 value: 20.896 - type: mrr_at_10 value: 29.982999999999997 - type: mrr_at_100 value: 30.895 - type: mrr_at_1000 value: 30.961 - type: mrr_at_3 value: 27.239 - type: mrr_at_5 value: 28.787000000000003 - type: ndcg_at_1 value: 20.896 - type: ndcg_at_10 value: 30.814000000000004 - type: ndcg_at_100 value: 36.418 - type: ndcg_at_1000 value: 39.182 - type: ndcg_at_3 value: 25.807999999999996 - type: ndcg_at_5 value: 28.143 - type: precision_at_1 value: 20.896 - type: precision_at_10 value: 5.821 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.562000000000001 - type: precision_at_5 value: 9.254 - type: recall_at_1 value: 16.719 - type: recall_at_10 value: 43.155 - type: recall_at_100 value: 67.831 - type: recall_at_1000 value: 87.617 - type: recall_at_3 value: 29.259 - type: recall_at_5 value: 35.260999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.398999999999997 - type: map_at_10 value: 39.876 - type: map_at_100 value: 41.205999999999996 - type: map_at_1000 value: 41.321999999999996 - type: map_at_3 value: 36.588 - type: map_at_5 value: 38.538 - type: mrr_at_1 value: 35.9 - type: mrr_at_10 value: 45.528 - type: mrr_at_100 value: 46.343 - type: mrr_at_1000 value: 46.388 - type: mrr_at_3 value: 42.862 - type: mrr_at_5 value: 44.440000000000005 - type: ndcg_at_1 value: 35.9 - type: ndcg_at_10 value: 45.987 - type: ndcg_at_100 value: 51.370000000000005 - type: ndcg_at_1000 value: 53.400000000000006 - type: ndcg_at_3 value: 40.841 - type: ndcg_at_5 value: 43.447 - type: precision_at_1 value: 35.9 - type: precision_at_10 value: 8.393 - type: precision_at_100 value: 1.283 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 19.538 - type: precision_at_5 value: 13.975000000000001 - type: recall_at_1 value: 29.398999999999997 - type: recall_at_10 value: 58.361 - type: recall_at_100 value: 81.081 - type: recall_at_1000 value: 94.004 - type: recall_at_3 value: 43.657000000000004 - type: recall_at_5 value: 50.519999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.589 - type: map_at_10 value: 31.608999999999998 - type: map_at_100 value: 33.128 - type: map_at_1000 value: 33.247 - type: map_at_3 value: 28.671999999999997 - type: map_at_5 value: 30.233999999999998 - type: mrr_at_1 value: 26.712000000000003 - type: mrr_at_10 value: 36.713 - type: mrr_at_100 value: 37.713 - type: mrr_at_1000 value: 37.771 - type: mrr_at_3 value: 34.075 - type: mrr_at_5 value: 35.451 - type: ndcg_at_1 value: 26.712000000000003 - type: ndcg_at_10 value: 37.519999999999996 - type: ndcg_at_100 value: 43.946000000000005 - type: ndcg_at_1000 value: 46.297 - type: ndcg_at_3 value: 32.551 - type: ndcg_at_5 value: 34.660999999999994 - type: precision_at_1 value: 26.712000000000003 - type: precision_at_10 value: 7.066 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 15.906 - type: precision_at_5 value: 11.437999999999999 - type: recall_at_1 value: 21.589 - type: recall_at_10 value: 50.090999999999994 - type: recall_at_100 value: 77.43900000000001 - type: recall_at_1000 value: 93.35900000000001 - type: recall_at_3 value: 36.028999999999996 - type: recall_at_5 value: 41.698 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.121666666666663 - type: map_at_10 value: 34.46258333333334 - type: map_at_100 value: 35.710499999999996 - type: map_at_1000 value: 35.82691666666666 - type: map_at_3 value: 31.563249999999996 - type: map_at_5 value: 33.189750000000004 - type: mrr_at_1 value: 29.66441666666667 - type: mrr_at_10 value: 38.5455 - type: mrr_at_100 value: 39.39566666666667 - type: mrr_at_1000 value: 39.45325 - type: mrr_at_3 value: 36.003333333333345 - type: mrr_at_5 value: 37.440916666666666 - type: ndcg_at_1 value: 29.66441666666667 - type: ndcg_at_10 value: 39.978416666666675 - type: ndcg_at_100 value: 45.278666666666666 - type: ndcg_at_1000 value: 47.52275 - type: ndcg_at_3 value: 35.00058333333334 - type: ndcg_at_5 value: 37.34908333333333 - type: precision_at_1 value: 29.66441666666667 - type: precision_at_10 value: 7.094500000000001 - type: precision_at_100 value: 1.1523333333333332 - type: precision_at_1000 value: 0.15358333333333332 - type: precision_at_3 value: 16.184166666666663 - type: precision_at_5 value: 11.6005 - type: recall_at_1 value: 25.121666666666663 - type: recall_at_10 value: 52.23975000000001 - type: recall_at_100 value: 75.48408333333333 - type: recall_at_1000 value: 90.95316666666668 - type: recall_at_3 value: 38.38458333333333 - type: recall_at_5 value: 44.39933333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.569000000000003 - type: map_at_10 value: 30.389 - type: map_at_100 value: 31.396 - type: map_at_1000 value: 31.493 - type: map_at_3 value: 28.276 - type: map_at_5 value: 29.459000000000003 - type: mrr_at_1 value: 26.534000000000002 - type: mrr_at_10 value: 33.217999999999996 - type: mrr_at_100 value: 34.054 - type: mrr_at_1000 value: 34.12 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.330999999999996 - type: ndcg_at_1 value: 26.534000000000002 - type: ndcg_at_10 value: 34.608 - type: ndcg_at_100 value: 39.391999999999996 - type: ndcg_at_1000 value: 41.837999999999994 - type: ndcg_at_3 value: 30.564999999999998 - type: ndcg_at_5 value: 32.509 - type: precision_at_1 value: 26.534000000000002 - type: precision_at_10 value: 5.414 - type: precision_at_100 value: 0.847 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.202 - type: recall_at_1 value: 23.569000000000003 - type: recall_at_10 value: 44.896 - type: recall_at_100 value: 66.476 - type: recall_at_1000 value: 84.548 - type: recall_at_3 value: 33.79 - type: recall_at_5 value: 38.512 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.36 - type: map_at_10 value: 23.57 - type: map_at_100 value: 24.698999999999998 - type: map_at_1000 value: 24.834999999999997 - type: map_at_3 value: 21.093 - type: map_at_5 value: 22.418 - type: mrr_at_1 value: 19.718 - type: mrr_at_10 value: 27.139999999999997 - type: mrr_at_100 value: 28.097 - type: mrr_at_1000 value: 28.177999999999997 - type: mrr_at_3 value: 24.805 - type: mrr_at_5 value: 26.121 - type: ndcg_at_1 value: 19.718 - type: ndcg_at_10 value: 28.238999999999997 - type: ndcg_at_100 value: 33.663 - type: ndcg_at_1000 value: 36.763 - type: ndcg_at_3 value: 23.747 - type: ndcg_at_5 value: 25.796000000000003 - type: precision_at_1 value: 19.718 - type: precision_at_10 value: 5.282 - type: precision_at_100 value: 0.9390000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.264000000000001 - type: precision_at_5 value: 8.341 - type: recall_at_1 value: 16.36 - type: recall_at_10 value: 38.669 - type: recall_at_100 value: 63.184 - type: recall_at_1000 value: 85.33800000000001 - type: recall_at_3 value: 26.214 - type: recall_at_5 value: 31.423000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.618999999999996 - type: map_at_10 value: 34.361999999999995 - type: map_at_100 value: 35.534 - type: map_at_1000 value: 35.634 - type: map_at_3 value: 31.402 - type: map_at_5 value: 32.815 - type: mrr_at_1 value: 30.037000000000003 - type: mrr_at_10 value: 38.284 - type: mrr_at_100 value: 39.141999999999996 - type: mrr_at_1000 value: 39.2 - type: mrr_at_3 value: 35.603 - type: mrr_at_5 value: 36.867 - type: ndcg_at_1 value: 30.037000000000003 - type: ndcg_at_10 value: 39.87 - type: ndcg_at_100 value: 45.243 - type: ndcg_at_1000 value: 47.507 - type: ndcg_at_3 value: 34.371 - type: ndcg_at_5 value: 36.521 - type: precision_at_1 value: 30.037000000000003 - type: precision_at_10 value: 6.819 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.392 - type: precision_at_5 value: 10.821 - type: recall_at_1 value: 25.618999999999996 - type: recall_at_10 value: 52.869 - type: recall_at_100 value: 76.395 - type: recall_at_1000 value: 92.19500000000001 - type: recall_at_3 value: 37.943 - type: recall_at_5 value: 43.342999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.283 - type: map_at_10 value: 32.155 - type: map_at_100 value: 33.724 - type: map_at_1000 value: 33.939 - type: map_at_3 value: 29.018 - type: map_at_5 value: 30.864000000000004 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.632 - type: mrr_at_100 value: 37.606 - type: mrr_at_1000 value: 37.671 - type: mrr_at_3 value: 33.992 - type: mrr_at_5 value: 35.613 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 38.024 - type: ndcg_at_100 value: 44.292 - type: ndcg_at_1000 value: 46.818 - type: ndcg_at_3 value: 32.965 - type: ndcg_at_5 value: 35.562 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.352 - type: precision_at_100 value: 1.514 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_3 value: 15.481 - type: precision_at_5 value: 11.542 - type: recall_at_1 value: 23.283 - type: recall_at_10 value: 49.756 - type: recall_at_100 value: 78.05 - type: recall_at_1000 value: 93.854 - type: recall_at_3 value: 35.408 - type: recall_at_5 value: 42.187000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.201999999999998 - type: map_at_10 value: 26.826 - type: map_at_100 value: 27.961000000000002 - type: map_at_1000 value: 28.066999999999997 - type: map_at_3 value: 24.237000000000002 - type: map_at_5 value: 25.811 - type: mrr_at_1 value: 20.887 - type: mrr_at_10 value: 28.660000000000004 - type: mrr_at_100 value: 29.660999999999998 - type: mrr_at_1000 value: 29.731 - type: mrr_at_3 value: 26.155 - type: mrr_at_5 value: 27.68 - type: ndcg_at_1 value: 20.887 - type: ndcg_at_10 value: 31.523 - type: ndcg_at_100 value: 37.055 - type: ndcg_at_1000 value: 39.579 - type: ndcg_at_3 value: 26.529000000000003 - type: ndcg_at_5 value: 29.137 - type: precision_at_1 value: 20.887 - type: precision_at_10 value: 5.065 - type: precision_at_100 value: 0.856 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.399 - type: precision_at_5 value: 8.392 - type: recall_at_1 value: 19.201999999999998 - type: recall_at_10 value: 44.285000000000004 - type: recall_at_100 value: 69.768 - type: recall_at_1000 value: 88.302 - type: recall_at_3 value: 30.804 - type: recall_at_5 value: 37.039 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.244 - type: map_at_10 value: 18.956 - type: map_at_100 value: 20.674 - type: map_at_1000 value: 20.863 - type: map_at_3 value: 15.923000000000002 - type: map_at_5 value: 17.518 - type: mrr_at_1 value: 25.080999999999996 - type: mrr_at_10 value: 35.94 - type: mrr_at_100 value: 36.969 - type: mrr_at_1000 value: 37.013 - type: mrr_at_3 value: 32.617000000000004 - type: mrr_at_5 value: 34.682 - type: ndcg_at_1 value: 25.080999999999996 - type: ndcg_at_10 value: 26.539 - type: ndcg_at_100 value: 33.601 - type: ndcg_at_1000 value: 37.203 - type: ndcg_at_3 value: 21.695999999999998 - type: ndcg_at_5 value: 23.567 - type: precision_at_1 value: 25.080999999999996 - type: precision_at_10 value: 8.143 - type: precision_at_100 value: 1.5650000000000002 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 15.983 - type: precision_at_5 value: 12.417 - type: recall_at_1 value: 11.244 - type: recall_at_10 value: 31.457 - type: recall_at_100 value: 55.92 - type: recall_at_1000 value: 76.372 - type: recall_at_3 value: 19.784 - type: recall_at_5 value: 24.857000000000003 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.595 - type: map_at_10 value: 18.75 - type: map_at_100 value: 26.354 - type: map_at_1000 value: 27.912 - type: map_at_3 value: 13.794 - type: map_at_5 value: 16.021 - type: mrr_at_1 value: 65.75 - type: mrr_at_10 value: 73.837 - type: mrr_at_100 value: 74.22800000000001 - type: mrr_at_1000 value: 74.234 - type: mrr_at_3 value: 72.5 - type: mrr_at_5 value: 73.387 - type: ndcg_at_1 value: 52.625 - type: ndcg_at_10 value: 39.101 - type: ndcg_at_100 value: 43.836000000000006 - type: ndcg_at_1000 value: 51.086 - type: ndcg_at_3 value: 44.229 - type: ndcg_at_5 value: 41.555 - type: precision_at_1 value: 65.75 - type: precision_at_10 value: 30.45 - type: precision_at_100 value: 9.81 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 48.667 - type: precision_at_5 value: 40.8 - type: recall_at_1 value: 8.595 - type: recall_at_10 value: 24.201 - type: recall_at_100 value: 50.096 - type: recall_at_1000 value: 72.677 - type: recall_at_3 value: 15.212 - type: recall_at_5 value: 18.745 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.565 - type: f1 value: 41.49914329345582 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.60000000000001 - type: map_at_10 value: 76.838 - type: map_at_100 value: 77.076 - type: map_at_1000 value: 77.09 - type: map_at_3 value: 75.545 - type: map_at_5 value: 76.39 - type: mrr_at_1 value: 71.707 - type: mrr_at_10 value: 81.514 - type: mrr_at_100 value: 81.64099999999999 - type: mrr_at_1000 value: 81.645 - type: mrr_at_3 value: 80.428 - type: mrr_at_5 value: 81.159 - type: ndcg_at_1 value: 71.707 - type: ndcg_at_10 value: 81.545 - type: ndcg_at_100 value: 82.477 - type: ndcg_at_1000 value: 82.73899999999999 - type: ndcg_at_3 value: 79.292 - type: ndcg_at_5 value: 80.599 - type: precision_at_1 value: 71.707 - type: precision_at_10 value: 10.035 - type: precision_at_100 value: 1.068 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 30.918 - type: precision_at_5 value: 19.328 - type: recall_at_1 value: 66.60000000000001 - type: recall_at_10 value: 91.353 - type: recall_at_100 value: 95.21 - type: recall_at_1000 value: 96.89999999999999 - type: recall_at_3 value: 85.188 - type: recall_at_5 value: 88.52 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.338 - type: map_at_10 value: 31.752000000000002 - type: map_at_100 value: 33.516 - type: map_at_1000 value: 33.694 - type: map_at_3 value: 27.716 - type: map_at_5 value: 29.67 - type: mrr_at_1 value: 38.117000000000004 - type: mrr_at_10 value: 47.323 - type: mrr_at_100 value: 48.13 - type: mrr_at_1000 value: 48.161 - type: mrr_at_3 value: 45.062000000000005 - type: mrr_at_5 value: 46.358 - type: ndcg_at_1 value: 38.117000000000004 - type: ndcg_at_10 value: 39.353 - type: ndcg_at_100 value: 46.044000000000004 - type: ndcg_at_1000 value: 49.083 - type: ndcg_at_3 value: 35.891 - type: ndcg_at_5 value: 36.661 - type: precision_at_1 value: 38.117000000000004 - type: precision_at_10 value: 11.187999999999999 - type: precision_at_100 value: 1.802 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 17.562 - type: recall_at_1 value: 19.338 - type: recall_at_10 value: 45.735 - type: recall_at_100 value: 71.281 - type: recall_at_1000 value: 89.537 - type: recall_at_3 value: 32.525 - type: recall_at_5 value: 37.671 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.995 - type: map_at_10 value: 55.032000000000004 - type: map_at_100 value: 55.86 - type: map_at_1000 value: 55.932 - type: map_at_3 value: 52.125 - type: map_at_5 value: 53.884 - type: mrr_at_1 value: 73.991 - type: mrr_at_10 value: 80.096 - type: mrr_at_100 value: 80.32000000000001 - type: mrr_at_1000 value: 80.331 - type: mrr_at_3 value: 79.037 - type: mrr_at_5 value: 79.719 - type: ndcg_at_1 value: 73.991 - type: ndcg_at_10 value: 63.786 - type: ndcg_at_100 value: 66.78 - type: ndcg_at_1000 value: 68.255 - type: ndcg_at_3 value: 59.501000000000005 - type: ndcg_at_5 value: 61.82299999999999 - type: precision_at_1 value: 73.991 - type: precision_at_10 value: 13.157 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 37.519999999999996 - type: precision_at_5 value: 24.351 - type: recall_at_1 value: 36.995 - type: recall_at_10 value: 65.78699999999999 - type: recall_at_100 value: 77.583 - type: recall_at_1000 value: 87.421 - type: recall_at_3 value: 56.279999999999994 - type: recall_at_5 value: 60.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.80239999999999 - type: ap value: 81.97305141128378 - type: f1 value: 86.76976305549273 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.166 - type: map_at_10 value: 33.396 - type: map_at_100 value: 34.588 - type: map_at_1000 value: 34.637 - type: map_at_3 value: 29.509999999999998 - type: map_at_5 value: 31.719 - type: mrr_at_1 value: 21.762 - type: mrr_at_10 value: 33.969 - type: mrr_at_100 value: 35.099000000000004 - type: mrr_at_1000 value: 35.141 - type: mrr_at_3 value: 30.148000000000003 - type: mrr_at_5 value: 32.324000000000005 - type: ndcg_at_1 value: 21.776999999999997 - type: ndcg_at_10 value: 40.306999999999995 - type: ndcg_at_100 value: 46.068 - type: ndcg_at_1000 value: 47.3 - type: ndcg_at_3 value: 32.416 - type: ndcg_at_5 value: 36.345 - type: precision_at_1 value: 21.776999999999997 - type: precision_at_10 value: 6.433 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.897 - type: precision_at_5 value: 10.324 - type: recall_at_1 value: 21.166 - type: recall_at_10 value: 61.587 - type: recall_at_100 value: 88.251 - type: recall_at_1000 value: 97.727 - type: recall_at_3 value: 40.196 - type: recall_at_5 value: 49.611 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.04605563155496 - type: f1 value: 92.78007303978372 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 69.65116279069767 - type: f1 value: 52.75775172527262 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.34633490248822 - type: f1 value: 68.15345065392562 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.63887020847343 - type: f1 value: 76.08074680233685 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.77933406071333 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.06504927238196 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.20682480490871 - type: mrr value: 33.41462721527003 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.548 - type: map_at_10 value: 13.086999999999998 - type: map_at_100 value: 16.698 - type: map_at_1000 value: 18.151999999999997 - type: map_at_3 value: 9.576 - type: map_at_5 value: 11.175 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 53.635999999999996 - type: mrr_at_100 value: 54.228 - type: mrr_at_1000 value: 54.26499999999999 - type: mrr_at_3 value: 51.754 - type: mrr_at_5 value: 53.086 - type: ndcg_at_1 value: 42.724000000000004 - type: ndcg_at_10 value: 34.769 - type: ndcg_at_100 value: 32.283 - type: ndcg_at_1000 value: 40.843 - type: ndcg_at_3 value: 39.852 - type: ndcg_at_5 value: 37.858999999999995 - type: precision_at_1 value: 44.272 - type: precision_at_10 value: 26.068 - type: precision_at_100 value: 8.328000000000001 - type: precision_at_1000 value: 2.1 - type: precision_at_3 value: 37.874 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 5.548 - type: recall_at_10 value: 16.936999999999998 - type: recall_at_100 value: 33.72 - type: recall_at_1000 value: 64.348 - type: recall_at_3 value: 10.764999999999999 - type: recall_at_5 value: 13.361 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 28.008 - type: map_at_10 value: 42.675000000000004 - type: map_at_100 value: 43.85 - type: map_at_1000 value: 43.884 - type: map_at_3 value: 38.286 - type: map_at_5 value: 40.78 - type: mrr_at_1 value: 31.518 - type: mrr_at_10 value: 45.015 - type: mrr_at_100 value: 45.924 - type: mrr_at_1000 value: 45.946999999999996 - type: mrr_at_3 value: 41.348 - type: mrr_at_5 value: 43.428 - type: ndcg_at_1 value: 31.489 - type: ndcg_at_10 value: 50.285999999999994 - type: ndcg_at_100 value: 55.291999999999994 - type: ndcg_at_1000 value: 56.05 - type: ndcg_at_3 value: 41.976 - type: ndcg_at_5 value: 46.103 - type: precision_at_1 value: 31.489 - type: precision_at_10 value: 8.456 - type: precision_at_100 value: 1.125 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 19.09 - type: precision_at_5 value: 13.841000000000001 - type: recall_at_1 value: 28.008 - type: recall_at_10 value: 71.21499999999999 - type: recall_at_100 value: 92.99 - type: recall_at_1000 value: 98.578 - type: recall_at_3 value: 49.604 - type: recall_at_5 value: 59.094 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.351 - type: map_at_10 value: 84.163 - type: map_at_100 value: 84.785 - type: map_at_1000 value: 84.801 - type: map_at_3 value: 81.16 - type: map_at_5 value: 83.031 - type: mrr_at_1 value: 80.96 - type: mrr_at_10 value: 87.241 - type: mrr_at_100 value: 87.346 - type: mrr_at_1000 value: 87.347 - type: mrr_at_3 value: 86.25699999999999 - type: mrr_at_5 value: 86.907 - type: ndcg_at_1 value: 80.97 - type: ndcg_at_10 value: 88.017 - type: ndcg_at_100 value: 89.241 - type: ndcg_at_1000 value: 89.34299999999999 - type: ndcg_at_3 value: 85.053 - type: ndcg_at_5 value: 86.663 - type: precision_at_1 value: 80.97 - type: precision_at_10 value: 13.358 - type: precision_at_100 value: 1.525 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.143 - type: precision_at_5 value: 24.451999999999998 - type: recall_at_1 value: 70.351 - type: recall_at_10 value: 95.39800000000001 - type: recall_at_100 value: 99.55199999999999 - type: recall_at_1000 value: 99.978 - type: recall_at_3 value: 86.913 - type: recall_at_5 value: 91.448 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.62406719814139 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 61.386700035141736 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.618 - type: map_at_10 value: 12.920000000000002 - type: map_at_100 value: 15.304 - type: map_at_1000 value: 15.656999999999998 - type: map_at_3 value: 9.187 - type: map_at_5 value: 10.937 - type: mrr_at_1 value: 22.8 - type: mrr_at_10 value: 35.13 - type: mrr_at_100 value: 36.239 - type: mrr_at_1000 value: 36.291000000000004 - type: mrr_at_3 value: 31.917 - type: mrr_at_5 value: 33.787 - type: ndcg_at_1 value: 22.8 - type: ndcg_at_10 value: 21.382 - type: ndcg_at_100 value: 30.257 - type: ndcg_at_1000 value: 36.001 - type: ndcg_at_3 value: 20.43 - type: ndcg_at_5 value: 17.622 - type: precision_at_1 value: 22.8 - type: precision_at_10 value: 11.26 - type: precision_at_100 value: 2.405 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 19.633 - type: precision_at_5 value: 15.68 - type: recall_at_1 value: 4.618 - type: recall_at_10 value: 22.811999999999998 - type: recall_at_100 value: 48.787000000000006 - type: recall_at_1000 value: 76.63799999999999 - type: recall_at_3 value: 11.952 - type: recall_at_5 value: 15.892000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.01529458252244 - type: cos_sim_spearman value: 77.92985224770254 - type: euclidean_pearson value: 81.04251429422487 - type: euclidean_spearman value: 77.92838490549133 - type: manhattan_pearson value: 80.95892251458979 - type: manhattan_spearman value: 77.81028089705941 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.97885282534388 - type: cos_sim_spearman value: 75.1221970851712 - type: euclidean_pearson value: 80.34455956720097 - type: euclidean_spearman value: 74.5894274239938 - type: manhattan_pearson value: 80.38999766325465 - type: manhattan_spearman value: 74.68524557166975 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.95746064915672 - type: cos_sim_spearman value: 85.08683458043946 - type: euclidean_pearson value: 84.56699492836385 - type: euclidean_spearman value: 85.66089116133713 - type: manhattan_pearson value: 84.47553323458541 - type: manhattan_spearman value: 85.56142206781472 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.71377893595067 - type: cos_sim_spearman value: 81.03453291428589 - type: euclidean_pearson value: 82.57136298308613 - type: euclidean_spearman value: 81.15839961890875 - type: manhattan_pearson value: 82.55157879373837 - type: manhattan_spearman value: 81.1540163767054 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.64197832372373 - type: cos_sim_spearman value: 88.31966852492485 - type: euclidean_pearson value: 87.98692129976983 - type: euclidean_spearman value: 88.6247340837856 - type: manhattan_pearson value: 87.90437827826412 - type: manhattan_spearman value: 88.56278787131457 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.84159950146693 - type: cos_sim_spearman value: 83.90678384140168 - type: euclidean_pearson value: 83.19005018860221 - type: euclidean_spearman value: 84.16260415876295 - type: manhattan_pearson value: 83.05030612994494 - type: manhattan_spearman value: 83.99605629718336 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.49935350176666 - type: cos_sim_spearman value: 87.59086606735383 - type: euclidean_pearson value: 88.06537181129983 - type: euclidean_spearman value: 87.6687448086014 - type: manhattan_pearson value: 87.96599131972935 - type: manhattan_spearman value: 87.63295748969642 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.68232799482763 - type: cos_sim_spearman value: 67.99930378085793 - type: euclidean_pearson value: 68.50275360001696 - type: euclidean_spearman value: 67.81588179309259 - type: manhattan_pearson value: 68.5892154749763 - type: manhattan_spearman value: 67.84357259640682 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.37049618406554 - type: cos_sim_spearman value: 85.57014313159492 - type: euclidean_pearson value: 85.57469513908282 - type: euclidean_spearman value: 85.661948135258 - type: manhattan_pearson value: 85.36866831229028 - type: manhattan_spearman value: 85.5043455368843 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.83259065376154 - type: mrr value: 95.58455433455433 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 58.817 - type: map_at_10 value: 68.459 - type: map_at_100 value: 68.951 - type: map_at_1000 value: 68.979 - type: map_at_3 value: 65.791 - type: map_at_5 value: 67.583 - type: mrr_at_1 value: 61.667 - type: mrr_at_10 value: 69.368 - type: mrr_at_100 value: 69.721 - type: mrr_at_1000 value: 69.744 - type: mrr_at_3 value: 67.278 - type: mrr_at_5 value: 68.611 - type: ndcg_at_1 value: 61.667 - type: ndcg_at_10 value: 72.70100000000001 - type: ndcg_at_100 value: 74.928 - type: ndcg_at_1000 value: 75.553 - type: ndcg_at_3 value: 68.203 - type: ndcg_at_5 value: 70.804 - type: precision_at_1 value: 61.667 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.444000000000003 - type: precision_at_5 value: 17.599999999999998 - type: recall_at_1 value: 58.817 - type: recall_at_10 value: 84.789 - type: recall_at_100 value: 95.0 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 72.8 - type: recall_at_5 value: 79.294 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8108910891089 - type: cos_sim_ap value: 95.5743678558349 - type: cos_sim_f1 value: 90.43133366385722 - type: cos_sim_precision value: 89.67551622418878 - type: cos_sim_recall value: 91.2 - type: dot_accuracy value: 99.75841584158415 - type: dot_ap value: 94.00786363627253 - type: dot_f1 value: 87.51910341314316 - type: dot_precision value: 89.20041536863967 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.81485148514851 - type: euclidean_ap value: 95.4752113136905 - type: euclidean_f1 value: 90.44334975369456 - type: euclidean_precision value: 89.126213592233 - type: euclidean_recall value: 91.8 - type: manhattan_accuracy value: 99.81584158415842 - type: manhattan_ap value: 95.5163172682464 - type: manhattan_f1 value: 90.51987767584097 - type: manhattan_precision value: 92.3076923076923 - type: manhattan_recall value: 88.8 - type: max_accuracy value: 99.81584158415842 - type: max_ap value: 95.5743678558349 - type: max_f1 value: 90.51987767584097 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 62.63235986949449 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.334795589585575 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.02955214518782 - type: mrr value: 52.8004838298956 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.63769566275453 - type: cos_sim_spearman value: 30.422379185989335 - type: dot_pearson value: 26.88493071882256 - type: dot_spearman value: 26.505249740971305 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.21 - type: map_at_10 value: 1.654 - type: map_at_100 value: 10.095 - type: map_at_1000 value: 25.808999999999997 - type: map_at_3 value: 0.594 - type: map_at_5 value: 0.9289999999999999 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 87.019 - type: mrr_at_100 value: 87.019 - type: mrr_at_1000 value: 87.019 - type: mrr_at_3 value: 86.333 - type: mrr_at_5 value: 86.733 - type: ndcg_at_1 value: 73.0 - type: ndcg_at_10 value: 66.52900000000001 - type: ndcg_at_100 value: 53.433 - type: ndcg_at_1000 value: 51.324000000000005 - type: ndcg_at_3 value: 72.02199999999999 - type: ndcg_at_5 value: 69.696 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 70.39999999999999 - type: precision_at_100 value: 55.46 - type: precision_at_1000 value: 22.758 - type: precision_at_3 value: 76.667 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.21 - type: recall_at_10 value: 1.8849999999999998 - type: recall_at_100 value: 13.801 - type: recall_at_1000 value: 49.649 - type: recall_at_3 value: 0.632 - type: recall_at_5 value: 1.009 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.797 - type: map_at_10 value: 9.01 - type: map_at_100 value: 14.682 - type: map_at_1000 value: 16.336000000000002 - type: map_at_3 value: 4.546 - type: map_at_5 value: 5.9270000000000005 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 41.156 - type: mrr_at_100 value: 42.392 - type: mrr_at_1000 value: 42.408 - type: mrr_at_3 value: 38.775999999999996 - type: mrr_at_5 value: 40.102 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 22.222 - type: ndcg_at_100 value: 34.405 - type: ndcg_at_1000 value: 46.599000000000004 - type: ndcg_at_3 value: 25.261 - type: ndcg_at_5 value: 22.695999999999998 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.306 - type: precision_at_1000 value: 1.5350000000000001 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 22.857 - type: recall_at_1 value: 1.797 - type: recall_at_10 value: 15.706000000000001 - type: recall_at_100 value: 46.412 - type: recall_at_1000 value: 83.159 - type: recall_at_3 value: 6.1370000000000005 - type: recall_at_5 value: 8.599 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.3302 - type: ap value: 14.169121204575601 - type: f1 value: 54.229345975274235 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 58.22297679683077 - type: f1 value: 58.62984908377875 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.952922428464255 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.68140907194373 - type: cos_sim_ap value: 70.12180123666836 - type: cos_sim_f1 value: 65.77501791258658 - type: cos_sim_precision value: 60.07853403141361 - type: cos_sim_recall value: 72.66490765171504 - type: dot_accuracy value: 81.92167848840674 - type: dot_ap value: 60.49837581423469 - type: dot_f1 value: 58.44186046511628 - type: dot_precision value: 52.24532224532224 - type: dot_recall value: 66.3060686015831 - type: euclidean_accuracy value: 84.73505394289802 - type: euclidean_ap value: 70.3278904593286 - type: euclidean_f1 value: 65.98851124940161 - type: euclidean_precision value: 60.38107752956636 - type: euclidean_recall value: 72.74406332453826 - type: manhattan_accuracy value: 84.73505394289802 - type: manhattan_ap value: 70.00737738537337 - type: manhattan_f1 value: 65.80150784822642 - type: manhattan_precision value: 61.892583120204606 - type: manhattan_recall value: 70.23746701846966 - type: max_accuracy value: 84.73505394289802 - type: max_ap value: 70.3278904593286 - type: max_f1 value: 65.98851124940161 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.44258159661582 - type: cos_sim_ap value: 84.91926704880888 - type: cos_sim_f1 value: 77.07651086632926 - type: cos_sim_precision value: 74.5894554883319 - type: cos_sim_recall value: 79.73514012935017 - type: dot_accuracy value: 85.88116583226608 - type: dot_ap value: 78.9753854779923 - type: dot_f1 value: 72.17757637979255 - type: dot_precision value: 66.80647486729143 - type: dot_recall value: 78.48783492454572 - type: euclidean_accuracy value: 88.5299025885823 - type: euclidean_ap value: 85.08006075642194 - type: euclidean_f1 value: 77.29637336504163 - type: euclidean_precision value: 74.69836253950014 - type: euclidean_recall value: 80.08161379735141 - type: manhattan_accuracy value: 88.55124771995187 - type: manhattan_ap value: 85.00941529932851 - type: manhattan_f1 value: 77.33100233100232 - type: manhattan_precision value: 73.37572573956317 - type: manhattan_recall value: 81.73698798891284 - type: max_accuracy value: 88.55124771995187 - type: max_ap value: 85.08006075642194 - type: max_f1 value: 77.33100233100232 language: - en license: mit --- # gte-small General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small") model = AutoModel.from_pretrained("thenlper/gte-small") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-large') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

LanguageBind/LanguageBind_Image

LanguageBind

transformers

zero-shot-image-classification

--- license: mit --- <p align="center"> <img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> ## 📰 News * **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy&noteId=OgsxQxAleA). * **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo). * **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months. * **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results). * **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md). * **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo). * **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training. * **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data. * **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders. * **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)! * **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub! * **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described. * **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind). * **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights ### 💡 High performance, but NO intermediate modality required LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ⚡️ A multimodal, fully aligned and voluminous dataset We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### 🔥 Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language. ## 🤗 Demo * **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. ```bash python gradio_app.py ``` * **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## 🛠️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/LanguageBind cd LanguageBind pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt ``` ## 🐳 Model Zoo The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version. You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td> </tr> <tr align="center"> <td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td> </tr> <tr align="center"> <td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td> </tr> <tr align="center"> <td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td> </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td> </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## 🤖 API **We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in [assets](assets) to quickly see how languagebind works. ```python import torch from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer if __name__ == '__main__': device = 'cuda:0' device = torch.device(device) clip_type = { 'video': 'LanguageBind_Video_FT', # also LanguageBind_Video 'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio 'thermal': 'LanguageBind_Thermal', 'image': 'LanguageBind_Image', 'depth': 'LanguageBind_Depth', } model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir') model = model.to(device) model.eval() pretrained_ckpt = f'lb203/LanguageBind_Image' tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir') modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()} image = ['assets/image/0.jpg', 'assets/image/1.jpg'] audio = ['assets/audio/0.wav', 'assets/audio/1.wav'] video = ['assets/video/0.mp4', 'assets/video/1.mp4'] depth = ['assets/depth/0.png', 'assets/depth/1.png'] thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg'] language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.'] inputs = { 'image': to_device(modality_transform['image'](image), device), 'video': to_device(modality_transform['video'](video), device), 'audio': to_device(modality_transform['audio'](audio), device), 'depth': to_device(modality_transform['depth'](depth), device), 'thermal': to_device(modality_transform['thermal'](thermal), device), } inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length', truncation=True, return_tensors='pt'), device) with torch.no_grad(): embeddings = model(inputs) print("Video x Text: \n", torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Image x Text: \n", torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Depth x Text: \n", torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Audio x Text: \n", torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Thermal x Text: \n", torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) ``` Then returns the following result. ```bash Video x Text: [[9.9989331e-01 1.0667283e-04] [1.3255903e-03 9.9867439e-01]] Image x Text: [[9.9990666e-01 9.3292067e-05] [4.6132666e-08 1.0000000e+00]] Depth x Text: [[0.9954276 0.00457235] [0.12042473 0.8795753 ]] Audio x Text: [[0.97634876 0.02365119] [0.02917843 0.97082156]] Thermal x Text: [[0.9482511 0.0517489 ] [0.48746133 0.5125386 ]] ``` ### Emergency zero-shot Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use. ```python print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) ``` Then, you will get: ``` Video x Audio: [[1.0000000e+00 0.0000000e+00] [3.1150486e-32 1.0000000e+00]] Image x Depth: [[1. 0.] [0. 1.]] Image x Thermal: [[1. 0.] [0. 1.]] ``` ### Different branches for X-Language task Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal ```python import torch from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal' model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') thermal_process = LanguageBindThermalProcessor(model.config, tokenizer) model.eval() data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Depth ```python import torch from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Depth' model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') depth_process = LanguageBindDepthProcessor(model.config, tokenizer) model.eval() data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Video ```python import torch from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video' model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') video_process = LanguageBindVideoProcessor(model.config, tokenizer) model.eval() data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Audio ```python import torch from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio' model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') audio_process = LanguageBindAudioProcessor(model.config, tokenizer) model.eval() data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Image Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities. ```python import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer) model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` ## 💥 VIDAL-10M The datasets is in [DATASETS.md](DATASETS.md). ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework. * [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework. * [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images. * [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images. ## 🔒 License * The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @misc{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan}, year={2023}, eprint={2310.01852}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" /> </a>

pyannote/embedding

pyannote

pyannote-audio

--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-recognition - speaker-verification - speaker-identification - speaker-embedding datasets: - voxceleb license: mit inference: false extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening." extra_gated_fields: Company/university: text Website: text I plan to use this model for (task, type of audio data, etc): text --- Using this open-source model in production? Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. # 🎹 Speaker embedding Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio/). This model is based on the [canonical x-vector TDNN-based architecture](https://ieeexplore.ieee.org/abstract/document/8461375), but with filter banks replaced with [trainable SincNet features](https://ieeexplore.ieee.org/document/8639585). See [`XVectorSincNet`](https://github.com/pyannote/pyannote-audio/blob/3c988c028dc505c64fe776720372f6fe816b585a/pyannote/audio/models/embedding/xvector.py#L104-L169) architecture for implementation details. ## Basic usage ```python # 1. visit hf.co/pyannote/embedding and accept user conditions # 2. visit hf.co/settings/tokens to create an access token # 3. instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/embedding", use_auth_token="ACCESS_TOKEN_GOES_HERE") ``` ```python from pyannote.audio import Inference inference = Inference(model, window="whole") embedding1 = inference("speaker1.wav") embedding2 = inference("speaker2.wav") # `embeddingX` is (1 x D) numpy array extracted from the file as a whole. from scipy.spatial.distance import cdist distance = cdist(embedding1, embedding2, metric="cosine")[0,0] # `distance` is a `float` describing how dissimilar speakers 1 and 2 are. ``` Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set. This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those. ## Advanced usage ### Running on GPU ```python import torch inference.to(torch.device("cuda")) embedding = inference("audio.wav") ``` ### Extract embedding from an excerpt ```python from pyannote.audio import Inference from pyannote.core import Segment inference = Inference(model, window="whole") excerpt = Segment(13.37, 19.81) embedding = inference.crop("audio.wav", excerpt) # `embedding` is (1 x D) numpy array extracted from the file excerpt. ``` ### Extract embeddings using a sliding window ```python from pyannote.audio import Inference inference = Inference(model, window="sliding", duration=3.0, step=1.0) embeddings = inference("audio.wav") # `embeddings` is a (N x D) pyannote.core.SlidingWindowFeature # `embeddings[i]` is the embedding of the ith position of the # sliding window, i.e. from [i * step, i * step + duration]. ``` ## Citation ```bibtex @inproceedings{Bredin2020, Title = {{pyannote.audio: neural building blocks for speaker diarization}}, Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, Address = {Barcelona, Spain}, Month = {May}, Year = {2020}, } ``` ```bibtex @inproceedings{Coria2020, author="Coria, Juan M. and Bredin, Herv{\'e} and Ghannay, Sahar and Rosset, Sophie", editor="Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena", title="{A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification}", booktitle="Statistical Language and Speech Processing", year="2020", publisher="Springer International Publishing", pages="137--148", isbn="978-3-030-59430-5" } ```

intfloat/e5-large-v2

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: e5-large-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.22388059701493 - type: ap value: 43.20816505595132 - type: f1 value: 73.27811303522058 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.748325 - type: ap value: 90.72534979701297 - type: f1 value: 93.73895874282185 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.612 - type: f1 value: 47.61157345898393 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.208 - type: map_at_100 value: 39.417 - type: map_at_1000 value: 39.428999999999995 - type: map_at_3 value: 33.95 - type: map_at_5 value: 36.329 - type: mrr_at_1 value: 23.755000000000003 - type: mrr_at_10 value: 38.288 - type: mrr_at_100 value: 39.511 - type: mrr_at_1000 value: 39.523 - type: mrr_at_3 value: 34.009 - type: mrr_at_5 value: 36.434 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.417 - type: ndcg_at_100 value: 51.812000000000005 - type: ndcg_at_1000 value: 52.137 - type: ndcg_at_3 value: 37.528 - type: ndcg_at_5 value: 41.81 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.979 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 47.937000000000005 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.546499570522094 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 41.01607489943561 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.616107510107774 - type: mrr value: 72.75106626214661 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.33018094733868 - type: cos_sim_spearman value: 83.60190492611737 - type: euclidean_pearson value: 82.1492450218961 - type: euclidean_spearman value: 82.70308926526991 - type: manhattan_pearson value: 81.93959600076842 - type: manhattan_spearman value: 82.73260801016369 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.54545454545455 - type: f1 value: 84.49582530928923 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.362725540120096 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.849509608178145 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.502999999999997 - type: map_at_10 value: 43.323 - type: map_at_100 value: 44.708999999999996 - type: map_at_1000 value: 44.838 - type: map_at_3 value: 38.987 - type: map_at_5 value: 41.516999999999996 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 49.13 - type: mrr_at_100 value: 49.697 - type: mrr_at_1000 value: 49.741 - type: mrr_at_3 value: 45.804 - type: mrr_at_5 value: 47.842 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 50.266999999999996 - type: ndcg_at_100 value: 54.967 - type: ndcg_at_1000 value: 56.976000000000006 - type: ndcg_at_3 value: 43.823 - type: ndcg_at_5 value: 47.12 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 10.057 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.125 - type: precision_at_5 value: 15.851 - type: recall_at_1 value: 31.502999999999997 - type: recall_at_10 value: 63.715999999999994 - type: recall_at_100 value: 83.61800000000001 - type: recall_at_1000 value: 96.63199999999999 - type: recall_at_3 value: 45.403 - type: recall_at_5 value: 54.481 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.833000000000002 - type: map_at_10 value: 37.330999999999996 - type: map_at_100 value: 38.580999999999996 - type: map_at_1000 value: 38.708 - type: map_at_3 value: 34.713 - type: map_at_5 value: 36.104 - type: mrr_at_1 value: 35.223 - type: mrr_at_10 value: 43.419000000000004 - type: mrr_at_100 value: 44.198 - type: mrr_at_1000 value: 44.249 - type: mrr_at_3 value: 41.614000000000004 - type: mrr_at_5 value: 42.553000000000004 - type: ndcg_at_1 value: 35.223 - type: ndcg_at_10 value: 42.687999999999995 - type: ndcg_at_100 value: 47.447 - type: ndcg_at_1000 value: 49.701 - type: ndcg_at_3 value: 39.162 - type: ndcg_at_5 value: 40.557 - type: precision_at_1 value: 35.223 - type: precision_at_10 value: 7.962 - type: precision_at_100 value: 1.304 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.184999999999999 - type: recall_at_1 value: 27.833000000000002 - type: recall_at_10 value: 51.881 - type: recall_at_100 value: 72.04 - type: recall_at_1000 value: 86.644 - type: recall_at_3 value: 40.778 - type: recall_at_5 value: 45.176 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.175 - type: map_at_10 value: 51.174 - type: map_at_100 value: 52.26499999999999 - type: map_at_1000 value: 52.315999999999995 - type: map_at_3 value: 47.897 - type: map_at_5 value: 49.703 - type: mrr_at_1 value: 43.448 - type: mrr_at_10 value: 54.505 - type: mrr_at_100 value: 55.216 - type: mrr_at_1000 value: 55.242000000000004 - type: mrr_at_3 value: 51.98500000000001 - type: mrr_at_5 value: 53.434000000000005 - type: ndcg_at_1 value: 43.448 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.537 - type: ndcg_at_1000 value: 62.546 - type: ndcg_at_3 value: 51.73799999999999 - type: ndcg_at_5 value: 54.324 - type: precision_at_1 value: 43.448 - type: precision_at_10 value: 9.292 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.218 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.175 - type: recall_at_10 value: 72.00999999999999 - type: recall_at_100 value: 90.155 - type: recall_at_1000 value: 97.257 - type: recall_at_3 value: 57.133 - type: recall_at_5 value: 63.424 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.405 - type: map_at_10 value: 30.043 - type: map_at_100 value: 31.191000000000003 - type: map_at_1000 value: 31.275 - type: map_at_3 value: 27.034000000000002 - type: map_at_5 value: 28.688000000000002 - type: mrr_at_1 value: 24.068 - type: mrr_at_10 value: 31.993 - type: mrr_at_100 value: 32.992 - type: mrr_at_1000 value: 33.050000000000004 - type: mrr_at_3 value: 28.964000000000002 - type: mrr_at_5 value: 30.653000000000002 - type: ndcg_at_1 value: 24.068 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 40.709 - type: ndcg_at_1000 value: 42.855 - type: ndcg_at_3 value: 29.139 - type: ndcg_at_5 value: 32.045 - type: precision_at_1 value: 24.068 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 12.279 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 22.405 - type: recall_at_10 value: 49.391 - type: recall_at_100 value: 74.53699999999999 - type: recall_at_1000 value: 90.605 - type: recall_at_3 value: 33.126 - type: recall_at_5 value: 40.073 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.309999999999999 - type: map_at_10 value: 20.688000000000002 - type: map_at_100 value: 22.022 - type: map_at_1000 value: 22.152 - type: map_at_3 value: 17.954 - type: map_at_5 value: 19.439 - type: mrr_at_1 value: 16.294 - type: mrr_at_10 value: 24.479 - type: mrr_at_100 value: 25.515 - type: mrr_at_1000 value: 25.593 - type: mrr_at_3 value: 21.642 - type: mrr_at_5 value: 23.189999999999998 - type: ndcg_at_1 value: 16.294 - type: ndcg_at_10 value: 25.833000000000002 - type: ndcg_at_100 value: 32.074999999999996 - type: ndcg_at_1000 value: 35.083 - type: ndcg_at_3 value: 20.493 - type: ndcg_at_5 value: 22.949 - type: precision_at_1 value: 16.294 - type: precision_at_10 value: 5.112 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.587000000000001 - type: recall_at_1 value: 13.309999999999999 - type: recall_at_10 value: 37.851 - type: recall_at_100 value: 64.835 - type: recall_at_1000 value: 86.334 - type: recall_at_3 value: 23.493 - type: recall_at_5 value: 29.528 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.857999999999997 - type: map_at_10 value: 35.503 - type: map_at_100 value: 36.957 - type: map_at_1000 value: 37.065 - type: map_at_3 value: 32.275999999999996 - type: map_at_5 value: 34.119 - type: mrr_at_1 value: 31.954 - type: mrr_at_10 value: 40.851 - type: mrr_at_100 value: 41.863 - type: mrr_at_1000 value: 41.900999999999996 - type: mrr_at_3 value: 38.129999999999995 - type: mrr_at_5 value: 39.737 - type: ndcg_at_1 value: 31.954 - type: ndcg_at_10 value: 41.343999999999994 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 49.501 - type: ndcg_at_3 value: 36.047000000000004 - type: ndcg_at_5 value: 38.639 - type: precision_at_1 value: 31.954 - type: precision_at_10 value: 7.68 - type: precision_at_100 value: 1.247 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.132 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 25.857999999999997 - type: recall_at_10 value: 53.43599999999999 - type: recall_at_100 value: 78.82400000000001 - type: recall_at_1000 value: 92.78999999999999 - type: recall_at_3 value: 38.655 - type: recall_at_5 value: 45.216 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.709 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.657 - type: map_at_1000 value: 35.783 - type: map_at_3 value: 31.326999999999998 - type: map_at_5 value: 33.021 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.093 - type: mrr_at_100 value: 39.992 - type: mrr_at_1000 value: 40.056999999999995 - type: mrr_at_3 value: 36.606 - type: mrr_at_5 value: 37.861 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 39.974 - type: ndcg_at_100 value: 45.647999999999996 - type: ndcg_at_1000 value: 48.259 - type: ndcg_at_3 value: 35.028 - type: ndcg_at_5 value: 37.175999999999995 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.363 - type: precision_at_100 value: 1.184 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 16.857 - type: precision_at_5 value: 11.963 - type: recall_at_1 value: 24.709 - type: recall_at_10 value: 52.087 - type: recall_at_100 value: 76.125 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 38.149 - type: recall_at_5 value: 43.984 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.40791666666667 - type: map_at_10 value: 32.458083333333335 - type: map_at_100 value: 33.691916666666664 - type: map_at_1000 value: 33.81191666666666 - type: map_at_3 value: 29.51625 - type: map_at_5 value: 31.168083333333335 - type: mrr_at_1 value: 27.96591666666666 - type: mrr_at_10 value: 36.528583333333344 - type: mrr_at_100 value: 37.404 - type: mrr_at_1000 value: 37.464333333333336 - type: mrr_at_3 value: 33.92883333333333 - type: mrr_at_5 value: 35.41933333333333 - type: ndcg_at_1 value: 27.96591666666666 - type: ndcg_at_10 value: 37.89141666666666 - type: ndcg_at_100 value: 43.23066666666666 - type: ndcg_at_1000 value: 45.63258333333333 - type: ndcg_at_3 value: 32.811249999999994 - type: ndcg_at_5 value: 35.22566666666667 - type: precision_at_1 value: 27.96591666666666 - type: precision_at_10 value: 6.834083333333332 - type: precision_at_100 value: 1.12225 - type: precision_at_1000 value: 0.15241666666666667 - type: precision_at_3 value: 15.264333333333335 - type: precision_at_5 value: 11.039416666666666 - type: recall_at_1 value: 23.40791666666667 - type: recall_at_10 value: 49.927083333333336 - type: recall_at_100 value: 73.44641666666668 - type: recall_at_1000 value: 90.19950000000001 - type: recall_at_3 value: 35.88341666666667 - type: recall_at_5 value: 42.061249999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.592000000000002 - type: map_at_10 value: 26.895999999999997 - type: map_at_100 value: 27.921000000000003 - type: map_at_1000 value: 28.02 - type: map_at_3 value: 24.883 - type: map_at_5 value: 25.812 - type: mrr_at_1 value: 22.698999999999998 - type: mrr_at_10 value: 29.520999999999997 - type: mrr_at_100 value: 30.458000000000002 - type: mrr_at_1000 value: 30.526999999999997 - type: mrr_at_3 value: 27.633000000000003 - type: mrr_at_5 value: 28.483999999999998 - type: ndcg_at_1 value: 22.698999999999998 - type: ndcg_at_10 value: 31.061 - type: ndcg_at_100 value: 36.398 - type: ndcg_at_1000 value: 38.89 - type: ndcg_at_3 value: 27.149 - type: ndcg_at_5 value: 28.627000000000002 - type: precision_at_1 value: 22.698999999999998 - type: precision_at_10 value: 5.106999999999999 - type: precision_at_100 value: 0.857 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 11.963 - type: precision_at_5 value: 8.221 - type: recall_at_1 value: 19.592000000000002 - type: recall_at_10 value: 41.329 - type: recall_at_100 value: 66.094 - type: recall_at_1000 value: 84.511 - type: recall_at_3 value: 30.61 - type: recall_at_5 value: 34.213 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.71 - type: map_at_10 value: 20.965 - type: map_at_100 value: 21.994 - type: map_at_1000 value: 22.133 - type: map_at_3 value: 18.741 - type: map_at_5 value: 19.951 - type: mrr_at_1 value: 18.307000000000002 - type: mrr_at_10 value: 24.66 - type: mrr_at_100 value: 25.540000000000003 - type: mrr_at_1000 value: 25.629 - type: mrr_at_3 value: 22.511 - type: mrr_at_5 value: 23.72 - type: ndcg_at_1 value: 18.307000000000002 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 30.229 - type: ndcg_at_1000 value: 33.623 - type: ndcg_at_3 value: 21.203 - type: ndcg_at_5 value: 23.006999999999998 - type: precision_at_1 value: 18.307000000000002 - type: precision_at_10 value: 4.725 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.14 - type: precision_at_5 value: 7.481 - type: recall_at_1 value: 14.71 - type: recall_at_10 value: 34.087 - type: recall_at_100 value: 57.147999999999996 - type: recall_at_1000 value: 81.777 - type: recall_at_3 value: 22.996 - type: recall_at_5 value: 27.73 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.472 - type: map_at_10 value: 32.699 - type: map_at_100 value: 33.867000000000004 - type: map_at_1000 value: 33.967000000000006 - type: map_at_3 value: 29.718 - type: map_at_5 value: 31.345 - type: mrr_at_1 value: 28.265 - type: mrr_at_10 value: 36.945 - type: mrr_at_100 value: 37.794 - type: mrr_at_1000 value: 37.857 - type: mrr_at_3 value: 34.266000000000005 - type: mrr_at_5 value: 35.768 - type: ndcg_at_1 value: 28.265 - type: ndcg_at_10 value: 38.35 - type: ndcg_at_100 value: 43.739 - type: ndcg_at_1000 value: 46.087 - type: ndcg_at_3 value: 33.004 - type: ndcg_at_5 value: 35.411 - type: precision_at_1 value: 28.265 - type: precision_at_10 value: 6.715999999999999 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 15.299 - type: precision_at_5 value: 10.951 - type: recall_at_1 value: 23.472 - type: recall_at_10 value: 51.413 - type: recall_at_100 value: 75.17 - type: recall_at_1000 value: 91.577 - type: recall_at_3 value: 36.651 - type: recall_at_5 value: 42.814 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.666 - type: map_at_10 value: 32.963 - type: map_at_100 value: 34.544999999999995 - type: map_at_1000 value: 34.792 - type: map_at_3 value: 29.74 - type: map_at_5 value: 31.5 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 38.013000000000005 - type: mrr_at_100 value: 38.997 - type: mrr_at_1000 value: 39.055 - type: mrr_at_3 value: 34.947 - type: mrr_at_5 value: 36.815 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.361000000000004 - type: ndcg_at_100 value: 45.186 - type: ndcg_at_1000 value: 47.867 - type: ndcg_at_3 value: 33.797 - type: ndcg_at_5 value: 36.456 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 15.876000000000001 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 51.858000000000004 - type: recall_at_100 value: 77.805 - type: recall_at_1000 value: 94.504 - type: recall_at_3 value: 36.207 - type: recall_at_5 value: 43.094 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.662 - type: map_at_10 value: 23.594 - type: map_at_100 value: 24.593999999999998 - type: map_at_1000 value: 24.694 - type: map_at_3 value: 20.925 - type: map_at_5 value: 22.817999999999998 - type: mrr_at_1 value: 17.375 - type: mrr_at_10 value: 25.734 - type: mrr_at_100 value: 26.586 - type: mrr_at_1000 value: 26.671 - type: mrr_at_3 value: 23.044 - type: mrr_at_5 value: 24.975 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 28.186 - type: ndcg_at_100 value: 33.436 - type: ndcg_at_1000 value: 36.203 - type: ndcg_at_3 value: 23.152 - type: ndcg_at_5 value: 26.397 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.677 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 15.662 - type: recall_at_10 value: 40.066 - type: recall_at_100 value: 65.006 - type: recall_at_1000 value: 85.94000000000001 - type: recall_at_3 value: 27.400000000000002 - type: recall_at_5 value: 35.002 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.853 - type: map_at_10 value: 15.568000000000001 - type: map_at_100 value: 17.383000000000003 - type: map_at_1000 value: 17.584 - type: map_at_3 value: 12.561 - type: map_at_5 value: 14.056 - type: mrr_at_1 value: 18.958 - type: mrr_at_10 value: 28.288000000000004 - type: mrr_at_100 value: 29.432000000000002 - type: mrr_at_1000 value: 29.498 - type: mrr_at_3 value: 25.049 - type: mrr_at_5 value: 26.857 - type: ndcg_at_1 value: 18.958 - type: ndcg_at_10 value: 22.21 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 33.583 - type: ndcg_at_3 value: 16.994999999999997 - type: ndcg_at_5 value: 18.95 - type: precision_at_1 value: 18.958 - type: precision_at_10 value: 7.192 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.573 - type: precision_at_5 value: 10.202 - type: recall_at_1 value: 8.853 - type: recall_at_10 value: 28.087 - type: recall_at_100 value: 53.701 - type: recall_at_1000 value: 76.29899999999999 - type: recall_at_3 value: 15.913 - type: recall_at_5 value: 20.658 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.077 - type: map_at_10 value: 20.788999999999998 - type: map_at_100 value: 30.429000000000002 - type: map_at_1000 value: 32.143 - type: map_at_3 value: 14.692 - type: map_at_5 value: 17.139 - type: mrr_at_1 value: 70.75 - type: mrr_at_10 value: 78.036 - type: mrr_at_100 value: 78.401 - type: mrr_at_1000 value: 78.404 - type: mrr_at_3 value: 76.75 - type: mrr_at_5 value: 77.47500000000001 - type: ndcg_at_1 value: 58.12500000000001 - type: ndcg_at_10 value: 44.015 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 56.211999999999996 - type: ndcg_at_3 value: 49.151 - type: ndcg_at_5 value: 46.195 - type: precision_at_1 value: 70.75 - type: precision_at_10 value: 35.5 - type: precision_at_100 value: 11.355 - type: precision_at_1000 value: 2.1950000000000003 - type: precision_at_3 value: 53.083000000000006 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 9.077 - type: recall_at_10 value: 26.259 - type: recall_at_100 value: 56.547000000000004 - type: recall_at_1000 value: 78.551 - type: recall_at_3 value: 16.162000000000003 - type: recall_at_5 value: 19.753999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.44500000000001 - type: f1 value: 44.67067691783401 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 68.182 - type: map_at_10 value: 78.223 - type: map_at_100 value: 78.498 - type: map_at_1000 value: 78.512 - type: map_at_3 value: 76.71 - type: map_at_5 value: 77.725 - type: mrr_at_1 value: 73.177 - type: mrr_at_10 value: 82.513 - type: mrr_at_100 value: 82.633 - type: mrr_at_1000 value: 82.635 - type: mrr_at_3 value: 81.376 - type: mrr_at_5 value: 82.182 - type: ndcg_at_1 value: 73.177 - type: ndcg_at_10 value: 82.829 - type: ndcg_at_100 value: 83.84 - type: ndcg_at_1000 value: 84.07900000000001 - type: ndcg_at_3 value: 80.303 - type: ndcg_at_5 value: 81.846 - type: precision_at_1 value: 73.177 - type: precision_at_10 value: 10.241999999999999 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 31.247999999999998 - type: precision_at_5 value: 19.697 - type: recall_at_1 value: 68.182 - type: recall_at_10 value: 92.657 - type: recall_at_100 value: 96.709 - type: recall_at_1000 value: 98.184 - type: recall_at_3 value: 85.9 - type: recall_at_5 value: 89.755 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 21.108 - type: map_at_10 value: 33.342 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.067 - type: map_at_5 value: 31.563000000000002 - type: mrr_at_1 value: 41.667 - type: mrr_at_10 value: 49.913000000000004 - type: mrr_at_100 value: 50.724000000000004 - type: mrr_at_1000 value: 50.766 - type: mrr_at_3 value: 47.504999999999995 - type: mrr_at_5 value: 49.033 - type: ndcg_at_1 value: 41.667 - type: ndcg_at_10 value: 41.144 - type: ndcg_at_100 value: 48.326 - type: ndcg_at_1000 value: 51.486 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 38.78 - type: precision_at_1 value: 41.667 - type: precision_at_10 value: 11.358 - type: precision_at_100 value: 1.873 - type: precision_at_1000 value: 0.244 - type: precision_at_3 value: 25 - type: precision_at_5 value: 18.519 - type: recall_at_1 value: 21.108 - type: recall_at_10 value: 47.249 - type: recall_at_100 value: 74.52 - type: recall_at_1000 value: 93.31 - type: recall_at_3 value: 33.271 - type: recall_at_5 value: 39.723000000000006 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.317 - type: map_at_10 value: 64.861 - type: map_at_100 value: 65.697 - type: map_at_1000 value: 65.755 - type: map_at_3 value: 61.258 - type: map_at_5 value: 63.590999999999994 - type: mrr_at_1 value: 80.635 - type: mrr_at_10 value: 86.528 - type: mrr_at_100 value: 86.66199999999999 - type: mrr_at_1000 value: 86.666 - type: mrr_at_3 value: 85.744 - type: mrr_at_5 value: 86.24300000000001 - type: ndcg_at_1 value: 80.635 - type: ndcg_at_10 value: 73.13199999999999 - type: ndcg_at_100 value: 75.927 - type: ndcg_at_1000 value: 76.976 - type: ndcg_at_3 value: 68.241 - type: ndcg_at_5 value: 71.071 - type: precision_at_1 value: 80.635 - type: precision_at_10 value: 15.326 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 43.961 - type: precision_at_5 value: 28.599999999999998 - type: recall_at_1 value: 40.317 - type: recall_at_10 value: 76.631 - type: recall_at_100 value: 87.495 - type: recall_at_1000 value: 94.362 - type: recall_at_3 value: 65.94200000000001 - type: recall_at_5 value: 71.499 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.686 - type: ap value: 87.5577120393173 - type: f1 value: 91.6629447355139 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.702 - type: map_at_10 value: 36.414 - type: map_at_100 value: 37.561 - type: map_at_1000 value: 37.605 - type: map_at_3 value: 32.456 - type: map_at_5 value: 34.827000000000005 - type: mrr_at_1 value: 24.355 - type: mrr_at_10 value: 37.01 - type: mrr_at_100 value: 38.085 - type: mrr_at_1000 value: 38.123000000000005 - type: mrr_at_3 value: 33.117999999999995 - type: mrr_at_5 value: 35.452 - type: ndcg_at_1 value: 24.384 - type: ndcg_at_10 value: 43.456 - type: ndcg_at_100 value: 48.892 - type: ndcg_at_1000 value: 49.964 - type: ndcg_at_3 value: 35.475 - type: ndcg_at_5 value: 39.711 - type: precision_at_1 value: 24.384 - type: precision_at_10 value: 6.7940000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 15.052999999999999 - type: precision_at_5 value: 11.189 - type: recall_at_1 value: 23.702 - type: recall_at_10 value: 65.057 - type: recall_at_100 value: 90.021 - type: recall_at_1000 value: 98.142 - type: recall_at_3 value: 43.551 - type: recall_at_5 value: 53.738 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.62380300957591 - type: f1 value: 94.49871222100734 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.14090287277702 - type: f1 value: 60.32101258220515 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.84330867518494 - type: f1 value: 71.92248688515255 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.10692669804976 - type: f1 value: 77.9904839122866 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.822988923078444 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.38394880253403 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.82504612539082 - type: mrr value: 32.84462298174977 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.029 - type: map_at_10 value: 14.088999999999999 - type: map_at_100 value: 17.601 - type: map_at_1000 value: 19.144 - type: map_at_3 value: 10.156 - type: map_at_5 value: 11.892 - type: mrr_at_1 value: 46.44 - type: mrr_at_10 value: 56.596999999999994 - type: mrr_at_100 value: 57.11000000000001 - type: mrr_at_1000 value: 57.14 - type: mrr_at_3 value: 54.334 - type: mrr_at_5 value: 55.774 - type: ndcg_at_1 value: 44.891999999999996 - type: ndcg_at_10 value: 37.134 - type: ndcg_at_100 value: 33.652 - type: ndcg_at_1000 value: 42.548 - type: ndcg_at_3 value: 41.851 - type: ndcg_at_5 value: 39.842 - type: precision_at_1 value: 46.44 - type: precision_at_10 value: 27.647 - type: precision_at_100 value: 8.309999999999999 - type: precision_at_1000 value: 2.146 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 6.029 - type: recall_at_10 value: 18.907 - type: recall_at_100 value: 33.76 - type: recall_at_1000 value: 65.14999999999999 - type: recall_at_3 value: 11.584999999999999 - type: recall_at_5 value: 14.626 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 39.373000000000005 - type: map_at_10 value: 55.836 - type: map_at_100 value: 56.611999999999995 - type: map_at_1000 value: 56.63 - type: map_at_3 value: 51.747 - type: map_at_5 value: 54.337999999999994 - type: mrr_at_1 value: 44.147999999999996 - type: mrr_at_10 value: 58.42699999999999 - type: mrr_at_100 value: 58.902 - type: mrr_at_1000 value: 58.914 - type: mrr_at_3 value: 55.156000000000006 - type: mrr_at_5 value: 57.291000000000004 - type: ndcg_at_1 value: 44.119 - type: ndcg_at_10 value: 63.444 - type: ndcg_at_100 value: 66.40599999999999 - type: ndcg_at_1000 value: 66.822 - type: ndcg_at_3 value: 55.962 - type: ndcg_at_5 value: 60.228 - type: precision_at_1 value: 44.119 - type: precision_at_10 value: 10.006 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.135 - type: precision_at_5 value: 17.59 - type: recall_at_1 value: 39.373000000000005 - type: recall_at_10 value: 83.78999999999999 - type: recall_at_100 value: 96.246 - type: recall_at_1000 value: 99.324 - type: recall_at_3 value: 64.71900000000001 - type: recall_at_5 value: 74.508 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.199 - type: map_at_10 value: 82.892 - type: map_at_100 value: 83.578 - type: map_at_1000 value: 83.598 - type: map_at_3 value: 79.948 - type: map_at_5 value: 81.779 - type: mrr_at_1 value: 79.67 - type: mrr_at_10 value: 86.115 - type: mrr_at_100 value: 86.249 - type: mrr_at_1000 value: 86.251 - type: mrr_at_3 value: 85.08200000000001 - type: mrr_at_5 value: 85.783 - type: ndcg_at_1 value: 79.67 - type: ndcg_at_10 value: 86.839 - type: ndcg_at_100 value: 88.252 - type: ndcg_at_1000 value: 88.401 - type: ndcg_at_3 value: 83.86200000000001 - type: ndcg_at_5 value: 85.473 - type: precision_at_1 value: 79.67 - type: precision_at_10 value: 13.19 - type: precision_at_100 value: 1.521 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.677 - type: precision_at_5 value: 24.118000000000002 - type: recall_at_1 value: 69.199 - type: recall_at_10 value: 94.321 - type: recall_at_100 value: 99.20400000000001 - type: recall_at_1000 value: 99.947 - type: recall_at_3 value: 85.787 - type: recall_at_5 value: 90.365 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.82810046856353 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.38132611783628 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.127000000000001 - type: map_at_10 value: 12.235 - type: map_at_100 value: 14.417 - type: map_at_1000 value: 14.75 - type: map_at_3 value: 8.906 - type: map_at_5 value: 10.591000000000001 - type: mrr_at_1 value: 25.2 - type: mrr_at_10 value: 35.879 - type: mrr_at_100 value: 36.935 - type: mrr_at_1000 value: 36.997 - type: mrr_at_3 value: 32.783 - type: mrr_at_5 value: 34.367999999999995 - type: ndcg_at_1 value: 25.2 - type: ndcg_at_10 value: 20.509 - type: ndcg_at_100 value: 28.67 - type: ndcg_at_1000 value: 34.42 - type: ndcg_at_3 value: 19.948 - type: ndcg_at_5 value: 17.166 - type: precision_at_1 value: 25.2 - type: precision_at_10 value: 10.440000000000001 - type: precision_at_100 value: 2.214 - type: precision_at_1000 value: 0.359 - type: precision_at_3 value: 18.533 - type: precision_at_5 value: 14.860000000000001 - type: recall_at_1 value: 5.127000000000001 - type: recall_at_10 value: 21.147 - type: recall_at_100 value: 44.946999999999996 - type: recall_at_1000 value: 72.89 - type: recall_at_3 value: 11.277 - type: recall_at_5 value: 15.042 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.0373011786213 - type: cos_sim_spearman value: 79.27889560856613 - type: euclidean_pearson value: 80.31186315495655 - type: euclidean_spearman value: 79.41630415280811 - type: manhattan_pearson value: 80.31755140442013 - type: manhattan_spearman value: 79.43069870027611 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.8659751342045 - type: cos_sim_spearman value: 76.95377612997667 - type: euclidean_pearson value: 81.24552945497848 - type: euclidean_spearman value: 77.18236963555253 - type: manhattan_pearson value: 81.26477607759037 - type: manhattan_spearman value: 77.13821753062756 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.34597139044875 - type: cos_sim_spearman value: 84.124169425592 - type: euclidean_pearson value: 83.68590721511401 - type: euclidean_spearman value: 84.18846190846398 - type: manhattan_pearson value: 83.57630235061498 - type: manhattan_spearman value: 84.10244043726902 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.67641885599572 - type: cos_sim_spearman value: 80.46450725650428 - type: euclidean_pearson value: 81.61645042715865 - type: euclidean_spearman value: 80.61418394236874 - type: manhattan_pearson value: 81.55712034928871 - type: manhattan_spearman value: 80.57905670523951 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.86650310886782 - type: cos_sim_spearman value: 89.76081629222328 - type: euclidean_pearson value: 89.1530747029954 - type: euclidean_spearman value: 89.80990657280248 - type: manhattan_pearson value: 89.10640563278132 - type: manhattan_spearman value: 89.76282108434047 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.93864027911118 - type: cos_sim_spearman value: 85.47096193999023 - type: euclidean_pearson value: 85.03141840870533 - type: euclidean_spearman value: 85.43124029598181 - type: manhattan_pearson value: 84.99002664393512 - type: manhattan_spearman value: 85.39169195120834 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.7045343749832 - type: cos_sim_spearman value: 89.03262221146677 - type: euclidean_pearson value: 89.56078218264365 - type: euclidean_spearman value: 89.17827006466868 - type: manhattan_pearson value: 89.52717595468582 - type: manhattan_spearman value: 89.15878115952923 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.20191302875551 - type: cos_sim_spearman value: 64.11446552557646 - type: euclidean_pearson value: 64.6918197393619 - type: euclidean_spearman value: 63.440182631197764 - type: manhattan_pearson value: 64.55692904121835 - type: manhattan_spearman value: 63.424877742756266 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.37793104662344 - type: cos_sim_spearman value: 87.7357802629067 - type: euclidean_pearson value: 87.4286301545109 - type: euclidean_spearman value: 87.78452920777421 - type: manhattan_pearson value: 87.42445169331255 - type: manhattan_spearman value: 87.78537677249598 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.31465405081792 - type: mrr value: 95.7173781193389 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.760999999999996 - type: map_at_10 value: 67.904 - type: map_at_100 value: 68.539 - type: map_at_1000 value: 68.562 - type: map_at_3 value: 65.415 - type: map_at_5 value: 66.788 - type: mrr_at_1 value: 60.333000000000006 - type: mrr_at_10 value: 68.797 - type: mrr_at_100 value: 69.236 - type: mrr_at_1000 value: 69.257 - type: mrr_at_3 value: 66.667 - type: mrr_at_5 value: 67.967 - type: ndcg_at_1 value: 60.333000000000006 - type: ndcg_at_10 value: 72.24199999999999 - type: ndcg_at_100 value: 74.86 - type: ndcg_at_1000 value: 75.354 - type: ndcg_at_3 value: 67.93400000000001 - type: ndcg_at_5 value: 70.02199999999999 - type: precision_at_1 value: 60.333000000000006 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.778000000000002 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 57.760999999999996 - type: recall_at_10 value: 84.383 - type: recall_at_100 value: 96.267 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.094 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8029702970297 - type: cos_sim_ap value: 94.9210324173411 - type: cos_sim_f1 value: 89.8521162672106 - type: cos_sim_precision value: 91.67533818938605 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.69504950495049 - type: dot_ap value: 90.4919719146181 - type: dot_f1 value: 84.72289156626506 - type: dot_precision value: 81.76744186046511 - type: dot_recall value: 87.9 - type: euclidean_accuracy value: 99.79702970297029 - type: euclidean_ap value: 94.87827463795753 - type: euclidean_f1 value: 89.55680081507896 - type: euclidean_precision value: 91.27725856697819 - type: euclidean_recall value: 87.9 - type: manhattan_accuracy value: 99.7990099009901 - type: manhattan_ap value: 94.87587025149682 - type: manhattan_f1 value: 89.76298537569339 - type: manhattan_precision value: 90.53916581892166 - type: manhattan_recall value: 89 - type: max_accuracy value: 99.8029702970297 - type: max_ap value: 94.9210324173411 - type: max_f1 value: 89.8521162672106 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.92385753948724 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.671756975431144 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.677928036739004 - type: mrr value: 51.56413133435193 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.523589340819683 - type: cos_sim_spearman value: 30.187407518823235 - type: dot_pearson value: 29.039713969699015 - type: dot_spearman value: 29.114740651155508 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.211 - type: map_at_10 value: 1.6199999999999999 - type: map_at_100 value: 8.658000000000001 - type: map_at_1000 value: 21.538 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.919 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.18599999999999 - type: mrr_at_100 value: 86.18599999999999 - type: mrr_at_1000 value: 86.18599999999999 - type: mrr_at_3 value: 85 - type: mrr_at_5 value: 85.9 - type: ndcg_at_1 value: 74 - type: ndcg_at_10 value: 66.542 - type: ndcg_at_100 value: 50.163999999999994 - type: ndcg_at_1000 value: 45.696999999999996 - type: ndcg_at_3 value: 71.531 - type: ndcg_at_5 value: 70.45 - type: precision_at_1 value: 78 - type: precision_at_10 value: 69.39999999999999 - type: precision_at_100 value: 51.06 - type: precision_at_1000 value: 20.022000000000002 - type: precision_at_3 value: 76 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.211 - type: recall_at_10 value: 1.813 - type: recall_at_100 value: 12.098 - type: recall_at_1000 value: 42.618 - type: recall_at_3 value: 0.603 - type: recall_at_5 value: 0.987 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.2079999999999997 - type: map_at_10 value: 7.777000000000001 - type: map_at_100 value: 12.825000000000001 - type: map_at_1000 value: 14.196 - type: map_at_3 value: 4.285 - type: map_at_5 value: 6.177 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 42.635 - type: mrr_at_100 value: 43.955 - type: mrr_at_1000 value: 43.955 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 41.088 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 20.666999999999998 - type: ndcg_at_100 value: 31.840000000000003 - type: ndcg_at_1000 value: 43.191 - type: ndcg_at_3 value: 23.45 - type: ndcg_at_5 value: 22.994 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 6.755 - type: precision_at_1000 value: 1.4200000000000002 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 2.2079999999999997 - type: recall_at_10 value: 13.144 - type: recall_at_100 value: 42.491 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 5.3469999999999995 - type: recall_at_5 value: 9.139 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.9044 - type: ap value: 14.625783489340755 - type: f1 value: 54.814936562590546 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.94227504244483 - type: f1 value: 61.22516038508854 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.602409155145864 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.94641473445789 - type: cos_sim_ap value: 76.91572747061197 - type: cos_sim_f1 value: 70.14348097317529 - type: cos_sim_precision value: 66.53254437869822 - type: cos_sim_recall value: 74.1688654353562 - type: dot_accuracy value: 84.80061989628658 - type: dot_ap value: 70.7952548895177 - type: dot_f1 value: 65.44780728844965 - type: dot_precision value: 61.53310104529617 - type: dot_recall value: 69.89445910290237 - type: euclidean_accuracy value: 86.94641473445789 - type: euclidean_ap value: 76.80774009393652 - type: euclidean_f1 value: 70.30522503879979 - type: euclidean_precision value: 68.94977168949772 - type: euclidean_recall value: 71.71503957783642 - type: manhattan_accuracy value: 86.8629671574179 - type: manhattan_ap value: 76.76518632600317 - type: manhattan_f1 value: 70.16056518946692 - type: manhattan_precision value: 68.360450563204 - type: manhattan_recall value: 72.0580474934037 - type: max_accuracy value: 86.94641473445789 - type: max_ap value: 76.91572747061197 - type: max_f1 value: 70.30522503879979 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.10428066907285 - type: cos_sim_ap value: 86.25114759921435 - type: cos_sim_f1 value: 78.37857884586856 - type: cos_sim_precision value: 75.60818546078993 - type: cos_sim_recall value: 81.35971666153372 - type: dot_accuracy value: 87.41995575736406 - type: dot_ap value: 81.51838010086782 - type: dot_f1 value: 74.77398015435503 - type: dot_precision value: 71.53002390662354 - type: dot_recall value: 78.32614721281182 - type: euclidean_accuracy value: 89.12368533395428 - type: euclidean_ap value: 86.33456799874504 - type: euclidean_f1 value: 78.45496750232127 - type: euclidean_precision value: 75.78388462366364 - type: euclidean_recall value: 81.32121958731136 - type: manhattan_accuracy value: 89.10622113556099 - type: manhattan_ap value: 86.31215061745333 - type: manhattan_f1 value: 78.40684906011539 - type: manhattan_precision value: 75.89536643366722 - type: manhattan_recall value: 81.09023714197721 - type: max_accuracy value: 89.12368533395428 - type: max_ap value: 86.33456799874504 - type: max_f1 value: 78.45496750232127 language: - en license: mit --- # E5-large-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 24 layers and the embedding size is 1024. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large-v2') model = AutoModel.from_pretrained('intfloat/e5-large-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/e5-large-v2') input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens.

ybelkada/tiny-random-T5ForConditionalGeneration-calibrated

ybelkada

transformers

text2text-generation

A "better calibrated" tiny T5 model for testing purposes

madhurjindal/autonlp-Gibberish-Detector-492513457

madhurjindal

transformers

text-classification

--- tags: - autonlp language: en widget: - text: I love Machine Learning! datasets: - madhurjindal/autonlp-data-Gibberish-Detector co2_eq_emissions: 5.527544460835904 license: mit --- # Problem Description The ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However, a common challenge faced in such systems is the presence of gibberish or nonsensical input. To address this problem, we present a project focused on developing a gibberish detector for the English language. The primary goal of this project is to classify user input as either **gibberish** or **non-gibberish**, enabling more accurate and meaningful interactions with the system. We also aim to enhance the overall performance and user experience of chatbots and other systems that rely on user input. >## What is Gibberish? Gibberish refers to **nonsensical or meaningless language or text** that lacks coherence or any discernible meaning. It can be characterized by a combination of random words, nonsensical phrases, grammatical errors, or syntactical abnormalities that prevent the communication from conveying a clear and understandable message. Gibberish can vary in intensity, ranging from simple noise with no meaningful words to sentences that may appear superficially correct but lack coherence or logical structure when examined closely. Detecting and identifying gibberish is essential in various contexts, such as **natural language processing**, **chatbot systems**, **spam filtering**, and **language-based security measures**, to ensure effective communication and accurate processing of user inputs. ## Label Description Thus, we break down the problem into 4 categories: 1. **Noise:** Gibberish at the zero level where even the different constituents of the input phrase (words) do not hold any meaning independently. *For example: `dfdfer fgerfow2e0d qsqskdsd djksdnfkff swq.`* 2. **Word Salad:** Gibberish at level 1 where words make sense independently, but when looked at the bigger picture (the phrase) any meaning is not depicted. *For example: `22 madhur old punjab pickle chennai`* 3. **Mild gibberish:** Gibberish at level 2 where there is a part of the sentence that has grammatical errors, word sense errors, or any syntactical abnormalities, which leads the sentence to miss out on a coherent meaning. *For example: `Madhur study in a teacher`* 4. **Clean:** This category represents a set of words that form a complete and meaningful sentence on its own. *For example: `I love this website`* > **Tip:** To facilitate gibberish detection, you can combine the labels based on the desired level of detection. For instance, if you need to detect gibberish at level 1, you can group Noise and Word Salad together as "Gibberish," while considering Mild gibberish and Clean separately as "NotGibberish." This approach allows for flexibility in detecting and categorizing different levels of gibberish based on specific requirements. # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 492513457 - CO2 Emissions (in grams): 5.527544460835904 ## Validation Metrics - Loss: 0.07609463483095169 - Accuracy: 0.9735624586913417 - Macro F1: 0.9736173135739408 - Micro F1: 0.9735624586913417 - Weighted F1: 0.9736173135739408 - Macro Precision: 0.9737771415197378 - Micro Precision: 0.9735624586913417 - Weighted Precision: 0.9737771415197378 - Macro Recall: 0.9735624586913417 - Micro Recall: 0.9735624586913417 - Weighted Recall: 0.9735624586913417 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love Machine Learning!"}' https://api-inference.huggingface.co/models/madhurjindal/autonlp-Gibberish-Detector-492513457 ``` Or Python API: ``` import torch import torch.nn.functional as F from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) inputs = tokenizer("I love Machine Learning!", return_tensors="pt") outputs = model(**inputs) probs = F.softmax(outputs.logits, dim=-1) predicted_index = torch.argmax(probs, dim=1).item() predicted_prob = probs[0][predicted_index].item() labels = model.config.id2label predicted_label = labels[predicted_index] for i, prob in enumerate(probs[0]): print(f"Class: {labels[i]}, Probability: {prob:.4f}") ``` Another simplifed solution with transformers pipline: ``` from transformers import pipeline selected_model = "madhurjindal/autonlp-Gibberish-Detector-492513457" classifier = pipeline("text-classification", model=selected_model) classifier("I love Machine Learning!") ```

deepset/roberta-base-squad2

deepset

transformers

question-answering

--- language: en license: cc-by-4.0 datasets: - squad_v2 model-index: - name: deepset/roberta-base-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 79.9309 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhNjg5YzNiZGQ1YTIyYTAwZGUwOWEzZTRiYzdjM2QzYjA3ZTUxNDM1NjE1MTUyMjE1MGY1YzEzMjRjYzVjYiIsInZlcnNpb24iOjF9.EH5JJo8EEFwU7osPz3s7qanw_tigeCFhCXjSfyN0Y1nWVnSfulSxIk_DbAEI5iE80V4EKLyp5-mYFodWvL2KDA - type: f1 value: 82.9501 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aH_s67KfbcmkvL-6ZiI2s8IoxhHJMSf29H_uV2YLyevwx900t-MwTVOW3qfFnMMEAQ - type: total value: 11869 name: total verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 85.289 name: Exact Match - type: f1 value: 91.841 name: F1 - task: type: question-answering name: Question Answering dataset: name: adversarial_qa type: adversarial_qa config: adversarialQA split: validation metrics: - type: exact_match value: 29.500 name: Exact Match - type: f1 value: 40.367 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad_adversarial type: squad_adversarial config: AddOneSent split: validation metrics: - type: exact_match value: 78.567 name: Exact Match - type: f1 value: 84.469 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics: - type: exact_match value: 69.924 name: Exact Match - type: f1 value: 83.284 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts new_wiki type: squadshifts config: new_wiki split: test metrics: - type: exact_match value: 81.204 name: Exact Match - type: f1 value: 90.595 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics: - type: exact_match value: 82.931 name: Exact Match - type: f1 value: 90.756 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics: - type: exact_match value: 71.550 name: Exact Match - type: f1 value: 82.939 name: F1 --- # roberta-base for QA This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/roberta-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "HasAns_total": 5928, "NoAns_exact": 81.79983179142137, "NoAns_f1": 81.79983179142137, "NoAns_total": 5945 ``` ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

Rostlab/prot_t5_xl_uniref50

Rostlab

transformers

text2text-generation

--- tags: - protein language model datasets: - UniRef50 --- # ProtT5-XL-UniRef50 model Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in [this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids. ## Model description ProtT5-XL-UniRef50 is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the original T5 version is the denosing objective. The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input. It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape. shape. This implied learning some of the grammar of the language of life realized in protein sequences. ## Intended uses & limitations The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor. We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder. ### How to use Here is how to use this model to extract the features of a given protein sequence in PyTorch: ```python sequence_examples = ["PRTEINO", "SEQWENCE"] # this will replace all rare/ambiguous amino acids by X and introduce white-space between all amino acids sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", sequence))) for sequence in sequence_examples] # tokenize sequences and pad up to the longest sequence in the batch ids = tokenizer.batch_encode_plus(sequence_examples, add_special_tokens=True, padding="longest") input_ids = torch.tensor(ids['input_ids']).to(device) attention_mask = torch.tensor(ids['attention_mask']).to(device) # generate embeddings with torch.no_grad(): embedding_repr = model(input_ids=input_ids,attention_mask=attention_mask) # extract embeddings for the first ([0,:]) sequence in the batch while removing padded & special tokens ([0,:7]) emb_0 = embedding_repr.last_hidden_state[0,:7] # shape (7 x 1024) print(f"Shape of per-residue embedding of first sequences: {emb_0.shape}") # do the same for the second ([1,:]) sequence in the batch while taking into account different sequence lengths ([1,:8]) emb_1 = embedding_repr.last_hidden_state[1,:8] # shape (8 x 1024) # if you want to derive a single representation (per-protein embedding) for the whole protein emb_0_per_protein = emb_0.mean(dim=0) # shape (1024) print(f"Shape of per-protein embedding of first sequences: {emb_0_per_protein.shape}") ``` ## Training data The ProtT5-XL-UniRef50 model was pretrained on [UniRef50](https://www.uniprot.org/help/uniref), a dataset consisting of 45 million protein sequences. ## Training procedure ### Preprocessing The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X". The inputs of the model are then of the form: ``` Protein Sequence [EOS] ``` The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens. The details of the masking procedure for each sequence are as follows: - 15% of the amino acids are masked. - In 90% of the cases, the masked amino acids are replaced by `[MASK]` token. - In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace. ### Pretraining The model was trained on a single TPU Pod V2-256 for 991.5 thousand steps in total, using sequence length 512 (batch size 2k). It was trained using ProtT5-XL-BFD model as an initial checkpoint, rather than training from scratch. It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ## Evaluation results When the model is used for feature extraction, this model achieves the following results: Test results : | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane | |:-----:|:-----:|:-----:|:-----:|:-----:| | CASP12 | 81 | 70 | | | | TS115 | 87 | 77 | | | | CB513 | 86 | 74 | | | | DeepLoc | | | 81 | 91 | ### BibTeX entry and citation info ```bibtex @article {Elnaggar2020.07.12.199554, author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard}, title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing}, elocation-id = {2020.07.12.199554}, year = {2020}, doi = {10.1101/2020.07.12.199554}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554}, eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf}, journal = {bioRxiv} } ``` > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

meta-llama/Llama-2-13b-chat-hf

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/. "Llama Materials" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non- transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a "Notice" text file distributed as a part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved." iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). 2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials. b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

shibing624/text2vec-base-multilingual

shibing624

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - text2vec - mteb datasets: - shibing624/nli-zh-all language: - zh - en - de - fr - it - nl - pt - pl - ru metrics: - spearmanr model-index: - name: text2vec-base-multilingual results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 70.97014925373134 - type: ap value: 33.95151328318672 - type: f1 value: 65.14740155705596 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.69379014989293 - type: ap value: 79.68277579733802 - type: f1 value: 66.54960052336921 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 70.90704647676162 - type: ap value: 20.747518928580437 - type: f1 value: 58.64365465884924 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 61.605995717344754 - type: ap value: 14.135974879487028 - type: f1 value: 49.980224800472136 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 66.103375 - type: ap value: 61.10087197664471 - type: f1 value: 65.75198509894145 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - 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type: cos_sim_accuracy value: 87.80416812201653 - type: cos_sim_ap value: 83.45540469219863 - type: cos_sim_f1 value: 75.58836427422892 - type: cos_sim_precision value: 71.93934335002783 - type: cos_sim_recall value: 79.62734832152756 - type: dot_accuracy value: 83.04226336011176 - type: dot_ap value: 70.63007268018524 - type: dot_f1 value: 65.35980325765405 - type: dot_precision value: 60.84677151768532 - type: dot_recall value: 70.59593470896212 - type: euclidean_accuracy value: 87.60430007373773 - type: euclidean_ap value: 83.10068502536592 - type: euclidean_f1 value: 75.02510506936439 - type: euclidean_precision value: 72.56637168141593 - type: euclidean_recall value: 77.65629812134279 - type: manhattan_accuracy value: 87.60041914076145 - type: manhattan_ap value: 83.05480769911229 - type: manhattan_f1 value: 74.98522895125554 - type: manhattan_precision value: 72.04797047970479 - type: manhattan_recall value: 78.17215891592238 - type: max_accuracy value: 87.80416812201653 - type: max_ap value: 83.45540469219863 - type: max_f1 value: 75.58836427422892 --- # shibing624/text2vec-base-multilingual This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual. It maps sentences to a 384 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. - training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset - base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - max_seq_length: 256 - best epoch: 4 - sentence embedding dim: 384 ## Evaluation For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec) ## Languages Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh ### Release Models - 本项目release模型的中文匹配评测结果： | Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg | QPS | |:-----------|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:| | Word2Vec | word2vec | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html) | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 55.04 | 20.70 | 35.03 | 23769 | | SBERT | xlm-roberta-base | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 63.01 | 52.28 | 46.46 | 3138 | | Instructor | hfl/chinese-roberta-wwm-ext | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 | 75.83 | 60.55 | 57.93 | 2980 | | CoSENT | hfl/chinese-macbert-base | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | 70.27 | 50.42 | 51.61 | 3008 | | CoSENT | hfl/chinese-lert-large | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 | 73.01 | 59.04 | 53.12 | 2092 | | CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 | | CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 | 76.70 | 63.30 | **63.08** | 3066 | | CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 | 68.88 | 51.17 | 53.67 | 4004 | 说明： - 结果评测指标：spearman系数 - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 - `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集[shibing624/nli-zh-all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset)训练得到，并在中英文测试集评估相对于原模型效果有提升，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用 - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 - QPS的GPU测试环境是Tesla V100，显存32GB 模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md) ## Usage (text2vec) Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed: ``` pip install -U text2vec ``` Then you can use the model like this: ```python from text2vec import SentenceModel sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card'] model = SentenceModel('shibing624/text2vec-base-multilingual') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Install transformers: ``` pip install transformers ``` Then load model and predict: ```python from transformers import AutoTokenizer, AutoModel import torch # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('shibing624/text2vec-base-multilingual') model = AutoModel.from_pretrained('shibing624/text2vec-base-multilingual') sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card'] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Usage (sentence-transformers) [sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences. Install sentence-transformers: ``` pip install -U sentence-transformers ``` Then load model and predict: ```python from sentence_transformers import SentenceTransformer m = SentenceTransformer("shibing624/text2vec-base-multilingual") sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card'] sentence_embeddings = m.encode(sentences) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` CoSENT( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True}) ) ``` ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the rank loss by comparing with true pairs and false pairs. ## Citing & Authors This model was trained by [text2vec](https://github.com/shibing624/text2vec). If you find this model helpful, feel free to cite: ```bibtex @software{text2vec, author = {Ming Xu}, title = {text2vec: A Tool for Text to Vector}, year = {2023}, url = {https://github.com/shibing624/text2vec}, } ```