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THUDM/chatglm2-6b

THUDM

transformers

--- language: - zh - en tags: - glm - chatglm - thudm --- # ChatGLM2-6B <p align="center"> 💻 <a href="https://github.com/THUDM/ChatGLM2-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1y7pqoloy-9b1g6T6JjA8J0KxvUjbwJw" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a> </p> <p align="center"> 📍Experience the larger-scale ChatGLM model at <a href="https://www.chatglm.cn">chatglm.cn</a> </p> ## 介绍 ChatGLM**2**-6B 是开源中英双语对话模型 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) 的第二代版本，在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM**2**-6B 引入了如下新特性： 1. **更强大的性能**：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 [GLM](https://github.com/THUDM/GLM) 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，[评测结果](#评测结果)显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。 2. **更长的上下文**：基于 [FlashAttention](https://github.com/HazyResearch/flash-attention) 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。 3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。 4. **更开放的协议**：ChatGLM2-6B 权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。 ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features: 1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size. 2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations. 3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K. 4. **More Open License**: ChatGLM2-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://open.bigmodel.cn/mla/form). ## 软件依赖 ```shell pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate ``` ## 代码调用 可以通过如下代码调用 ChatGLM-6B 模型来生成对话： ```ipython >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True) >>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda() >>> model = model.eval() >>> response, history = model.chat(tokenizer, "你好", history=[]) >>> print(response) 你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。 >>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) >>> print(response) 晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法: 1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。 2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。 3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。 4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。 5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。 6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。 如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。 ``` 关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM2-6B)。 For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B). ## Change Log * v1.0 ## 协议 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM2-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。 ## 引用 如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，敬请期待～ ``` @article{zeng2022glm, title={Glm-130b: An open bilingual pre-trained model}, author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others}, journal={arXiv preprint arXiv:2210.02414}, year={2022} } ``` ``` @inproceedings{du2022glm, title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={320--335}, year={2022} } ```

neulab/codebert-python

neulab

transformers

fill-mask

This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **Python** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task. It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score), but can be used for any other model or task. For more information, see: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score) ## Citation If you use this model for research, please cite: ``` @article{zhou2023codebertscore, url = {https://arxiv.org/abs/2302.05527}, author = {Zhou, Shuyan and Alon, Uri and Agarwal, Sumit and Neubig, Graham}, title = {CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code}, publisher = {arXiv}, year = {2023}, } ```

Kaludi/food-category-classification-v2.0

Kaludi

transformers

image-classification

--- tags: - vision - image-classification datasets: - Kaludi/food-category-classification-v2.0 widget: - src: https://www.foodandwine.com/thmb/gv06VNqj1uUJHGlw5e7IULwUmr8=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/2012-r-xl-vegetable-sandwich-with-dill-sauce-2000-0984c1b513ae4af396aee039afa5e38c.jpg example_title: Bread - src: https://cdn.britannica.com/34/176234-050-0E0C55C6/Glass-milk.jpg example_title: Dairy - src: https://images-gmi-pmc.edge-generalmills.com/7c1096c7-bfd0-4806-a794-1d3001fe0063.jpg example_title: Dessert - src: https://theheirloompantry.co/wp-content/uploads/2022/06/how-to-fry-eggs-perfectly-in-4-ways-the-heirloom-pantry.jpg example_title: Egg - src: https://www.mashed.com/img/gallery/the-real-reason-fried-foods-are-so-popular-right-now/l-intro-1650327494.jpg example_title: Fried Food - src: https://www.seriouseats.com/thmb/WzQz05gt5witRGeOYKTcTqfe1gs=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/butter-basted-pan-seared-steaks-recipe-hero-06-03b1131c58524be2bd6c9851a2fbdbc3.jpg example_title: Meat - src: https://assets3.thrillist.com/v1/image/3097381/1200x600/scale; example_title: Seafood - src: https://i0.wp.com/post.healthline.com/wp-content/uploads/2020/03/romaine-lettuce-1296x728-body.jpg?w=1155&h=1528 example_title: Vegetable co2_eq_emissions: emissions: 12.456278925446485 --- # Food Category Classification v2.0 This is an updated Food Category Image Classifier model of the [old](https://huggingface.co/Kaludi/food-category-classification) model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **12** different categories of foods, which includes **Bread**, **Dairy**, **Dessert**, **Egg**, **Fried Food**, **Fruit**, **Meat**, **Noodles**, **Rice**, **Seafood**, **Soup**, and **Vegetable**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio This model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_V2_App) ## Validation Metrics - Problem type: Multi-class Classification - Model ID: 3353292434 - CO2 Emissions (in grams): 12.4563 - Loss: 0.144 - Accuracy: 0.960 - Macro F1: 0.959 - Micro F1: 0.960 - Weighted F1: 0.959 - Macro Precision: 0.962 - Micro Precision: 0.960 - Weighted Precision: 0.962 - Macro Recall: 0.960 - Micro Recall: 0.960 - Weighted Recall: 0.960

tiiuae/falcon-7b

tiiuae

transformers

text-generation

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false license: apache-2.0 --- # 🚀 Falcon-7B **Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. 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? * **It outperforms 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)). * **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions. ⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct). 🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b" 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. # Model Card for Falcon-7B ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish); - **License:** Apache 2.0. ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### 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 is trained on English and French data only, 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 to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken 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" 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 was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). | **Data source** | **Fraction** | **Tokens** | **Sources** | |--------------------|--------------|------------|-----------------------------------| | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl | | Books | 7% | 110B | | | Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews | | Code | 3% | 45B | | | RefinedWeb-French | 3% | 45B | massive web crawl | | Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ### Training Procedure Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO. #### Training Hyperparameters | **Hyperparameter** | **Value** | **Comment** | |--------------------|------------|-------------------------------------------| | Precision | `bfloat16` | | | Optimizer | AdamW | | | Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 | | Weight decay | 1e-1 | | | Z-loss | 1e-4 | | | Batch size | 2304 | 30B tokens ramp-up | #### Speeds, Sizes, Times Training happened in early March 2023 and took about two weeks. ## Evaluation *Paper coming soon*. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### 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 was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances. #### Software Falcon-7B 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 is made available under the Apache 2.0 license. ## Contact [email protected]

jonatasgrosman/wav2vec2-large-xlsr-53-arabic

jonatasgrosman

transformers

automatic-speech-recognition

--- language: ar datasets: - common_voice - arabic_speech_corpus metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Arabic by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ar type: common_voice args: ar metrics: - name: Test WER type: wer value: 39.59 - name: Test CER type: cer value: 18.18 --- # Fine-tuned XLSR-53 large model for speech recognition in Arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus). 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-arabic") 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 = "ar" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic" 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 | | ------------- | ------------- | | ألديك قلم ؟ | ألديك قلم | | ليست هناك مسافة على هذه الأرض أبعد من يوم أمس. | ليست نالك مسافة على هذه الأرض أبعد من يوم الأمس م | | إنك تكبر المشكلة. | إنك تكبر المشكلة | | يرغب أن يلتقي بك. | يرغب أن يلتقي بك | | إنهم لا يعرفون لماذا حتى. | إنهم لا يعرفون لماذا حتى | | سيسعدني مساعدتك أي وقت تحب. | سيسئدنيمساعدتك أي وقد تحب | | أَحَبُّ نظريّة علمية إليّ هي أن حلقات زحل مكونة بالكامل من الأمتعة المفقودة. | أحب نظرية علمية إلي هي أن حل قتزح المكوينا بالكامل من الأمت عن المفقودة | | سأشتري له قلماً. | سأشتري له قلما | | أين المشكلة ؟ | أين المشكل | | وَلِلَّهِ يَسْجُدُ مَا فِي السَّمَاوَاتِ وَمَا فِي الْأَرْضِ مِنْ دَابَّةٍ وَالْمَلَائِكَةُ وَهُمْ لَا يَسْتَكْبِرُونَ | ولله يسجد ما في السماوات وما في الأرض من دابة والملائكة وهم لا يستكبرون | ## Evaluation The model can be evaluated as follows on the Arabic 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 = "ar" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic" 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 2021-05-14). 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-arabic | **39.59%** | **18.18%** | | bakrianoo/sinai-voice-ar-stt | 45.30% | 21.84% | | othrif/wav2vec2-large-xlsr-arabic | 45.93% | 20.51% | | kmfoda/wav2vec2-large-xlsr-arabic | 54.14% | 26.07% | | mohammed/wav2vec2-large-xlsr-arabic | 56.11% | 26.79% | | anas/wav2vec2-large-xlsr-arabic | 62.02% | 27.09% | | elgeish/wav2vec2-large-xlsr-53-arabic | 100.00% | 100.56% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-arabic, title={Fine-tuned {XLSR}-53 large model for speech recognition in {A}rabic}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic}}, year={2021} } ```

diffusers/stable-diffusion-xl-1.0-inpainting-0.1

diffusers

diffusers

text-to-image

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - inpainting inference: false --- # SD-XL Inpainting 0.1 Model Card  SD-XL Inpainting 0.1 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 SD-XL Inpainting 0.1 was initialized with the `stable-diffusion-xl-base-1.0` weights. The model is trained for 40k steps at resolution 1024x1024 and 5% dropping of the text-conditioning to improve classifier-free classifier-free guidance sampling. 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. ## How to use ```py from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to("cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] ``` **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;">a tiger sitting on a park bench</span> | <img src="https://huggingface.co/datasets/valhalla/images/resolve/main/tiger.png" alt="drawing" width="300"/> ## Model Description - **Developed by:** The Diffusers team - **Model type:** Diffusion-based text-to-image generative model - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) - **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 two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### 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. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with 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 autoencoding part of the model is lossy. - When the strength parameter is set to 1 (i.e. starting in-painting from a fully masked image), the quality of the image is degraded. The model retains the non-masked contents of the image, but images look less sharp. We're investing this and working on the next version. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

google/long-t5-tglobal-base

google

transformers

text2text-generation

--- license: apache-2.0 language: en --- # LongT5 (transient-global attention, base-sized model) LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x). Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence. LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens). ## Intended uses & limitations The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you. ### How to use ```python from transformers import AutoTokenizer, LongT5Model tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base") model = LongT5Model.from_pretrained("google/long-t5-tglobal-base") 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{guo2021longt5, title={LongT5: Efficient Text-To-Text Transformer for Long Sequences}, author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei}, journal={arXiv preprint arXiv:2112.07916}, year={2021} } ```

nateraw/food

nateraw

transformers

image-classification

--- license: apache-2.0 tags: - generated_from_trainer - image-classification - pytorch datasets: - food101 metrics: - accuracy model-index: - name: food101_outputs results: - task: name: Image Classification type: image-classification dataset: name: food-101 type: food101 args: default metrics: - name: Accuracy type: accuracy value: 0.8912871287128713 --- <!-- 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. --> # nateraw/food This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the nateraw/food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.4501 - Accuracy: 0.8913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 128 - eval_batch_size: 128 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8271 | 1.0 | 592 | 0.6070 | 0.8562 | | 0.4376 | 2.0 | 1184 | 0.4947 | 0.8691 | | 0.2089 | 3.0 | 1776 | 0.4876 | 0.8747 | | 0.0882 | 4.0 | 2368 | 0.4639 | 0.8857 | | 0.0452 | 5.0 | 2960 | 0.4501 | 0.8913 | ### Framework versions - Transformers 4.9.0.dev0 - Pytorch 1.9.0+cu102 - Datasets 1.9.1.dev0 - Tokenizers 0.10.3

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" tags: - nllb - translation 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.

facebook/encodec_32khz

facebook

transformers

feature-extraction

--- inference: false ---  # Model Card for EnCodec This model card provides details and information about EnCodec 32kHz, a state-of-the-art real-time audio codec developed by Meta AI. This EnCodec checkpoint was trained specifically as part of the [MusicGen project](https://huggingface.co/docs/transformers/main/model_doc/musicgen), and is intended to be used in conjuction with the MusicGen models. ## 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. This variant of EnCodec is trained on 20k of music data, consisting of an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music datasets. - **Developed by:** Meta AI - **Model type:** Audio Codec ### Model Sources - **Repository:** [GitHub Repository](https://github.com/facebookresearch/audiocraft) - **Paper:** [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) ## 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 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 This variant of EnCodec is designed to be used in conjunction with the official [MusicGen checkpoints](https://huggingface.co/models?search=facebook/musicgen-). However, it can also be used standalone to encode audio files. ## 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 transformers datasets[audio] ``` 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_48khz") processor = AutoProcessor.from_pretrained("facebook/encodec_48khz") # 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 ``` ## Evaluation For evaluation results, refer to the [MusicGen evaluation scores](https://huggingface.co/facebook/musicgen-large#evaluation-results). ## 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 **BibTeX:** ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ```

airesearch/wav2vec2-large-xlsr-53-th

airesearch

transformers

automatic-speech-recognition

--- language: th datasets: - common_voice tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - robust-speech-event - speech - xlsr-fine-tuning license: cc-by-sa-4.0 model-index: - name: XLS-R-53 - Thai results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: th metrics: - name: Test WER type: wer value: 0.9524 - name: Test SER type: ser value: 1.2346 - name: Test CER type: cer value: 0.1623 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: null - name: Test SER type: ser value: null - name: Test CER type: cer value: null --- # `wav2vec2-large-xlsr-53-th` Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets) [Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd) We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) based on [Fine-tuning Wav2Vec2 for English ASR](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) using Thai examples of [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets). The notebooks and scripts can be found in [vistec-ai/wav2vec2-large-xlsr-53-th](https://github.com/vistec-ai/wav2vec2-large-xlsr-53-th). The pretrained model and processor can be found at [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). ## `robust-speech-event` Add `syllable_tokenize`, `word_tokenize` ([PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)) and [deepcut](https://github.com/rkcosmos/deepcut) tokenizers to `eval.py` from [robust-speech-event](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#evaluation) ``` > python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config th --split test --log_outputs --thai_tokenizer newmm/syllable/deepcut/cer ``` ### Eval results on Common Voice 7 "test": | | WER PyThaiNLP 2.3.1 | WER deepcut | SER | CER | |---------------------------------|---------------------|-------------|---------|---------| | Only Tokenization | 0.9524% | 2.5316% | 1.2346% | 0.1623% | | Cleaning rules and Tokenization | TBD | TBD | TBD | TBD | ## Usage ``` #load pretrained processor and model processor = Wav2Vec2Processor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") model = Wav2Vec2ForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") #function to resample to 16_000 def speech_file_to_array_fn(batch, text_col="sentence", fname_col="path", resampling_to=16000): speech_array, sampling_rate = torchaudio.load(batch[fname_col]) resampler=torchaudio.transforms.Resample(sampling_rate, resampling_to) batch["speech"] = resampler(speech_array)[0].numpy() batch["sampling_rate"] = resampling_to batch["target_text"] = batch[text_col] return batch #get 2 examples as sample input 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) #infer with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) >> Prediction: ['และ เขา ก็ สัมผัส ดีบุก', 'คุณ สามารถ รับทราบ เมื่อ ข้อความ นี้ ถูก อ่าน แล้ว'] >> Reference: ['และเขาก็สัมผัสดีบุก', 'คุณสามารถรับทราบเมื่อข้อความนี้ถูกอ่านแล้ว'] ``` ## Datasets Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with `pythainlp.tokenize.word_tokenize`. We preprocess the dataset using cleaning rules described in `notebooks/cv-preprocess.ipynb` by [@tann9949](https://github.com/tann9949). We then deduplicate and split as described in [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) in order to 1) avoid data leakage due to random splits after cleaning in [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) and 2) preserve the majority of the data for the training set. The dataset loading script is `scripts/th_common_voice_70.py`. You can use this scripts together with `train_cleand.tsv`, `validation_cleaned.tsv` and `test_cleaned.tsv` to have the same splits as we do. The resulting dataset is as follows: ``` DatasetDict({ train: Dataset({ features: ['path', 'sentence'], num_rows: 86586 }) test: Dataset({ features: ['path', 'sentence'], num_rows: 2502 }) validation: Dataset({ features: ['path', 'sentence'], num_rows: 3027 }) }) ``` ## Training We fintuned using the following configuration on a single V100 GPU and chose the checkpoint with the lowest validation loss. The finetuning script is `scripts/wav2vec2_finetune.py` ``` # create model model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-large-xlsr-53", attention_dropout=0.1, hidden_dropout=0.1, feat_proj_dropout=0.0, mask_time_prob=0.05, layerdrop=0.1, gradient_checkpointing=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer) ) model.freeze_feature_extractor() training_args = TrainingArguments( output_dir="../data/wav2vec2-large-xlsr-53-thai", group_by_length=True, per_device_train_batch_size=32, gradient_accumulation_steps=1, per_device_eval_batch_size=16, metric_for_best_model='wer', evaluation_strategy="steps", eval_steps=1000, logging_strategy="steps", logging_steps=1000, save_strategy="steps", save_steps=1000, num_train_epochs=100, fp16=True, learning_rate=1e-4, warmup_steps=1000, save_total_limit=3, report_to="tensorboard" ) ``` ## Evaluation We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evaluation codes can be found in `notebooks/wav2vec2_finetuning_tutorial.ipynb`. Benchmark is performed on `test-unique` split. | | WER PyThaiNLP 2.3.1 | WER deepcut | CER | |--------------------------------|---------------------|----------------|----------------| | [Kaldi from scratch](https://github.com/vistec-AI/commonvoice-th) | 23.04 | | 7.57 | | Ours without spell correction | 13.634024 | **8.152052** | **2.813019** | | Ours with spell correction | 17.996397 | 14.167975 | 5.225761 | | Google Web Speech API※ | 13.711234 | 10.860058 | 7.357340 | | Microsoft Bing Speech API※ | **12.578819** | 9.620991 | 5.016620 | | Amazon Transcribe※ | 21.86334 | 14.487553 | 7.077562 | | NECTEC AI for Thai Partii API※ | 20.105887 | 15.515631 | 9.551027 | ※ APIs are not finetuned with Common Voice 7.0 data ## LICENSE [cc-by-sa 4.0](https://github.com/vistec-AI/wav2vec2-large-xlsr-53-th/blob/main/LICENSE) ## Ackowledgements * model training and validation notebooks/scripts [@cstorm125](https://github.com/cstorm125/) * dataset cleaning scripts [@tann9949](https://github.com/tann9949) * dataset splits [@ekapolc](https://github.com/ekapolc/) and [@14mss](https://github.com/14mss) * running the training [@mrpeerat](https://github.com/mrpeerat) * spell correction [@wannaphong](https://github.com/wannaphong)

princeton-nlp/sup-simcse-roberta-large

princeton-nlp

transformers

feature-extraction

--- tags: - feature-extraction --- # Model Card for sup-simcse-roberta-large # Model Details ## Model Description - **Developed by:** Princeton-nlp - **Shared by [Optional]:** More information needed - **Model type:** Feature Extraction - **Language(s) (NLP):** More information needed - **License:** More information needed - **Related Models:** - **Parent Model:** RoBERTa-large - **Resources for more information:** - [GitHub Repo](https://github.com/princeton-nlp/SimCSE) - [Associated Paper](https://arxiv.org/abs/2104.08821) - [Blog Post]({0}) # Uses ## Direct Use This model can be used for the task of Feature Extraction ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # 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. More information needed for further recommendations. # Training Details ## Training Data The model craters note in the [Github Repository](https://github.com/princeton-nlp/SimCSE/blob/main/README.md) > We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k). ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf) > Our evaluation code for sentence embeddings is based on a modified version of [SentEval](https://github.com/facebookresearch/SentEval). It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See [associated paper](https://arxiv.org/pdf/2104.08821.pdf) (Appendix B) for evaluation details. ### Factors ### Metrics More information needed ## Results More information needed # Model Examination More information needed # 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 # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation **BibTeX:** ```bibtex @inproceedings{gao2021simcse, title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings}, author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi}, booktitle={Empirical Methods in Natural Language Processing (EMNLP)}, year={2021} } ``` # Glossary [optional] More information needed # More Information [optional] If you have any questions related to the code or the paper, feel free to email Tianyu (`[email protected]`) and Xingcheng (`[email protected]`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker! # Model Card Authors [optional] Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/sup-simcse-roberta-large") model = AutoModel.from_pretrained("princeton-nlp/sup-simcse-roberta-large") ``` </details>

roneneldan/TinyStories-33M

roneneldan

transformers

text-generation

--- datasets: - roneneldan/TinyStories --- Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 Based on GPT-Neo architecture. License: mit --- hyperparams used to train this model: lr = 5e-4, lr_schedule = constant, wd=0.1, adam_beta1=0.9, adam_beta2 = 0.95, context_length=512, batch_size=80, gradient_accumulation_steps=16 ------ EXAMPLE USAGE --- from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M') tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") prompt = "Once upon a time there was" input_ids = tokenizer.encode(prompt, return_tensors="pt") # Generate completion output = model.generate(input_ids, max_length = 1000, num_beams=1) # Decode the completion output_text = tokenizer.decode(output[0], skip_special_tokens=True) # Print the generated text print(output_text)

vinai/phobert-large

vinai

transformers

fill-mask

# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance. - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744): @article{phobert, title = {{PhoBERT: Pre-trained language models for Vietnamese}}, author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, journal = {Findings of EMNLP}, year = {2020} } **Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software. For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!

fxmarty/tiny-doc-qa-vision-encoder-decoder

fxmarty

transformers

document-question-answering

--- license: mit pipeline_tag: document-question-answering --- For testing purposes only

imvladikon/wav2vec2-xls-r-300m-hebrew

imvladikon

transformers

automatic-speech-recognition

--- language: - he tags: - automatic-speech-recognition - generated_from_trainer - he - hf-asr-leaderboard - robust-speech-event base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-xls-r-300m-hebrew results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Custom Dataset type: custom args: he metrics: - type: wer value: 23.18 name: Test WER --- <!-- 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. --> # wav2vec2-xls-r-300m-hebrew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model. Small dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|4.19| 20306 | 28 | | |dev |1.05| 5076 | 7 | | Large dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|12.3| 90777 | 69 | | |dev |2.39| 20246 | 14* | | (*weakly labeled data wasn't used in validation set) After firts training it achieves: on small dataset - Loss: 0.5438 - WER: 0.1773 on large dataset - WER: 0.3811 after second training: on small dataset - WER: 0.1697 on large dataset - Loss: 0.4502 - WER: 0.2318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters #### First training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 3.15 | 1000 | 0.5203 | 0.4333 | | 1.4284 | 6.31 | 2000 | 0.4816 | 0.3951 | | 1.4284 | 9.46 | 3000 | 0.4315 | 0.3546 | | 1.283 | 12.62 | 4000 | 0.4278 | 0.3404 | | 1.283 | 15.77 | 5000 | 0.4090 | 0.3054 | | 1.1777 | 18.93 | 6000 | 0.3893 | 0.3006 | | 1.1777 | 22.08 | 7000 | 0.3968 | 0.2857 | | 1.0994 | 25.24 | 8000 | 0.3892 | 0.2751 | | 1.0994 | 28.39 | 9000 | 0.4061 | 0.2690 | | 1.0323 | 31.54 | 10000 | 0.4114 | 0.2507 | | 1.0323 | 34.7 | 11000 | 0.4021 | 0.2508 | | 0.9623 | 37.85 | 12000 | 0.4032 | 0.2378 | | 0.9623 | 41.01 | 13000 | 0.4148 | 0.2374 | | 0.9077 | 44.16 | 14000 | 0.4350 | 0.2323 | | 0.9077 | 47.32 | 15000 | 0.4515 | 0.2246 | | 0.8573 | 50.47 | 16000 | 0.4474 | 0.2180 | | 0.8573 | 53.63 | 17000 | 0.4649 | 0.2171 | | 0.8083 | 56.78 | 18000 | 0.4455 | 0.2102 | | 0.8083 | 59.94 | 19000 | 0.4587 | 0.2092 | | 0.769 | 63.09 | 20000 | 0.4794 | 0.2012 | | 0.769 | 66.25 | 21000 | 0.4845 | 0.2007 | | 0.7308 | 69.4 | 22000 | 0.4937 | 0.2008 | | 0.7308 | 72.55 | 23000 | 0.4920 | 0.1895 | | 0.6927 | 75.71 | 24000 | 0.5179 | 0.1911 | | 0.6927 | 78.86 | 25000 | 0.5202 | 0.1877 | | 0.6622 | 82.02 | 26000 | 0.5266 | 0.1840 | | 0.6622 | 85.17 | 27000 | 0.5351 | 0.1854 | | 0.6315 | 88.33 | 28000 | 0.5373 | 0.1811 | | 0.6315 | 91.48 | 29000 | 0.5331 | 0.1792 | | 0.6075 | 94.64 | 30000 | 0.5390 | 0.1779 | | 0.6075 | 97.79 | 31000 | 0.5459 | 0.1773 | #### Second training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.7 | 1000 | 0.5371 | 0.3811 | | 1.3606 | 1.41 | 2000 | 0.5247 | 0.3902 | | 1.3606 | 2.12 | 3000 | 0.5126 | 0.3859 | | 1.3671 | 2.82 | 4000 | 0.5062 | 0.3828 | | 1.3671 | 3.53 | 5000 | 0.4979 | 0.3672 | | 1.3421 | 4.23 | 6000 | 0.4906 | 0.3816 | | 1.3421 | 4.94 | 7000 | 0.4784 | 0.3651 | | 1.328 | 5.64 | 8000 | 0.4810 | 0.3669 | | 1.328 | 6.35 | 9000 | 0.4747 | 0.3597 | | 1.3109 | 7.05 | 10000 | 0.4813 | 0.3808 | | 1.3109 | 7.76 | 11000 | 0.4631 | 0.3561 | | 1.2873 | 8.46 | 12000 | 0.4603 | 0.3431 | | 1.2873 | 9.17 | 13000 | 0.4579 | 0.3533 | | 1.2661 | 9.87 | 14000 | 0.4471 | 0.3365 | | 1.2661 | 10.58 | 15000 | 0.4584 | 0.3437 | | 1.249 | 11.28 | 16000 | 0.4461 | 0.3454 | | 1.249 | 11.99 | 17000 | 0.4482 | 0.3367 | | 1.2322 | 12.69 | 18000 | 0.4464 | 0.3335 | | 1.2322 | 13.4 | 19000 | 0.4427 | 0.3454 | | 1.22 | 14.1 | 20000 | 0.4440 | 0.3395 | | 1.22 | 14.81 | 21000 | 0.4459 | 0.3378 | | 1.2044 | 15.51 | 22000 | 0.4406 | 0.3199 | | 1.2044 | 16.22 | 23000 | 0.4398 | 0.3155 | | 1.1913 | 16.92 | 24000 | 0.4237 | 0.3150 | | 1.1913 | 17.63 | 25000 | 0.4287 | 0.3279 | | 1.1705 | 18.34 | 26000 | 0.4253 | 0.3103 | | 1.1705 | 19.04 | 27000 | 0.4234 | 0.3098 | | 1.1564 | 19.75 | 28000 | 0.4174 | 0.3076 | | 1.1564 | 20.45 | 29000 | 0.4260 | 0.3160 | | 1.1461 | 21.16 | 30000 | 0.4235 | 0.3036 | | 1.1461 | 21.86 | 31000 | 0.4309 | 0.3055 | | 1.1285 | 22.57 | 32000 | 0.4264 | 0.3006 | | 1.1285 | 23.27 | 33000 | 0.4201 | 0.2880 | | 1.1135 | 23.98 | 34000 | 0.4131 | 0.2975 | | 1.1135 | 24.68 | 35000 | 0.4202 | 0.2849 | | 1.0968 | 25.39 | 36000 | 0.4105 | 0.2888 | | 1.0968 | 26.09 | 37000 | 0.4210 | 0.2834 | | 1.087 | 26.8 | 38000 | 0.4123 | 0.2843 | | 1.087 | 27.5 | 39000 | 0.4216 | 0.2803 | | 1.0707 | 28.21 | 40000 | 0.4161 | 0.2787 | | 1.0707 | 28.91 | 41000 | 0.4186 | 0.2740 | | 1.0575 | 29.62 | 42000 | 0.4118 | 0.2845 | | 1.0575 | 30.32 | 43000 | 0.4243 | 0.2773 | | 1.0474 | 31.03 | 44000 | 0.4221 | 0.2707 | | 1.0474 | 31.73 | 45000 | 0.4138 | 0.2700 | | 1.0333 | 32.44 | 46000 | 0.4102 | 0.2638 | | 1.0333 | 33.15 | 47000 | 0.4162 | 0.2650 | | 1.0191 | 33.85 | 48000 | 0.4155 | 0.2636 | | 1.0191 | 34.56 | 49000 | 0.4129 | 0.2656 | | 1.0087 | 35.26 | 50000 | 0.4157 | 0.2632 | | 1.0087 | 35.97 | 51000 | 0.4090 | 0.2654 | | 0.9901 | 36.67 | 52000 | 0.4183 | 0.2587 | | 0.9901 | 37.38 | 53000 | 0.4251 | 0.2648 | | 0.9795 | 38.08 | 54000 | 0.4229 | 0.2555 | | 0.9795 | 38.79 | 55000 | 0.4176 | 0.2546 | | 0.9644 | 39.49 | 56000 | 0.4223 | 0.2513 | | 0.9644 | 40.2 | 57000 | 0.4244 | 0.2530 | | 0.9534 | 40.9 | 58000 | 0.4175 | 0.2538 | | 0.9534 | 41.61 | 59000 | 0.4213 | 0.2505 | | 0.9397 | 42.31 | 60000 | 0.4275 | 0.2565 | | 0.9397 | 43.02 | 61000 | 0.4315 | 0.2528 | | 0.9269 | 43.72 | 62000 | 0.4316 | 0.2501 | | 0.9269 | 44.43 | 63000 | 0.4247 | 0.2471 | | 0.9175 | 45.13 | 64000 | 0.4376 | 0.2469 | | 0.9175 | 45.84 | 65000 | 0.4335 | 0.2450 | | 0.9026 | 46.54 | 66000 | 0.4336 | 0.2452 | | 0.9026 | 47.25 | 67000 | 0.4400 | 0.2427 | | 0.8929 | 47.95 | 68000 | 0.4382 | 0.2429 | | 0.8929 | 48.66 | 69000 | 0.4361 | 0.2415 | | 0.8786 | 49.37 | 70000 | 0.4413 | 0.2398 | | 0.8786 | 50.07 | 71000 | 0.4392 | 0.2415 | | 0.8714 | 50.78 | 72000 | 0.4345 | 0.2406 | | 0.8714 | 51.48 | 73000 | 0.4475 | 0.2402 | | 0.8589 | 52.19 | 74000 | 0.4473 | 0.2374 | | 0.8589 | 52.89 | 75000 | 0.4457 | 0.2357 | | 0.8493 | 53.6 | 76000 | 0.4462 | 0.2366 | | 0.8493 | 54.3 | 77000 | 0.4494 | 0.2356 | | 0.8395 | 55.01 | 78000 | 0.4472 | 0.2352 | | 0.8395 | 55.71 | 79000 | 0.4490 | 0.2339 | | 0.8295 | 56.42 | 80000 | 0.4489 | 0.2318 | | 0.8295 | 57.12 | 81000 | 0.4469 | 0.2320 | | 0.8225 | 57.83 | 82000 | 0.4478 | 0.2321 | | 0.8225 | 58.53 | 83000 | 0.4525 | 0.2326 | | 0.816 | 59.24 | 84000 | 0.4532 | 0.2316 | | 0.816 | 59.94 | 85000 | 0.4502 | 0.2318 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

mosaicml/mpt-7b

mosaicml

transformers

text-generation

--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- # MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 6.7B | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 50432 | | sequence length | 2048 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 | | C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 | | The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 | | RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 | | S2ORC | 48.85 B | 0.033 | 33 B | 0.68 | | RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 | | RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-05-05}, urldate = {2023-05-05} } ```

codellama/CodeLlama-7b-hf

codellama

transformers

text-generation

--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | ## Model Use To use this model, please make sure to install transformers from `main` until the next version is released: ```bash pip install git+https://github.com/huggingface/transformers.git@main accelerate ``` Model capabilities: - [x] Code completion. - [x] Infilling. - [ ] Instructions / chat. - [ ] Python specialist. ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B and 34B parameters. **This repository contains the base model of 7B parameters.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct 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** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or it's [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **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 Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are 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, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).

sentence-transformers/paraphrase-MiniLM-L3-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - flax-sentence-embeddings/stackexchange_xml - s2orc - ms_marco - wiki_atomic_edits - snli - multi_nli - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/flickr30k-captions - embedding-data/coco_captions - embedding-data/sentence-compression - embedding-data/QQP - yahoo_answers_topics --- # sentence-transformers/paraphrase-MiniLM-L3-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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/paraphrase-MiniLM-L3-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```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) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L3-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L3-v2') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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/paraphrase-MiniLM-L3-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## 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", } ```

t5-11b

transformers

translation

--- language: - en - fr - ro - de - multilingual license: apache-2.0 tags: - summarization - translation datasets: - c4 inference: false --- # Model Card for T5 11B  # 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. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-11B is the checkpoint with 11 billion parameters. - **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - **Model type:** Language model - **Language(s) (NLP):** English, French, Romanian, German - **License:** Apache 2.0 - **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) - **Resources for more information:** - [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5) # Uses ## Direct Use and Downstream Use The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Recommendations More information needed. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr) 2. **Datasets used for Supervised text-to-text language modeling objective** - Sentence acceptability judgment - CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471) - Sentiment analysis - SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - Paraphrasing/sentence similarity - MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002) - STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055) - QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - Natural language inference - MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426) - QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250) - RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9) - CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf) - Sentence completion - COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning) - Word sense disambiguation - WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121) - Question answering - MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023) - ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885) - BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044) ## Training Procedure In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write: > In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details. ## Results For full results for T5-11B, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14. # 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 - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model ## Disclaimer **Before `transformers` v3.5.0**, due do its immense size, `t5-11b` required some special treatment. If you're using transformers `<= v3.4.0`, `t5-11b` should be loaded with flag `use_cdn` set to `False` as follows: ```python t5 = transformers.T5ForConditionalGeneration.from_pretrained('t5-11b', use_cdn = False) ``` Secondly, a single GPU will most likely not have enough memory to even load the model into memory as the weights alone amount to over 40 GB. - Model parallelism has to be used here to overcome this problem as is explained in this [PR](https://github.com/huggingface/transformers/pull/3578). - DeepSpeed's ZeRO-Offload is another approach as explained in this [post](https://github.com/huggingface/transformers/issues/9996). See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more context.

roberta-large-mnli

transformers

text-classification

--- language: - en license: mit tags: - autogenerated-modelcard datasets: - multi_nli - wikipedia - bookcorpus --- # roberta-large-mnli ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-author) ## Model Details **Model Description:** roberta-large-mnli is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://huggingface.co/datasets/multi_nli) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective. - **Developed by:** See [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for model developers - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** MIT - **Parent Model:** This model is a fine-tuned version of the RoBERTa large model. Users should see the [RoBERTa large model card](https://huggingface.co/roberta-large) for relevant information. - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/1907.11692) - [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) ## How to Get Started with the Model Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so: ```python from transformers import pipeline classifier = pipeline('zero-shot-classification', model='roberta-large-mnli') ``` You can then use this pipeline to classify sequences into any of the class names you specify. For example: ```python sequence_to_classify = "one day I will see the world" candidate_labels = ['travel', 'cooking', 'dancing'] classifier(sequence_to_classify, candidate_labels) ``` ## Uses #### Direct Use This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the [GitHub repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for examples) and zero-shot sequence classification. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, 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. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate 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)). The [RoBERTa large model card](https://huggingface.co/roberta-large) notes that: "The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral." Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python sequence_to_classify = "The CEO had a strong handshake." candidate_labels = ['male', 'female'] hypothesis_template = "This text speaks about a {} profession." classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template) ``` Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Training #### Training Data This model was fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. Also see the [MNLI data card](https://huggingface.co/datasets/multi_nli) for more information. As described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The RoBERTa model was pretrained on the reunion of five datasets: > > - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books; > - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ; > - [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news articles crawled between September 2016 and February 2019. > - [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to train GPT-2, > - [Stories](https://arxiv.org/abs/1806.02847), a dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. > > Together theses datasets weight 160GB of text. Also see the [bookcorpus data card](https://huggingface.co/datasets/bookcorpus) and the [wikipedia data card](https://huggingface.co/datasets/wikipedia) for additional information. #### Training Procedure ##### Preprocessing As described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of > the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked > with `<s>` and the end of one by `</s>` > > 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. > > Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed). ##### Pretraining Also as described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The > optimizer used is Adam with a learning rate of 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and > \\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 30,000 steps and linear decay of the learning > rate after. ## Evaluation The following evaluation information is extracted from the associated [GitHub repo for RoBERTa](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta). #### Testing Data, Factors and Metrics The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics: - **Dataset:** Part of [GLUE (Wang et al., 2019)](https://arxiv.org/pdf/1804.07461.pdf), the General Language Understanding Evaluation benchmark, a collection of 9 datasets for evaluating natural language understanding systems. Specifically, the model was evaluated on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. See the [GLUE data card](https://huggingface.co/datasets/glue) or [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) for further information. - **Tasks:** NLI. [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) describe the inference task for MNLI as: > The Multi-Genre Natural Language Inference Corpus [(Williams et al., 2018)](https://arxiv.org/abs/1704.05426) is a crowd-sourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. We use the standard test set, for which we obtained private labels from the authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) sections. We also use and recommend the SNLI corpus [(Bowman et al., 2015)](https://arxiv.org/abs/1508.05326) as 550k examples of auxiliary training data. - **Metrics:** Accuracy - **Dataset:** [XNLI (Conneau et al., 2018)](https://arxiv.org/pdf/1809.05053.pdf), the extension of the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus to 15 languages: English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. See the [XNLI data card](https://huggingface.co/datasets/xnli) or [Conneau et al. (2018)](https://arxiv.org/pdf/1809.05053.pdf) for further information. - **Tasks:** Translate-test (e.g., the model is used to translate input sentences in other languages to the training language) - **Metrics:** Accuracy #### Results GLUE test results (dev set, single model, single-task fine-tuning): 90.2 on MNLI XNLI test results: | Task | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | |:----:|:--:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | |91.3|82.91|84.27|81.24|81.74|83.13|78.28|76.79|76.64|74.17|74.05| 77.5| 70.9|66.65|66.81| ## 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). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1907.11692.pdf). - **Hardware Type:** 1024 V100 GPUs - **Hours used:** 24 hours (one day) - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/pdf/1907.11692.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @article{liu2019roberta, title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach}, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, journal={arXiv preprint arXiv:1907.11692}, year = {2019}, } ```

microsoft/DialoGPT-medium

microsoft

transformers

conversational

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

yiyanghkust/finbert-esg-9-categories

yiyanghkust

transformers

text-classification

--- language: "en" tags: - financial-text-analysis - esg - environmental-social-corporate-governance widget: - text: "For 2002, our total net emissions were approximately 60 million metric tons of CO2 equivalents for all businesses and operations we have financial interests in, based on its equity share in those businesses and operations. " --- ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. **FinBERT-esg-9-categories** is a FinBERT model fine-tuned on about 14,000 manually annotated sentences from firms' ESG reports and annual reports. **finbert-esg-9-categories** classifies a text into nine fine-grained ESG topics: *Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG*. This model complements [**finbert-esg**](https://huggingface.co/yiyanghkust/finbert-esg) which classifies a text into four coarse-grained ESG themes (*E, S, G or None*). Detailed description of the nine fine-grained ESG topic definition, some examples for each topic, training sample, and the model’s performance can be found [**here**](https://www.allenhuang.org/uploads/2/6/5/5/26555246/esg_9-class_descriptions.pdf). **Input**: A text. **Output**: Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, or Non-ESG. # How to use You can use this model with Transformers pipeline for fine-grained ESG 9 categories classification. ```python from transformers import BertTokenizer, BertForSequenceClassification, pipeline finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg-9-categories',num_labels=9) tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg-9-categories') nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) results = nlp('For 2002, our total net emissions were approximately 60 million metric tons of CO2 equivalents for all businesses and operations we have financial interests in, based on its equity share in those businesses and operations.') print(results) # [{'label': 'Climate Change', 'score': 0.9955655932426453}] ``` If you use the model in your academic work, please cite the following paper: Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

siebert/sentiment-roberta-large-english

siebert

transformers

text-classification

--- language: "en" tags: - sentiment - twitter - reviews - siebert --- ## SiEBERT - English-Language Sentiment Classification # Overview This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large) ([Liu et al. 2019](https://arxiv.org/pdf/1907.11692.pdf)). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). Consequently, it outperforms models trained on only one type of text (e.g., movie reviews from the popular SST-2 benchmark) when used on new data as shown below. # Predictions on a data set If you want to predict sentiment for your own data, we provide an example script via [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb). You can load your data to a Google Drive and run the script for free on a Colab GPU. Set-up only takes a few minutes. We suggest that you manually label a subset of your data to evaluate performance for your use case. For performance benchmark values across various sentiment analysis contexts, please refer to our paper ([Hartmann et al. 2022](https://www.sciencedirect.com/science/article/pii/S0167811622000477?via%3Dihub)). [](https://colab.research.google.com/github/chrsiebert/sentiment-roberta-large-english/blob/main/sentiment_roberta_prediction_example.ipynb) # Use in a Hugging Face pipeline The easiest way to use the model for single predictions is Hugging Face's [sentiment analysis pipeline](https://huggingface.co/transformers/quicktour.html#getting-started-on-a-task-with-a-pipeline), which only needs a couple lines of code as shown in the following example: ``` from transformers import pipeline sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english") print(sentiment_analysis("I love this!")) ``` [](https://colab.research.google.com/github/chrsiebert/sentiment-roberta-large-english/blob/main/sentiment_roberta_pipeline.ipynb) # Use for further fine-tuning The model can also be used as a starting point for further fine-tuning of RoBERTa on your specific data. Please refer to Hugging Face's [documentation](https://huggingface.co/docs/transformers/training) for further details and example code. # Performance To evaluate the performance of our general-purpose sentiment analysis model, we set aside an evaluation set from each data set, which was not used for training. On average, our model outperforms a [DistilBERT-based model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) (which is solely fine-tuned on the popular SST-2 data set) by more than 15 percentage points (78.1 vs. 93.2 percent, see table below). As a robustness check, we evaluate the model in a leave-one-out manner (training on 14 data sets, evaluating on the one left out), which decreases model performance by only about 3 percentage points on average and underscores its generalizability. Model performance is given as evaluation set accuracy in percent. |Dataset|DistilBERT SST-2|This model| |---|---|---| |McAuley and Leskovec (2013) (Reviews)|84.7|98.0| |McAuley and Leskovec (2013) (Review Titles)|65.5|87.0| |Yelp Academic Dataset|84.8|96.5| |Maas et al. (2011)|80.6|96.0| |Kaggle|87.2|96.0| |Pang and Lee (2005)|89.7|91.0| |Nakov et al. (2013)|70.1|88.5| |Shamma (2009)|76.0|87.0| |Blitzer et al. (2007) (Books)|83.0|92.5| |Blitzer et al. (2007) (DVDs)|84.5|92.5| |Blitzer et al. (2007) (Electronics)|74.5|95.0| |Blitzer et al. (2007) (Kitchen devices)|80.0|98.5| |Pang et al. (2002)|73.5|95.5| |Speriosu et al. (2011)|71.5|85.5| |Hartmann et al. (2019)|65.5|98.0| |**Average**|**78.1**|**93.2**| # Fine-tuning hyperparameters - learning_rate = 2e-5 - num_train_epochs = 3.0 - warmump_steps = 500 - weight_decay = 0.01 Other values were left at their defaults as listed [here](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments). # Citation and contact Please cite [this paper](https://www.sciencedirect.com/science/article/pii/S0167811622000477) (Published in the [IJRM](https://www.journals.elsevier.com/international-journal-of-research-in-marketing)) when you use our model. Feel free to reach out to [[email protected]](mailto:[email protected]) with any questions or feedback you may have. ``` @article{hartmann2023, title = {More than a Feeling: Accuracy and Application of Sentiment Analysis}, journal = {International Journal of Research in Marketing}, volume = {40}, number = {1}, pages = {75-87}, year = {2023}, doi = {https://doi.org/10.1016/j.ijresmar.2022.05.005}, url = {https://www.sciencedirect.com/science/article/pii/S0167811622000477}, author = {Jochen Hartmann and Mark Heitmann and Christian Siebert and Christina Schamp}, } ```

kandinsky-community/kandinsky-2-2-decoder

kandinsky-community

diffusers

text-to-image

--- license: apache-2.0 prior: - kandinsky-community/kandinsky-2-2-prior tags: - text-to-image - kandinsky inference: false --- # Kandinsky 2.2 Kandinsky inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) ## Usage Kandinsky 2.2 is available in diffusers! ```python pip install diffusers transformers accelerate ``` ### Text to image ```python from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "portrait of a young women, blue eyes, cinematic" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0] image.save("portrait.png") ```  ### Text Guided Image-to-Image Generation ```python from PIL import Image import requests from io import BytesIO url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) original_image = Image.open(BytesIO(response.content)).convert("RGB") original_image = original_image.resize((768, 512)) ```  ```python from diffusers import AutoPipelineForImage2Image import torch pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, image=original_image, strength=0.3, height=768, width=768).images[0] out.images[0].save("fantasy_land.png") ```  ### Interpolate ```python from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline from diffusers.utils import load_image import PIL import torch pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior.to("cuda") img1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) img2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg" ) # add all the conditions we want to interpolate, can be either text or image images_texts = ["a cat", img1, img2] # specify the weights for each condition in images_texts weights = [0.3, 0.3, 0.4] # We can leave the prompt empty prompt = "" prior_out = pipe_prior.interpolate(images_texts, weights) pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.to("cuda") image = pipe(**prior_out, height=768, width=768).images[0] image.save("starry_cat.png") ```  ## Model Architecture ### Overview Kandinsky 2.2 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. <p float="left"> <img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/> </p> Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [CLIP-ViT-G model](https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K). The trained image prior model is then used to generate CLIP image embeddings for input text prompts. Both the input text prompts and its CLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image. ### Details The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution). The main Text2Image diffusion model was trained on [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) and then fine-tuned with a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources. The main change in Kandinsky 2.2 is the replacement of CLIP-ViT-G. Its image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing its overall performance. Due to the switch CLIP model, the image prior model was retrained, and the Text2Image diffusion model was fine-tuned for 2000 iterations. Kandinsky 2.2 was trained on data of various resolutions, from 512 x 512 to 1536 x 1536, and also as different aspect ratios. As a result, Kandinsky 2.2 can generate 1024 x 1024 outputs with any aspect ratio. ### Evaluation We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID. FID metric values ​​for generative models on COCO_30k | | FID (30k)| |:------|----:| | eDiff-I (2022) | 6.95 | | Image (2022) | 7.27 | | Kandinsky 2.1 (2023) | 8.21| | Stable Diffusion 2.1 (2022) | 8.59 | | GigaGAN, 512x512 (2023) | 9.09 | | DALL-E 2 (2022) | 10.39 | | GLIDE (2022) | 12.24 | | Kandinsky 1.0 (2022) | 15.40 | | DALL-E (2021) | 17.89 | | Kandinsky 2.0 (2022) | 20.00 | | GLIGEN (2022) | 21.04 | For more information, please refer to the upcoming technical report. ## BibTex If you find this repository useful in your research, please cite: ``` @misc{kandinsky 2.2, title = {kandinsky 2.2}, author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov}, year = {2023}, howpublished = {}, } ```

Helsinki-NLP/opus-mt-en-de

Helsinki-NLP

transformers

translation

--- tags: - translation license: cc-by-4.0 --- ### opus-mt-en-de ## 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: English - Target Language: German - **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: [en-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-de/README.md) #### Training Data ##### Preprocessing * pre-processing: normalization + SentencePiece * dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.test.txt) ## Evaluation #### Results * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.eval.txt) #### Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.en.de | 23.5 | 0.540 | | news-test2008.en.de | 23.5 | 0.529 | | newstest2009.en.de | 22.3 | 0.530 | | newstest2010.en.de | 24.9 | 0.544 | | newstest2011.en.de | 22.5 | 0.524 | | newstest2012.en.de | 23.0 | 0.525 | | newstest2013.en.de | 26.9 | 0.553 | | newstest2015-ende.en.de | 31.1 | 0.594 | | newstest2016-ende.en.de | 37.0 | 0.636 | | newstest2017-ende.en.de | 29.9 | 0.586 | | newstest2018-ende.en.de | 45.2 | 0.690 | | newstest2019-ende.en.de | 40.9 | 0.654 | | Tatoeba.en.de | 47.3 | 0.664 | ## 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-en-de") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de") ```

stablediffusionapi/realistic-vision-v51

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Realistic Vision V5.1 API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "realistic-vision-v51" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/realistic-vision-v51) Model link: [View model](https://stablediffusionapi.com/models/realistic-vision-v51) Credits: [View credits](https://civitai.com/?query=Realistic%20Vision%20V5.1) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "realistic-vision-v51", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

hotshotco/Hotshot-XL

hotshotco

diffusers

text-to-video

--- license: openrail++ tags: - text-to-video - stable-diffusion ---  <font size="32">**Try Hotshot-XL yourself here**: https://www.hotshot.co</font> Hotshot-XL is an AI text-to-GIF model trained to work alongside [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). Hotshot-XL can generate GIFs with any fine-tuned SDXL model. This means two things: 1. You’ll be able to make GIFs with any existing or newly fine-tuned SDXL model you may want to use. 2. If you'd like to make GIFs of personalized subjects, you can load your own SDXL based LORAs, and not have to worry about fine-tuning Hotshot-XL. This is awesome because it’s usually much easier to find suitable images for training data than it is to find videos. It also hopefully fits into everyone's existing LORA usage/workflows :) See more [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-personalized-loras). Hotshot-XL is compatible with SDXL ControlNet to make GIFs in the composition/layout you’d like. See [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-controlnet) for more info. Hotshot-XL was trained to generate 1 second GIFs at 8 FPS. Hotshot-XL was trained on various aspect ratios. For best results with the base Hotshot-XL model, we recommend using it with an SDXL model that has been fine-tuned with 512x512 images. You can find an SDXL model we fine-tuned for 512x512 resolutions [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-personalized-loras).   Source code is available at https://github.com/hotshotco/Hotshot-XL. # Model Description - **Developed by**: Natural Synthetics Inc. - **Model type**: Diffusion-based text-to-GIF generative model - **License**: [CreativeML Open RAIL++-M License](https://huggingface.co/hotshotco/Hotshot-XL/raw/main/LICENSE.md) - **Model Description**: This is a model that can be used to generate and modify GIFs based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L). - **Resources for more information**: Check out our [GitHub Repository](https://github.com/hotshotco/Hotshot-XL). # Limitations and Bias ## Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with 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. ## Bias While the capabilities of video generation models are impressive, they can also reinforce or exacerbate social biases.

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> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. ************* 🌟**Updates**🌟 ************* - 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire: - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [masive 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/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.

google/long-t5-tglobal-large

google

transformers

text2text-generation

--- license: apache-2.0 language: en --- # LongT5 (transient-global attention, large-sized model) LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x). Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence. LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens). Results of LongT5 (transient-global attention, large-sized model) fine-tuned on multiple (summarization, QA) tasks. | Dataset | Rouge-1 | Rouge-2 | Rouge-Lsum | | --- | --- | --- | --- | | arXiv (16k input) | 48.28 | 21.63 | 44.11 | | PubMed (16k input) | 49.98 | 24.69 | 46.46 | | BigPatent (16k input) | 70.38 | 56.81 | 62.73 | | MultiNews (8k input) | 47.18 | 18.44 | 24.18 | | MediaSum (4k input) | 35.54 | 19.04 | 32.20 | | CNN / DailyMail (4k input) | 42.49 | 20.51 | 40.18 | | Dataset | EM | F1 | | --- | --- | --- | | Natural Questions (4k input) | 60.77 | 65.38 | | Trivia QA (16k input) | 78.38 | 82.45 | ## Intended uses & limitations The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you. ### How to use ```python from transformers import AutoTokenizer, LongT5Model tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-large") model = LongT5Model.from_pretrained("google/long-t5-tglobal-large") 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{guo2021longt5, title={LongT5: Efficient Text-To-Text Transformer for Long Sequences}, author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei}, journal={arXiv preprint arXiv:2112.07916}, year={2021} } ```

jinaai/jina-embeddings-v2-small-en

jinaai

sentence-transformers

feature-extraction

--- tags: - finetuner - mteb - sentence-transformers - feature-extraction - sentence-similarity datasets: - jinaai/negation-dataset language: en inference: false license: apache-2.0 model-index: - name: jina-embedding-s-en-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.35820895522387 - type: ap value: 33.99931933598115 - type: f1 value: 65.3853685535555 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 82.90140000000001 - type: ap value: 78.01434597815617 - type: f1 value: 82.83357802722676 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.88999999999999 - type: f1 value: 39.209432767163456 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 23.257 - type: map_at_10 value: 37.946000000000005 - type: map_at_100 value: 39.17 - type: map_at_1000 value: 39.181 - type: map_at_3 value: 32.99 - type: map_at_5 value: 35.467999999999996 - type: mrr_at_1 value: 23.541999999999998 - type: mrr_at_10 value: 38.057 - type: mrr_at_100 value: 39.289 - type: mrr_at_1000 value: 39.299 - type: mrr_at_3 value: 33.096 - type: mrr_at_5 value: 35.628 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 46.729 - type: ndcg_at_100 value: 51.900999999999996 - type: ndcg_at_1000 value: 52.16 - type: ndcg_at_3 value: 36.323 - type: ndcg_at_5 value: 40.766999999999996 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 7.510999999999999 - type: precision_at_100 value: 0.976 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.339 - type: precision_at_5 value: 11.350999999999999 - type: recall_at_1 value: 23.257 - type: recall_at_10 value: 75.107 - type: recall_at_100 value: 97.58200000000001 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 46.017 - type: recall_at_5 value: 56.757000000000005 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.02420878391967 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.16136856000258 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.61809790513646 - type: mrr value: 73.07215406938397 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.0167350090749 - type: cos_sim_spearman value: 80.51569002630401 - type: euclidean_pearson value: 81.46820525099726 - type: euclidean_spearman value: 80.51569002630401 - type: manhattan_pearson value: 81.35596555056757 - type: manhattan_spearman value: 80.12592210903303 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.25 - type: f1 value: 77.34950913540605 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.57238596005698 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.066444306196683 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.891000000000002 - type: map_at_10 value: 42.772 - type: map_at_100 value: 44.108999999999995 - type: map_at_1000 value: 44.236 - type: map_at_3 value: 39.289 - type: map_at_5 value: 41.113 - type: mrr_at_1 value: 39.342 - type: mrr_at_10 value: 48.852000000000004 - type: mrr_at_100 value: 49.534 - type: mrr_at_1000 value: 49.582 - type: mrr_at_3 value: 46.089999999999996 - type: mrr_at_5 value: 47.685 - type: ndcg_at_1 value: 39.342 - type: ndcg_at_10 value: 48.988 - type: ndcg_at_100 value: 53.854 - type: ndcg_at_1000 value: 55.955 - type: ndcg_at_3 value: 43.877 - type: ndcg_at_5 value: 46.027 - type: precision_at_1 value: 39.342 - type: precision_at_10 value: 9.285 - type: precision_at_100 value: 1.488 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 20.696 - type: precision_at_5 value: 14.878 - type: recall_at_1 value: 31.891000000000002 - type: recall_at_10 value: 60.608 - type: recall_at_100 value: 81.025 - type: recall_at_1000 value: 94.883 - type: recall_at_3 value: 45.694 - type: recall_at_5 value: 51.684 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.778 - type: map_at_10 value: 37.632 - type: map_at_100 value: 38.800000000000004 - type: map_at_1000 value: 38.934999999999995 - type: map_at_3 value: 35.293 - type: map_at_5 value: 36.547000000000004 - type: mrr_at_1 value: 35.35 - type: mrr_at_10 value: 42.936 - type: mrr_at_100 value: 43.69 - type: mrr_at_1000 value: 43.739 - type: mrr_at_3 value: 41.062 - type: mrr_at_5 value: 42.097 - type: ndcg_at_1 value: 35.35 - type: ndcg_at_10 value: 42.528 - type: ndcg_at_100 value: 46.983000000000004 - type: ndcg_at_1000 value: 49.187999999999995 - type: ndcg_at_3 value: 39.271 - type: ndcg_at_5 value: 40.654 - type: precision_at_1 value: 35.35 - type: precision_at_10 value: 7.828 - type: precision_at_100 value: 1.3010000000000002 - type: precision_at_1000 value: 0.17700000000000002 - type: precision_at_3 value: 18.96 - type: precision_at_5 value: 13.120999999999999 - type: recall_at_1 value: 28.778 - type: recall_at_10 value: 50.775000000000006 - type: recall_at_100 value: 69.66799999999999 - type: recall_at_1000 value: 83.638 - type: recall_at_3 value: 40.757 - type: recall_at_5 value: 44.86 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 37.584 - type: map_at_10 value: 49.69 - type: map_at_100 value: 50.639 - type: map_at_1000 value: 50.702999999999996 - type: map_at_3 value: 46.61 - type: map_at_5 value: 48.486000000000004 - type: mrr_at_1 value: 43.009 - type: mrr_at_10 value: 52.949999999999996 - type: mrr_at_100 value: 53.618 - type: mrr_at_1000 value: 53.65299999999999 - type: mrr_at_3 value: 50.605999999999995 - type: mrr_at_5 value: 52.095 - type: ndcg_at_1 value: 43.009 - type: ndcg_at_10 value: 55.278000000000006 - type: ndcg_at_100 value: 59.134 - type: ndcg_at_1000 value: 60.528999999999996 - type: ndcg_at_3 value: 50.184 - type: ndcg_at_5 value: 52.919000000000004 - type: precision_at_1 value: 43.009 - type: precision_at_10 value: 8.821 - type: precision_at_100 value: 1.161 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 22.424 - type: precision_at_5 value: 15.436 - type: recall_at_1 value: 37.584 - type: recall_at_10 value: 68.514 - type: recall_at_100 value: 85.099 - type: recall_at_1000 value: 95.123 - type: recall_at_3 value: 55.007 - type: recall_at_5 value: 61.714999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.7 - type: map_at_10 value: 32.804 - type: map_at_100 value: 33.738 - type: map_at_1000 value: 33.825 - type: map_at_3 value: 30.639 - type: map_at_5 value: 31.781 - type: mrr_at_1 value: 26.328000000000003 - type: mrr_at_10 value: 34.679 - type: mrr_at_100 value: 35.510000000000005 - type: mrr_at_1000 value: 35.577999999999996 - type: mrr_at_3 value: 32.58 - type: mrr_at_5 value: 33.687 - type: ndcg_at_1 value: 26.328000000000003 - type: ndcg_at_10 value: 37.313 - type: ndcg_at_100 value: 42.004000000000005 - type: ndcg_at_1000 value: 44.232 - type: ndcg_at_3 value: 33.076 - type: ndcg_at_5 value: 34.966 - type: precision_at_1 value: 26.328000000000003 - type: precision_at_10 value: 5.627 - type: precision_at_100 value: 0.8410000000000001 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 14.011000000000001 - type: precision_at_5 value: 9.582 - type: recall_at_1 value: 24.7 - type: recall_at_10 value: 49.324 - type: recall_at_100 value: 71.018 - type: recall_at_1000 value: 87.905 - type: recall_at_3 value: 37.7 - type: recall_at_5 value: 42.281 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.350999999999999 - type: map_at_10 value: 21.745 - type: map_at_100 value: 22.731 - type: map_at_1000 value: 22.852 - type: map_at_3 value: 19.245 - type: map_at_5 value: 20.788 - type: mrr_at_1 value: 18.159 - type: mrr_at_10 value: 25.833000000000002 - type: mrr_at_100 value: 26.728 - type: mrr_at_1000 value: 26.802 - type: mrr_at_3 value: 23.383000000000003 - type: mrr_at_5 value: 24.887999999999998 - type: ndcg_at_1 value: 18.159 - type: ndcg_at_10 value: 26.518000000000004 - type: ndcg_at_100 value: 31.473000000000003 - type: ndcg_at_1000 value: 34.576 - type: ndcg_at_3 value: 21.907 - type: ndcg_at_5 value: 24.39 - type: precision_at_1 value: 18.159 - type: precision_at_10 value: 4.938 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.655000000000001 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 14.350999999999999 - type: recall_at_10 value: 37.284 - type: recall_at_100 value: 59.11300000000001 - type: recall_at_1000 value: 81.634 - type: recall_at_3 value: 24.753 - type: recall_at_5 value: 30.979 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.978 - type: map_at_10 value: 36.276 - type: map_at_100 value: 37.547000000000004 - type: map_at_1000 value: 37.678 - type: map_at_3 value: 33.674 - type: map_at_5 value: 35.119 - type: mrr_at_1 value: 32.916000000000004 - type: mrr_at_10 value: 41.798 - type: mrr_at_100 value: 42.72 - type: mrr_at_1000 value: 42.778 - type: mrr_at_3 value: 39.493 - type: mrr_at_5 value: 40.927 - type: ndcg_at_1 value: 32.916000000000004 - type: ndcg_at_10 value: 41.81 - type: ndcg_at_100 value: 47.284 - type: ndcg_at_1000 value: 49.702 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 39.597 - type: precision_at_1 value: 32.916000000000004 - type: precision_at_10 value: 7.411 - type: precision_at_100 value: 1.189 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.581 - type: precision_at_5 value: 12.397 - type: recall_at_1 value: 26.978 - type: recall_at_10 value: 52.869 - type: recall_at_100 value: 75.78399999999999 - type: recall_at_1000 value: 91.545 - type: recall_at_3 value: 40.717 - type: recall_at_5 value: 46.168 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.641 - type: map_at_10 value: 32.916000000000004 - type: map_at_100 value: 34.165 - type: map_at_1000 value: 34.286 - type: map_at_3 value: 30.335 - type: map_at_5 value: 31.569000000000003 - type: mrr_at_1 value: 30.593999999999998 - type: mrr_at_10 value: 38.448 - type: mrr_at_100 value: 39.299 - type: mrr_at_1000 value: 39.362 - type: mrr_at_3 value: 36.244 - type: mrr_at_5 value: 37.232 - type: ndcg_at_1 value: 30.593999999999998 - type: ndcg_at_10 value: 38.2 - type: ndcg_at_100 value: 43.742 - type: ndcg_at_1000 value: 46.217000000000006 - type: ndcg_at_3 value: 33.925 - type: ndcg_at_5 value: 35.394 - type: precision_at_1 value: 30.593999999999998 - type: precision_at_10 value: 6.895 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 16.096 - type: precision_at_5 value: 11.05 - type: recall_at_1 value: 24.641 - type: recall_at_10 value: 48.588 - type: recall_at_100 value: 72.841 - type: recall_at_1000 value: 89.535 - type: recall_at_3 value: 36.087 - type: recall_at_5 value: 40.346 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.79425 - type: map_at_10 value: 33.12033333333333 - type: map_at_100 value: 34.221333333333334 - type: map_at_1000 value: 34.3435 - type: map_at_3 value: 30.636583333333338 - type: map_at_5 value: 31.974083333333326 - type: mrr_at_1 value: 29.242416666666664 - type: mrr_at_10 value: 37.11675 - type: mrr_at_100 value: 37.93783333333334 - type: mrr_at_1000 value: 38.003083333333336 - type: mrr_at_3 value: 34.904666666666664 - type: mrr_at_5 value: 36.12916666666667 - type: ndcg_at_1 value: 29.242416666666664 - type: ndcg_at_10 value: 38.03416666666667 - type: ndcg_at_100 value: 42.86674999999999 - type: ndcg_at_1000 value: 45.34550000000001 - type: ndcg_at_3 value: 33.76466666666666 - type: ndcg_at_5 value: 35.668666666666674 - type: precision_at_1 value: 29.242416666666664 - type: precision_at_10 value: 6.589833333333334 - type: precision_at_100 value: 1.0693333333333332 - type: precision_at_1000 value: 0.14641666666666667 - type: precision_at_3 value: 15.430749999999998 - type: precision_at_5 value: 10.833833333333333 - type: recall_at_1 value: 24.79425 - type: recall_at_10 value: 48.582916666666655 - type: recall_at_100 value: 69.88499999999999 - type: recall_at_1000 value: 87.211 - type: recall_at_3 value: 36.625499999999995 - type: recall_at_5 value: 41.553999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.767 - type: map_at_10 value: 28.450999999999997 - type: map_at_100 value: 29.332 - type: map_at_1000 value: 29.426000000000002 - type: map_at_3 value: 26.379 - type: map_at_5 value: 27.584999999999997 - type: mrr_at_1 value: 25.46 - type: mrr_at_10 value: 30.974 - type: mrr_at_100 value: 31.784000000000002 - type: mrr_at_1000 value: 31.857999999999997 - type: mrr_at_3 value: 28.962 - type: mrr_at_5 value: 30.066 - type: ndcg_at_1 value: 25.46 - type: ndcg_at_10 value: 32.041 - type: ndcg_at_100 value: 36.522 - type: ndcg_at_1000 value: 39.101 - type: ndcg_at_3 value: 28.152 - type: ndcg_at_5 value: 30.03 - type: precision_at_1 value: 25.46 - type: precision_at_10 value: 4.893 - type: precision_at_100 value: 0.77 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 11.605 - type: precision_at_5 value: 8.19 - type: recall_at_1 value: 22.767 - type: recall_at_10 value: 40.71 - type: recall_at_100 value: 61.334999999999994 - type: recall_at_1000 value: 80.567 - type: recall_at_3 value: 30.198000000000004 - type: recall_at_5 value: 34.803 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.722 - type: map_at_10 value: 22.794 - type: map_at_100 value: 23.7 - type: map_at_1000 value: 23.822 - type: map_at_3 value: 20.781 - type: map_at_5 value: 22.024 - type: mrr_at_1 value: 20.061999999999998 - type: mrr_at_10 value: 26.346999999999998 - type: mrr_at_100 value: 27.153 - type: mrr_at_1000 value: 27.233 - type: mrr_at_3 value: 24.375 - type: mrr_at_5 value: 25.593 - type: ndcg_at_1 value: 20.061999999999998 - type: ndcg_at_10 value: 26.785999999999998 - type: ndcg_at_100 value: 31.319999999999997 - type: ndcg_at_1000 value: 34.346 - type: ndcg_at_3 value: 23.219 - type: ndcg_at_5 value: 25.107000000000003 - type: precision_at_1 value: 20.061999999999998 - type: precision_at_10 value: 4.78 - type: precision_at_100 value: 0.83 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.874 - type: precision_at_5 value: 7.956 - type: recall_at_1 value: 16.722 - type: recall_at_10 value: 35.204 - type: recall_at_100 value: 55.797 - type: recall_at_1000 value: 77.689 - type: recall_at_3 value: 25.245 - type: recall_at_5 value: 30.115 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.842 - type: map_at_10 value: 32.917 - type: map_at_100 value: 33.961000000000006 - type: map_at_1000 value: 34.069 - type: map_at_3 value: 30.595 - type: map_at_5 value: 31.837 - type: mrr_at_1 value: 29.011 - type: mrr_at_10 value: 36.977 - type: mrr_at_100 value: 37.814 - type: mrr_at_1000 value: 37.885999999999996 - type: mrr_at_3 value: 34.966 - type: mrr_at_5 value: 36.043 - type: ndcg_at_1 value: 29.011 - type: ndcg_at_10 value: 37.735 - type: ndcg_at_100 value: 42.683 - type: ndcg_at_1000 value: 45.198 - type: ndcg_at_3 value: 33.650000000000006 - type: ndcg_at_5 value: 35.386 - type: precision_at_1 value: 29.011 - type: precision_at_10 value: 6.259 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 15.329999999999998 - type: precision_at_5 value: 10.541 - type: recall_at_1 value: 24.842 - type: recall_at_10 value: 48.304 - type: recall_at_100 value: 70.04899999999999 - type: recall_at_1000 value: 87.82600000000001 - type: recall_at_3 value: 36.922 - type: recall_at_5 value: 41.449999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.252000000000002 - type: map_at_10 value: 32.293 - type: map_at_100 value: 33.816 - type: map_at_1000 value: 34.053 - type: map_at_3 value: 29.781999999999996 - type: map_at_5 value: 31.008000000000003 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 36.722 - type: mrr_at_100 value: 37.663000000000004 - type: mrr_at_1000 value: 37.734 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 35.609 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 37.775999999999996 - type: ndcg_at_100 value: 43.221 - type: ndcg_at_1000 value: 46.116 - type: ndcg_at_3 value: 33.403 - type: ndcg_at_5 value: 35.118 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.332 - type: precision_at_100 value: 1.49 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 15.415000000000001 - type: precision_at_5 value: 11.107 - type: recall_at_1 value: 24.252000000000002 - type: recall_at_10 value: 47.861 - type: recall_at_100 value: 72.21600000000001 - type: recall_at_1000 value: 90.886 - type: recall_at_3 value: 35.533 - type: recall_at_5 value: 39.959 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.025000000000002 - type: map_at_10 value: 27.154 - type: map_at_100 value: 28.118 - type: map_at_1000 value: 28.237000000000002 - type: map_at_3 value: 25.017 - type: map_at_5 value: 25.832 - type: mrr_at_1 value: 21.627 - type: mrr_at_10 value: 28.884999999999998 - type: mrr_at_100 value: 29.741 - type: mrr_at_1000 value: 29.831999999999997 - type: mrr_at_3 value: 26.741 - type: mrr_at_5 value: 27.628000000000004 - type: ndcg_at_1 value: 21.627 - type: ndcg_at_10 value: 31.436999999999998 - type: ndcg_at_100 value: 36.181000000000004 - type: ndcg_at_1000 value: 38.986 - type: ndcg_at_3 value: 27.025 - type: ndcg_at_5 value: 28.436 - type: precision_at_1 value: 21.627 - type: precision_at_10 value: 5.009 - type: precision_at_100 value: 0.7929999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 11.522 - type: precision_at_5 value: 7.763000000000001 - type: recall_at_1 value: 20.025000000000002 - type: recall_at_10 value: 42.954 - type: recall_at_100 value: 64.67500000000001 - type: recall_at_1000 value: 85.301 - type: recall_at_3 value: 30.892999999999997 - type: recall_at_5 value: 34.288000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.079 - type: map_at_10 value: 16.930999999999997 - type: map_at_100 value: 18.398999999999997 - type: map_at_1000 value: 18.561 - type: map_at_3 value: 14.294 - type: map_at_5 value: 15.579 - type: mrr_at_1 value: 22.606 - type: mrr_at_10 value: 32.513 - type: mrr_at_100 value: 33.463 - type: mrr_at_1000 value: 33.513999999999996 - type: mrr_at_3 value: 29.479 - type: mrr_at_5 value: 31.3 - type: ndcg_at_1 value: 22.606 - type: ndcg_at_10 value: 24.053 - type: ndcg_at_100 value: 30.258000000000003 - type: ndcg_at_1000 value: 33.516 - type: ndcg_at_3 value: 19.721 - type: ndcg_at_5 value: 21.144 - type: precision_at_1 value: 22.606 - type: precision_at_10 value: 7.55 - type: precision_at_100 value: 1.399 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 14.701 - type: precision_at_5 value: 11.192 - type: recall_at_1 value: 10.079 - type: recall_at_10 value: 28.970000000000002 - type: recall_at_100 value: 50.805 - type: recall_at_1000 value: 69.378 - type: recall_at_3 value: 18.199 - type: recall_at_5 value: 22.442 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 7.794 - type: map_at_10 value: 15.165999999999999 - type: map_at_100 value: 20.508000000000003 - type: map_at_1000 value: 21.809 - type: map_at_3 value: 11.568000000000001 - type: map_at_5 value: 13.059000000000001 - type: mrr_at_1 value: 56.49999999999999 - type: mrr_at_10 value: 65.90899999999999 - type: mrr_at_100 value: 66.352 - type: mrr_at_1000 value: 66.369 - type: mrr_at_3 value: 64.0 - type: mrr_at_5 value: 65.10000000000001 - type: ndcg_at_1 value: 44.25 - type: ndcg_at_10 value: 32.649 - type: ndcg_at_100 value: 36.668 - type: ndcg_at_1000 value: 43.918 - type: ndcg_at_3 value: 37.096000000000004 - type: ndcg_at_5 value: 34.048 - type: precision_at_1 value: 56.49999999999999 - type: precision_at_10 value: 25.45 - type: precision_at_100 value: 8.055 - type: precision_at_1000 value: 1.7489999999999999 - type: precision_at_3 value: 41.0 - type: precision_at_5 value: 32.85 - type: recall_at_1 value: 7.794 - type: recall_at_10 value: 20.101 - type: recall_at_100 value: 42.448 - type: recall_at_1000 value: 65.88000000000001 - type: recall_at_3 value: 12.753 - type: recall_at_5 value: 15.307 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.01 - type: f1 value: 38.659680951114964 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 49.713 - type: map_at_10 value: 61.79 - type: map_at_100 value: 62.28 - type: map_at_1000 value: 62.297000000000004 - type: map_at_3 value: 59.361 - type: map_at_5 value: 60.92100000000001 - type: mrr_at_1 value: 53.405 - type: mrr_at_10 value: 65.79899999999999 - type: mrr_at_100 value: 66.219 - type: mrr_at_1000 value: 66.227 - type: mrr_at_3 value: 63.431000000000004 - type: mrr_at_5 value: 64.98 - type: ndcg_at_1 value: 53.405 - type: ndcg_at_10 value: 68.01899999999999 - type: ndcg_at_100 value: 70.197 - type: ndcg_at_1000 value: 70.571 - type: ndcg_at_3 value: 63.352 - type: ndcg_at_5 value: 66.018 - type: precision_at_1 value: 53.405 - type: precision_at_10 value: 9.119 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 25.602999999999998 - type: precision_at_5 value: 16.835 - type: recall_at_1 value: 49.713 - type: recall_at_10 value: 83.306 - type: recall_at_100 value: 92.92 - type: recall_at_1000 value: 95.577 - type: recall_at_3 value: 70.798 - type: recall_at_5 value: 77.254 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 15.310000000000002 - type: map_at_10 value: 26.204 - type: map_at_100 value: 27.932000000000002 - type: map_at_1000 value: 28.121000000000002 - type: map_at_3 value: 22.481 - type: map_at_5 value: 24.678 - type: mrr_at_1 value: 29.784 - type: mrr_at_10 value: 39.582 - type: mrr_at_100 value: 40.52 - type: mrr_at_1000 value: 40.568 - type: mrr_at_3 value: 37.114000000000004 - type: mrr_at_5 value: 38.596000000000004 - type: ndcg_at_1 value: 29.784 - type: ndcg_at_10 value: 33.432 - type: ndcg_at_100 value: 40.281 - type: ndcg_at_1000 value: 43.653999999999996 - type: ndcg_at_3 value: 29.612 - type: ndcg_at_5 value: 31.223 - type: precision_at_1 value: 29.784 - type: precision_at_10 value: 9.645 - type: precision_at_100 value: 1.645 - type: precision_at_1000 value: 0.22499999999999998 - type: precision_at_3 value: 20.165 - type: precision_at_5 value: 15.401000000000002 - type: recall_at_1 value: 15.310000000000002 - type: recall_at_10 value: 40.499 - type: recall_at_100 value: 66.643 - type: recall_at_1000 value: 87.059 - type: recall_at_3 value: 27.492 - type: recall_at_5 value: 33.748 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 33.599000000000004 - type: map_at_10 value: 47.347 - type: map_at_100 value: 48.191 - type: map_at_1000 value: 48.263 - type: map_at_3 value: 44.698 - type: map_at_5 value: 46.278999999999996 - type: mrr_at_1 value: 67.19800000000001 - type: mrr_at_10 value: 74.054 - type: mrr_at_100 value: 74.376 - type: mrr_at_1000 value: 74.392 - type: mrr_at_3 value: 72.849 - type: mrr_at_5 value: 73.643 - type: ndcg_at_1 value: 67.19800000000001 - type: ndcg_at_10 value: 56.482 - type: ndcg_at_100 value: 59.694 - type: ndcg_at_1000 value: 61.204 - type: ndcg_at_3 value: 52.43299999999999 - type: ndcg_at_5 value: 54.608000000000004 - type: precision_at_1 value: 67.19800000000001 - type: precision_at_10 value: 11.613999999999999 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 32.726 - type: precision_at_5 value: 21.349999999999998 - type: recall_at_1 value: 33.599000000000004 - type: recall_at_10 value: 58.069 - type: recall_at_100 value: 70.736 - type: recall_at_1000 value: 80.804 - type: recall_at_3 value: 49.088 - type: recall_at_5 value: 53.376000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 73.64359999999999 - type: ap value: 67.54685976014599 - type: f1 value: 73.55148707559482 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 19.502 - type: map_at_10 value: 30.816 - type: map_at_100 value: 32.007999999999996 - type: map_at_1000 value: 32.067 - type: map_at_3 value: 27.215 - type: map_at_5 value: 29.304000000000002 - type: mrr_at_1 value: 20.072000000000003 - type: mrr_at_10 value: 31.406 - type: mrr_at_100 value: 32.549 - type: mrr_at_1000 value: 32.602 - type: mrr_at_3 value: 27.839000000000002 - type: mrr_at_5 value: 29.926000000000002 - type: ndcg_at_1 value: 20.086000000000002 - type: ndcg_at_10 value: 37.282 - type: ndcg_at_100 value: 43.206 - type: ndcg_at_1000 value: 44.690000000000005 - type: ndcg_at_3 value: 29.932 - type: ndcg_at_5 value: 33.668 - type: precision_at_1 value: 20.086000000000002 - type: precision_at_10 value: 5.961 - type: precision_at_100 value: 0.898 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.856000000000002 - type: precision_at_5 value: 9.596 - type: recall_at_1 value: 19.502 - type: recall_at_10 value: 57.182 - type: recall_at_100 value: 84.952 - type: recall_at_1000 value: 96.34700000000001 - type: recall_at_3 value: 37.193 - type: recall_at_5 value: 46.157 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.96488828089375 - type: f1 value: 93.32119260543482 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.4965800273598 - type: f1 value: 49.34896217536082 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.60928043039678 - type: f1 value: 64.34244712074538 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.75453934095493 - type: f1 value: 68.39224867489249 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.862573504920082 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.511123551196803 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.99145104942086 - type: mrr value: 32.03606480418627 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.015 - type: map_at_10 value: 11.054 - type: map_at_100 value: 13.773 - type: map_at_1000 value: 15.082999999999998 - type: map_at_3 value: 8.253 - type: map_at_5 value: 9.508999999999999 - type: mrr_at_1 value: 42.105 - type: mrr_at_10 value: 50.44499999999999 - type: mrr_at_100 value: 51.080000000000005 - type: mrr_at_1000 value: 51.129999999999995 - type: mrr_at_3 value: 48.555 - type: mrr_at_5 value: 49.84 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 30.403000000000002 - type: ndcg_at_100 value: 28.216 - type: ndcg_at_1000 value: 37.021 - type: ndcg_at_3 value: 35.53 - type: ndcg_at_5 value: 33.202999999999996 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 22.353 - type: precision_at_100 value: 7.266 - type: precision_at_1000 value: 2.011 - type: precision_at_3 value: 32.921 - type: precision_at_5 value: 28.297 - type: recall_at_1 value: 5.015 - type: recall_at_10 value: 14.393 - type: recall_at_100 value: 28.893 - type: recall_at_1000 value: 60.18 - type: recall_at_3 value: 9.184000000000001 - type: recall_at_5 value: 11.39 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 29.524 - type: map_at_10 value: 44.182 - type: map_at_100 value: 45.228 - type: map_at_1000 value: 45.265 - type: map_at_3 value: 39.978 - type: map_at_5 value: 42.482 - type: mrr_at_1 value: 33.256 - type: mrr_at_10 value: 46.661 - type: mrr_at_100 value: 47.47 - type: mrr_at_1000 value: 47.496 - type: mrr_at_3 value: 43.187999999999995 - type: mrr_at_5 value: 45.330999999999996 - type: ndcg_at_1 value: 33.227000000000004 - type: ndcg_at_10 value: 51.589 - type: ndcg_at_100 value: 56.043 - type: ndcg_at_1000 value: 56.937000000000005 - type: ndcg_at_3 value: 43.751 - type: ndcg_at_5 value: 47.937000000000005 - type: precision_at_1 value: 33.227000000000004 - type: precision_at_10 value: 8.556999999999999 - type: precision_at_100 value: 1.103 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.921 - type: precision_at_5 value: 14.396999999999998 - type: recall_at_1 value: 29.524 - type: recall_at_10 value: 71.615 - type: recall_at_100 value: 91.056 - type: recall_at_1000 value: 97.72800000000001 - type: recall_at_3 value: 51.451 - type: recall_at_5 value: 61.119 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.596 - type: map_at_10 value: 83.281 - type: map_at_100 value: 83.952 - type: map_at_1000 value: 83.97200000000001 - type: map_at_3 value: 80.315 - type: map_at_5 value: 82.223 - type: mrr_at_1 value: 80.17 - type: mrr_at_10 value: 86.522 - type: mrr_at_100 value: 86.644 - type: mrr_at_1000 value: 86.64500000000001 - type: mrr_at_3 value: 85.438 - type: mrr_at_5 value: 86.21799999999999 - type: ndcg_at_1 value: 80.19 - type: ndcg_at_10 value: 87.19 - type: ndcg_at_100 value: 88.567 - type: ndcg_at_1000 value: 88.70400000000001 - type: ndcg_at_3 value: 84.17999999999999 - type: ndcg_at_5 value: 85.931 - type: precision_at_1 value: 80.19 - type: precision_at_10 value: 13.209000000000001 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.717 - type: precision_at_5 value: 24.248 - type: recall_at_1 value: 69.596 - type: recall_at_10 value: 94.533 - type: recall_at_100 value: 99.322 - type: recall_at_1000 value: 99.965 - type: recall_at_3 value: 85.911 - type: recall_at_5 value: 90.809 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 49.27650627571912 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 57.08550946534183 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.568 - type: map_at_10 value: 10.862 - type: map_at_100 value: 12.757 - type: map_at_1000 value: 13.031 - type: map_at_3 value: 7.960000000000001 - type: map_at_5 value: 9.337 - type: mrr_at_1 value: 22.5 - type: mrr_at_10 value: 32.6 - type: mrr_at_100 value: 33.603 - type: mrr_at_1000 value: 33.672000000000004 - type: mrr_at_3 value: 29.299999999999997 - type: mrr_at_5 value: 31.25 - type: ndcg_at_1 value: 22.5 - type: ndcg_at_10 value: 18.605 - type: ndcg_at_100 value: 26.029999999999998 - type: ndcg_at_1000 value: 31.256 - type: ndcg_at_3 value: 17.873 - type: ndcg_at_5 value: 15.511 - type: precision_at_1 value: 22.5 - type: precision_at_10 value: 9.58 - type: precision_at_100 value: 2.033 - type: precision_at_1000 value: 0.33 - type: precision_at_3 value: 16.633 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.568 - type: recall_at_10 value: 19.402 - type: recall_at_100 value: 41.277 - type: recall_at_1000 value: 66.963 - type: recall_at_3 value: 10.112 - type: recall_at_5 value: 13.712 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.31992291680787 - type: cos_sim_spearman value: 76.7212346922664 - type: euclidean_pearson value: 80.42189271706478 - type: euclidean_spearman value: 76.7212342532493 - type: manhattan_pearson value: 80.33171093031578 - type: manhattan_spearman value: 76.63192883074694 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.16654278886763 - type: cos_sim_spearman value: 73.66390263429565 - type: euclidean_pearson value: 79.7485360086639 - type: euclidean_spearman value: 73.66389870373436 - type: manhattan_pearson value: 79.73652237443706 - type: manhattan_spearman value: 73.65296117151647 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.40389689929246 - type: cos_sim_spearman value: 83.29727595993955 - type: euclidean_pearson value: 82.23970587854079 - type: euclidean_spearman value: 83.29727595993955 - type: manhattan_pearson value: 82.18823600831897 - type: manhattan_spearman value: 83.20746192209594 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.73505246913413 - type: cos_sim_spearman value: 79.1686548248754 - type: euclidean_pearson value: 80.48889135993412 - type: euclidean_spearman value: 79.16864112930354 - type: manhattan_pearson value: 80.40720651057302 - type: manhattan_spearman value: 79.0640155089286 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.3953512879065 - type: cos_sim_spearman value: 87.29947322714338 - type: euclidean_pearson value: 86.59759438529645 - type: euclidean_spearman value: 87.29947511092824 - type: manhattan_pearson value: 86.52097806169155 - type: manhattan_spearman value: 87.22987242146534 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.48565753792056 - type: cos_sim_spearman value: 83.6049720319893 - type: euclidean_pearson value: 82.56452023172913 - type: euclidean_spearman value: 83.60490168191697 - type: manhattan_pearson value: 82.58079941137872 - type: manhattan_spearman value: 83.60975807374051 - 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: 88.18239976618212 - type: cos_sim_spearman value: 88.23061724730616 - type: euclidean_pearson value: 87.78482472776658 - type: euclidean_spearman value: 88.23061724730616 - type: manhattan_pearson value: 87.75059641730239 - type: manhattan_spearman value: 88.22527413524622 - 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: 63.42816418706765 - type: cos_sim_spearman value: 63.4569864520124 - type: euclidean_pearson value: 64.35405409953853 - type: euclidean_spearman value: 63.4569864520124 - type: manhattan_pearson value: 63.96649236073056 - type: manhattan_spearman value: 63.01448583722708 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.41659638047614 - type: cos_sim_spearman value: 84.03893866106175 - type: euclidean_pearson value: 84.2251203953798 - type: euclidean_spearman value: 84.03893866106175 - type: manhattan_pearson value: 84.22733643205514 - type: manhattan_spearman value: 84.06504411263612 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.75608022582414 - type: mrr value: 94.0947732369301 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 50.161 - type: map_at_10 value: 59.458999999999996 - type: map_at_100 value: 60.156 - type: map_at_1000 value: 60.194 - type: map_at_3 value: 56.45400000000001 - type: map_at_5 value: 58.165 - type: mrr_at_1 value: 53.333 - type: mrr_at_10 value: 61.050000000000004 - type: mrr_at_100 value: 61.586 - type: mrr_at_1000 value: 61.624 - type: mrr_at_3 value: 58.889 - type: mrr_at_5 value: 60.122 - type: ndcg_at_1 value: 53.333 - type: ndcg_at_10 value: 63.888999999999996 - type: ndcg_at_100 value: 66.963 - type: ndcg_at_1000 value: 68.062 - type: ndcg_at_3 value: 59.01 - type: ndcg_at_5 value: 61.373999999999995 - type: precision_at_1 value: 53.333 - type: precision_at_10 value: 8.633000000000001 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 23.111 - type: precision_at_5 value: 15.467 - type: recall_at_1 value: 50.161 - type: recall_at_10 value: 75.922 - type: recall_at_100 value: 90.0 - type: recall_at_1000 value: 98.667 - type: recall_at_3 value: 62.90599999999999 - type: recall_at_5 value: 68.828 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81188118811882 - type: cos_sim_ap value: 95.11619225962413 - type: cos_sim_f1 value: 90.35840484603736 - type: cos_sim_precision value: 91.23343527013252 - type: cos_sim_recall value: 89.5 - type: dot_accuracy value: 99.81188118811882 - type: dot_ap value: 95.11619225962413 - type: dot_f1 value: 90.35840484603736 - type: dot_precision value: 91.23343527013252 - type: dot_recall value: 89.5 - type: euclidean_accuracy value: 99.81188118811882 - type: euclidean_ap value: 95.11619225962413 - type: euclidean_f1 value: 90.35840484603736 - type: euclidean_precision value: 91.23343527013252 - type: euclidean_recall value: 89.5 - type: manhattan_accuracy value: 99.80891089108911 - type: manhattan_ap value: 95.07294266220966 - type: manhattan_f1 value: 90.21794221996959 - type: manhattan_precision value: 91.46968139773895 - type: manhattan_recall value: 89.0 - type: max_accuracy value: 99.81188118811882 - type: max_ap value: 95.11619225962413 - type: max_f1 value: 90.35840484603736 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 55.3481874105239 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.421291695525 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.98746633276634 - type: mrr value: 50.63143249724133 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.009961979844036 - type: cos_sim_spearman value: 30.558416108881044 - type: dot_pearson value: 31.009964941134253 - type: dot_spearman value: 30.545760761761393 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.207 - type: map_at_10 value: 1.6 - type: map_at_100 value: 8.594 - type: map_at_1000 value: 20.213 - type: map_at_3 value: 0.585 - type: map_at_5 value: 0.9039999999999999 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 87.4 - type: mrr_at_100 value: 87.4 - type: mrr_at_1000 value: 87.4 - type: mrr_at_3 value: 86.667 - type: mrr_at_5 value: 87.06700000000001 - type: ndcg_at_1 value: 73.0 - type: ndcg_at_10 value: 65.18 - type: ndcg_at_100 value: 49.631 - type: ndcg_at_1000 value: 43.498999999999995 - type: ndcg_at_3 value: 71.83800000000001 - type: ndcg_at_5 value: 69.271 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 69.19999999999999 - type: precision_at_100 value: 50.980000000000004 - type: precision_at_1000 value: 19.426 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.207 - type: recall_at_10 value: 1.822 - type: recall_at_100 value: 11.849 - type: recall_at_1000 value: 40.492 - type: recall_at_3 value: 0.622 - type: recall_at_5 value: 0.9809999999999999 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.001 - type: map_at_10 value: 10.376000000000001 - type: map_at_100 value: 16.936999999999998 - type: map_at_1000 value: 18.615000000000002 - type: map_at_3 value: 5.335999999999999 - type: map_at_5 value: 7.374 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 38.29 - type: mrr_at_100 value: 39.33 - type: mrr_at_1000 value: 39.347 - type: mrr_at_3 value: 32.993 - type: mrr_at_5 value: 36.973 - type: ndcg_at_1 value: 17.347 - type: ndcg_at_10 value: 23.515 - type: ndcg_at_100 value: 37.457 - type: ndcg_at_1000 value: 49.439 - type: ndcg_at_3 value: 22.762999999999998 - type: ndcg_at_5 value: 22.622 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 22.448999999999998 - type: precision_at_100 value: 8.184 - type: precision_at_1000 value: 1.608 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 25.306 - type: recall_at_1 value: 2.001 - type: recall_at_10 value: 17.422 - type: recall_at_100 value: 51.532999999999994 - type: recall_at_1000 value: 87.466 - type: recall_at_3 value: 6.861000000000001 - type: recall_at_5 value: 10.502 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.54419999999999 - type: ap value: 14.372170450843907 - type: f1 value: 54.94420257390529 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.402942840973395 - type: f1 value: 59.4166538875571 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.569064336457906 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.31322644096085 - type: cos_sim_ap value: 72.14518894837381 - type: cos_sim_f1 value: 66.67489813557229 - type: cos_sim_precision value: 62.65954977953121 - type: cos_sim_recall value: 71.2401055408971 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.14521480685293 - type: dot_f1 value: 66.67489813557229 - type: dot_precision value: 62.65954977953121 - type: dot_recall value: 71.2401055408971 - type: euclidean_accuracy value: 85.31322644096085 - type: euclidean_ap value: 72.14520820485349 - type: euclidean_f1 value: 66.67489813557229 - type: euclidean_precision value: 62.65954977953121 - type: euclidean_recall value: 71.2401055408971 - type: manhattan_accuracy value: 85.21785778148656 - type: manhattan_ap value: 72.01177147657364 - type: manhattan_f1 value: 66.62594673833374 - type: manhattan_precision value: 62.0336669699727 - type: manhattan_recall value: 71.95250659630607 - type: max_accuracy value: 85.31322644096085 - type: max_ap value: 72.14521480685293 - type: max_f1 value: 66.67489813557229 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.12756626693057 - type: cos_sim_ap value: 86.05430786440826 - type: cos_sim_f1 value: 78.27759692216631 - type: cos_sim_precision value: 75.33466248931929 - type: cos_sim_recall value: 81.45980905451185 - type: dot_accuracy value: 89.12950673341872 - type: dot_ap value: 86.05431161145492 - type: dot_f1 value: 78.27759692216631 - type: dot_precision value: 75.33466248931929 - type: dot_recall value: 81.45980905451185 - type: euclidean_accuracy value: 89.12756626693057 - type: euclidean_ap value: 86.05431303247397 - type: euclidean_f1 value: 78.27759692216631 - type: euclidean_precision value: 75.33466248931929 - type: euclidean_recall value: 81.45980905451185 - type: manhattan_accuracy value: 89.04994760740482 - type: manhattan_ap value: 86.00860610892074 - type: manhattan_f1 value: 78.1846776005392 - type: manhattan_precision value: 76.10438839480975 - type: manhattan_recall value: 80.3818909762858 - type: max_accuracy value: 89.12950673341872 - type: max_ap value: 86.05431303247397 - type: max_f1 value: 78.27759692216631 --- <!-- TODO: add evaluation results here --> <br><br> <p align="center"> <img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b> </p> ## Intended Usage & Model Info `jina-embeddings-v2-small-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length. The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc. This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance. Additionally, we provide the following embedding models: **V1 (Based on T5, 512 Seq)** - [`jina-embeddings-v1-small-en`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters. - [`jina-embeddings-v1-base-en`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters. - [`jina-embeddings-v1-large-en`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters. **V2 (Based on JinaBert, 8k Seq)** - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**. - [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters. - [`jina-embeddings-v2-large-en`](): 435 million parameters (releasing soon). ## Data & Parameters Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923) ## Usage **<details><summary>Please apply mean pooling when integrating the model.</summary>** <p> ### Why mean pooling? `mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this. However, if you would like to do it without using the default `encode` function: ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] 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) sentences = ['How is the weather today?', 'What is the current weather like today?'] tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en') model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): model_output = model(**encoded_input) embeddings = mean_pooling(model_output, encoded_input['attention_mask']) embeddings = F.normalize(embeddings, p=2, dim=1) ``` </p> </details> You can use Jina Embedding models directly from transformers package: ```python !pip install transformers from transformers import AutoModel from numpy.linalg import norm cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?']) print(cos_sim(embeddings[0], embeddings[1])) ``` If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode( ['Very long ... document'], max_length=2048 ) ``` ## Fully-managed Embeddings Service Alternatively, you can use Jina AI's [Embeddings platform](https://jina.ai/embeddings/) for fully-managed access to Jina Embeddings models. ## RAG Performance According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83), > In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out. <img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px"> ## Plans The development of new bilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called `jina-embeddings-v2-small-de/es`. ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find Jina Embeddings useful in your research, please cite the following paper: ``` @misc{günther2023jina, title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao}, year={2023}, eprint={2310.19923}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

nvidia/mit-b0

nvidia

transformers

image-classification

--- license: other tags: - vision datasets: - imagenet_1k widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # SegFormer (b0-sized) encoder pre-trained-only SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes. ## Intended uses & limitations You can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) 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 SegformerImageProcessor, SegformerForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0") model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0") inputs = image_processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

sentence-transformers/paraphrase-mpnet-base-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # sentence-transformers/paraphrase-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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/paraphrase-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```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) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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/paraphrase-mpnet-base-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) ) ``` ## 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", } ```

fxmarty/really-tiny-falcon-testing

fxmarty

transformers

text-generation

--- license: mit --- tiny = <10 MB

liuhaotian/llava-v1.5-13b

liuhaotian

transformers

text-generation

--- inference: false --- <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. **Model date:** LLaVA-v1.5-13B was trained in September 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 450K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

eugenesiow/edsr-base

eugenesiow

transformers

--- license: apache-2.0 tags: - super-image - image-super-resolution datasets: - eugenesiow/Div2k - eugenesiow/Set5 - eugenesiow/Set14 - eugenesiow/BSD100 - eugenesiow/Urban100 metrics: - pnsr - ssim --- # Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch). The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.  ## Model description EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation. This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels. ## Intended uses & limitations You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset. ### How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import EdsrModel, ImageLoader from PIL import Image import requests url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg' image = Image.open(requests.get(url, stream=True).raw) model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) # scale 2, 3 and 4 models available inputs = ImageLoader.load_image(image) preds = model(inputs) ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png` ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab") ## Training data The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900). ## Training procedure ### Preprocessing We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566). Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times. During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image. We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data: ```bash pip install datasets ``` The following code gets the data and preprocesses/augments the data. ```python from datasets import load_dataset from super_image.data import EvalDataset, TrainDataset, augment_five_crop augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader ``` ### Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig training_args = TrainingArguments( output_dir='./results', # output directory num_train_epochs=1000, # total number of training epochs ) config = EdsrConfig( scale=4, # train a model to upscale 4x ) model = EdsrModel(config) trainer = Trainer( model=model, # the instantiated model to be trained args=training_args, # training arguments, defined above train_dataset=train_dataset, # training dataset eval_dataset=eval_dataset # evaluation dataset ) trainer.train() ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab") ## Evaluation results The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100) The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline. |Dataset |Scale |Bicubic |edsr-base | |--- |--- |--- |--- | |Set5 |2x |33.64/0.9292 |**38.02/0.9607** | |Set5 |3x |30.39/0.8678 |**35.04/0.9403** | |Set5 |4x |28.42/0.8101 |**32.12/0.8947** | |Set14 |2x |30.22/0.8683 |**33.57/0.9172** | |Set14 |3x |27.53/0.7737 |**30.93/0.8567** | |Set14 |4x |25.99/0.7023 |**28.60/0.7815** | |BSD100 |2x |29.55/0.8425 |**32.21/0.8999** | |BSD100 |3x |27.20/0.7382 |**29.65/0.8204** | |BSD100 |4x |25.96/0.6672 |**27.61/0.7363** | |Urban100 |2x |26.66/0.8408 |**32.04/0.9276** | |Urban100 |3x | |**29.23/0.8723** | |Urban100 |4x |23.14/0.6573 |**26.02/0.7832** |  You can find a notebook to easily run evaluation on pretrained models below: [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab") ## BibTeX entry and citation info ```bibtex @InProceedings{Lim_2017_CVPR_Workshops, author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}, title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {July}, year = {2017} } ```

monologg/bert-base-cased-goemotions-original

monologg

transformers

Entry not found

Helsinki-NLP/opus-mt-ROMANCE-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-ROMANCE-en * source languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co,nap,scn,vec,sc,ro,la * target languages: en * OPUS readme: [fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/README.md) * dataset: opus * model: transformer * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-04-01.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.zip) * test set translations: [opus-2020-04-01.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.test.txt) * test set scores: [opus-2020-04-01.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.fr.en | 62.2 | 0.750 |

echarlaix/tiny-random-mistral

echarlaix

transformers

text-generation

--- license: apache-2.0 ---

Helsinki-NLP/opus-mt-sv-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-en * source languages: sv * target languages: en * OPUS readme: [sv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-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/sv-en/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.sv.en | 64.5 | 0.763 |

TheBloke/Mistral-7B-OpenOrca-GPTQ

TheBloke

transformers

text-generation

--- base_model: Open-Orca/Mistral-7B-OpenOrca datasets: - Open-Orca/OpenOrca inference: false language: - en library_name: transformers license: apache-2.0 model_creator: OpenOrca model_name: Mistral 7B OpenOrca model_type: mistral pipeline_tag: text-generation prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B OpenOrca - GPTQ - Model creator: [OpenOrca](https://huggingface.co/Open-Orca) - Original model: [Mistral 7B OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) <!-- description start --> ## Description This repo contains GPTQ model files for [OpenOrca's Mistral 7B OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF) * [OpenOrca's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Mistral-7B-OpenOrca-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-OpenOrca-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Mistral-7B-OpenOrca-GPTQ`: ```shell mkdir Mistral-7B-OpenOrca-GPTQ huggingface-cli download TheBloke/Mistral-7B-OpenOrca-GPTQ --local-dir Mistral-7B-OpenOrca-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Mistral-7B-OpenOrca-GPTQ huggingface-cli download TheBloke/Mistral-7B-OpenOrca-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-OpenOrca-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Mistral-7B-OpenOrca-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-OpenOrca-GPTQ --local-dir Mistral-7B-OpenOrca-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-OpenOrca-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Mistral-7B-OpenOrca-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-OpenOrca-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Mistral-7B-OpenOrca-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Mistral-7B-OpenOrca-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: OpenOrca's Mistral 7B OpenOrca <p><h1>🐋 TBD 🐋</h1></p>  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # OpenOrca - Mistral - 7B - 8k We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune on top of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707). We use [OpenChat](https://huggingface.co/openchat) packing, trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). This release is trained on a curated filtered subset of most of our GPT-4 augmented data. It is the same subset of our data as was used in our [OpenOrcaxOpenChat-Preview2-13B model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B). HF Leaderboard evals place this model as #2 for all models smaller than 30B at release time, outperforming all but one 13B model. TBD Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners. We will also give sneak-peak announcements on our Discord, which you can find here: https://AlignmentLab.ai or on the OpenAccess AI Collective Discord for more information about Axolotl trainer here: https://discord.gg/5y8STgB3P3 # Prompt Template We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this. ## Example Prompt Exchange TBD # Evaluation We have evaluated using the methodology and tools for the HuggingFace Leaderboard, and find that we have significantly improved upon the base model. TBD ## HuggingFaceH4 Open LLM Leaderboard Performance TBD ## GPT4ALL Leaderboard Performance TBD # Dataset We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset. # Training We trained with 8x A6000 GPUs for 62 hours, completing 4 epochs of full fine tuning on our dataset in one training run. Commodity cost was ~$400. # Citation ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

baichuan-inc/Baichuan-7B

baichuan-inc

transformers

text-generation

--- language: - zh - en pipeline_tag: text-generation inference: false --- # Baichuan-7B <!-- Provide a quick summary of what the model is/does. --> Baichuan-7B是由百川智能开发的一个开源的大规模预训练模型。基于Transformer结构，在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。在标准的中文和英文权威benchmark（C-EVAL/MMLU）上均取得同尺寸最好的效果。 如果希望使用Baichuan-7B（如进行推理、Finetune等），我们推荐使用配套代码库[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 Baichuan-7B is an open-source large-scale pre-trained model developed by Baichuan Intelligent Technology. Based on the Transformer architecture, it is a model with 7 billion parameters trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. It achieves the best performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU). If you wish to use Baichuan-7B (for inference, finetuning, etc.), we recommend using the accompanying code library [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ## Why use Baichuan-7B - 在同尺寸模型中Baichuan-7B达到了目前SOTA的水平，参考下面MMLU指标 - Baichuan-7B使用自有的中英文双语语料进行训练，在中文上进行优化，在C-Eval达到SOTA水平 - 不同于LLaMA完全禁止商业使用，Baichuan-7B使用更宽松的开源协议，允许用于商业目的 - Among models of the same size, Baichuan-7B has achieved the current state-of-the-art (SOTA) level, as evidenced by the following MMLU metrics. - Baichuan-7B is trained on proprietary bilingual Chinese-English corpora, optimized for Chinese, and achieves SOTA performance on C-Eval. - Unlike LLaMA, which completely prohibits commercial use, Baichuan-7B employs a more lenient open-source license, allowing for commercial purposes. ## How to Get Started with the Model 如下是一个使用Baichuan-7B进行1-shot推理的任务，根据作品给出作者名，正确输出为"夜雨寄北->李商隐" ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-7B", device_map="auto", trust_remote_code=True) inputs = tokenizer('登鹳雀楼->王之涣\n夜雨寄北->', return_tensors='pt') inputs = inputs.to('cuda:0') pred = model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` The following is a task of performing 1-shot inference using Baichuan-7B, where the author's name is given based on the work, with the correct output being "One Hundred Years of Solitude->Gabriel Garcia Marquez" ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-7B", device_map="auto", trust_remote_code=True) inputs = tokenizer('Hamlet->Shakespeare\nOne Hundred Years of Solitude->', return_tensors='pt') inputs = inputs.to('cuda:0') pred = model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** 百川智能(Baichuan Intelligent Technology) - **Email**: [email protected] - **Language(s) (NLP):** Chinese/English - **License:** [Baichuan-7B License](https://huggingface.co/baichuan-inc/Baichuan-7B/blob/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) ### Model Sources <!-- Provide the basic links for the model. --> 整体模型基于标准的Transformer结构，我们采用了和LLaMA一样的模型设计 - **Position Embedding**：采用rotary-embedding，是现阶段被大多数模型采用的位置编码方案，具有很好的外推性。 - **Feedforward Layer**：采用SwiGLU，Feedforward变化为(8/3)倍的隐含层大小，即11008。 - **Layer Normalization**: 基于[RMSNorm](https://arxiv.org/abs/1910.07467)的Pre-Normalization。 具体参数和见下表 | Hyperparameter | Value | |----------------|-------| |n_parameters | 7000559616 | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 64000 | | sequence length | 4096 | The overall model is based on the standard Transformer structure, and we have adopted the same model design as LLaMA: - Position Embedding: We use rotary-embedding, which is the position encoding scheme adopted by most models at this stage, and it has excellent extrapolation capabilities. - Feedforward Layer: We use SwiGLU. The feedforward changes to (8/3) times the size of the hidden layer, that is, 11008. - Layer Normalization: Pre-Normalization based on [RMSNorm](https://arxiv.org/abs/1910.07467). The specific parameters are as follows: | Hyperparameter | Value | |----------------|-------| |n_parameters | 7000559616 | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 64000 | | sequence length | 4096 | ## 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. --> ### Downstream Use <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> 我们同时开源出了和本模型配套的训练代码，允许进行高效的Finetune用于下游任务，具体参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 We have also open-sourced the training code that accompanies this model, allowing for efficient finetuning for downstream tasks. For more details, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> 在没有充分评估风险和采取缓解措施的情况下投入生产使用；任何可能被视为不负责任或有害的使用案例。 Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Baichuan-7B可能会产生事实上不正确的输出，不应依赖它产生事实上准确的信息。Baichuan-7B是在各种公共数据集上进行训练的。尽管我们已经做出了巨大的努力来清洗预训练数据，但这个模型可能会生成淫秽、偏见或其他冒犯性的输出。 Baichuan-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. Baichuan-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Training Details 训练具体设置参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 For specific training settings, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ## Evaluation ### 中文评测 #### C-Eval [CEval数据集](https://cevalbenchmark.com/index.html)是一个全面的中文基础模型评测数据集，涵盖了52个学科和四个难度的级别。我们使用该数据集的dev集作为few-shot的来源，在test集上进行了5-shot测试。 | Model 5-shot | Average | Avg(Hard) | STEM | Social Sciences | Humanities | Others | |-----------------------------|---------|-----------|------|-----------------|------------|--------| | GPT-4 | 68.7 | 54.9 | 67.1 | 77.6 | 64.5 | 67.8 | | ChatGPT | 54.4 | 41.4 | 52.9 | 61.8 | 50.9 | 53.6 | | Claude-v1.3 | 54.2 | 39.0 | 51.9 | 61.7 | 52.1 | 53.7 | | Claude-instant-v1.0 | 45.9 | 35.5 | 43.1 | 53.8 | 44.2 | 45.4 | | moss-moon-003-base (16B) | 27.4 | 24.5 | 27.0 | 29.1 | 27.2 | 26.9 | | Ziya-LLaMA-13B-pretrain | 30.2 | 22.7 | 27.7 | 34.4 | 32.0 | 28.9 | | LLaMA-7B-hf | 27.1 | 25.9 | 27.1 | 26.8 | 27.9 | 26.3 | | ChatGLM-6B | 34.5 | 23.1 | 30.4 | 39.6 | 37.4 | 34.5 | | Falcon-7B | 25.8 | 24.3 | 25.8 | 26.0 | 25.8 | 25.6 | | Open-LLaMA-v2-pretrain (7B) | 24.0 | 22.5 | 23.1 | 25.3 | 25.2 | 23.2 | | TigerBot-7B-base | 25.7 | 27.0 | 27.3 | 24.7 | 23.4 | 26.1 | | Aquila-7B^* | 25.5 | 25.2 | 25.6 | 24.6 | 25.2 | 26.6 | | BLOOM-7B | 22.8 | 20.2 | 21.8 | 23.3 | 23.9 | 23.3 | | BLOOMZ-7B | 35.7 | 25.8 | 31.3 | 43.5 | 36.6 | 35.6 | | **Baichuan-7B** | 42.8 | 31.5 | 38.2 | 52.0 | 46.2 | 39.3 | #### Gaokao [Gaokao](https://github.com/ExpressAI/AI-Gaokao) 是一个以中国高考题作为评测大语言模型能力的数据集，用以评估模型的语言能力和逻辑推理能力。 我们只保留了其中的单项选择题，并对所有模型进行统一5-shot测试。 以下是测试的结果。 | Model | Average | |-------------------------|-----------------| | Open-LLaMA-v2-pretrain | 21.41 | | Ziya-LLaMA-13B-pretrain | 23.17 | | Falcon-7B | 23.98 | | TigerBot-7B-base | 25.94 | | LLaMA-7B | 27.81 | | ChatGLM-6B | 21.41 | | BLOOM-7B | 26.96 | | BLOOMZ-7B | 28.72 | | Aquila-7B^* | 24.39 | | **Baichuan-7B** | **36.24** | #### AGIEval [AGIEval](https://github.com/microsoft/AGIEval) 旨在评估模型的认知和解决问题相关的任务中的一般能力。 我们只保留了其中的四选一单项选择题，随机划分后对所有模型进行了统一5-shot测试。 | Model | Average | |-------------------------|-----------------| | Open-LLaMA-v2-pretrain | 23.49 | | Ziya-LLaMA-13B-pretrain | 27.64 | | Falcon-7B | 27.18 | | TigerBot-7B-base | 25.19 | | LLaMA-7B | 28.17 | | ChatGLM-6B | 23.49 | | BLOOM-7B | 26.55 | | BLOOMZ-7B | 30.27 | | Aquila-7B^* | 25.58 | | **Baichuan-7B** | **34.44** | ^*其中Aquila模型来源于[智源官方网站](https://model.baai.ac.cn/model-detail/100098)，仅做参考 ### English Leaderboard In addition to Chinese, we also tested the model's performance in English. #### MMLU [MMLU](https://arxiv.org/abs/2009.03300) is an English evaluation dataset that includes 57 multiple-choice tasks, covering elementary mathematics, American history, computer science, law, etc. The difficulty ranges from high school level to expert level, making it a mainstream LLM evaluation dataset. We adopted the [open-source]((https://github.com/hendrycks/test)) evaluation scheme, and the final 5-shot results are as follows: | Model | Humanities | Social Sciences | STEM | Other | Average | |----------------------------------------|-----------:|:---------------:|:----:|:-----:|:-------:| | LLaMA-7B² | 34.0 | 38.3 | 30.5 | 38.1 | 35.1 | | Falcon-7B¹ | - | - | - | - | 35.0 | | mpt-7B¹ | - | - | - | - | 35.6 | | ChatGLM-6B⁰ | 35.4 | 41.0 | 31.3 | 40.5 | 36.9 | | BLOOM 7B⁰ | 25.0 | 24.4 | 26.5 | 26.4 | 25.5 | | BLOOMZ 7B⁰ | 31.3 | 42.1 | 34.4 | 39.0 | 36.1 | | moss-moon-003-base (16B)⁰ | 24.2 | 22.8 | 22.4 | 24.4 | 23.6 | | moss-moon-003-sft (16B)⁰ | 30.5 | 33.8 | 29.3 | 34.4 | 31.9 | | **Baichuan-7B⁰** | 38.4 | 48.9 | 35.6 | 48.1 | 42.3 | The superscript in the Model column indicates the source of the results. ``` 0:reimplemented 1:https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard 2:https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu ``` ## Our Group 

d4data/biomedical-ner-all

d4data

transformers

token-classification

--- license: apache-2.0 language: - en tags: - Token Classification co2_eq_emissions: 0.0279399890043426 widget: - text: "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations. The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea. Except for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings." example_title: "example 1" - text: "A 63-year-old woman with no known cardiac history presented with a sudden onset of dyspnea requiring intubation and ventilatory support out of hospital. She denied preceding symptoms of chest discomfort, palpitations, syncope or infection. The patient was afebrile and normotensive, with a sinus tachycardia of 140 beats/min." example_title: "example 2" - text: "A 48 year-old female presented with vaginal bleeding and abnormal Pap smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic lymph nodes and the parametrium. Pathological examination revealed that the tumour also extensively involved the lower uterine segment." example_title: "example 3" --- ## About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased - Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942 - Carbon emission: 0.0279399890043426 Kg - Training time: 30.16527 minutes - GPU used : 1 x GeForce RTX 3060 Laptop GPU Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""") ``` ## Author This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at: > https://github.com/dreji18/Bio-Epidemiology-NER ## You can support me here :) <a href="https://www.buymeacoffee.com/deepakjohnreji" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

gsdf/Counterfeit-V2.5

gsdf

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Update V2.5 has been updated for ease of use as anime-style model. I use this embedding for negative prompts. https://huggingface.co/datasets/gsdf/EasyNegative Share by-products V2.1…Feeling of use similar to V2.0 V2.2…NSFW model # Counterfeit-V2.5 e.g.  ``` ((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 448x768, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ```  ``` ((masterpiece,best quality)),1girl, from below, solo, school uniform, serafuku, sky, cloud, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth, black skirt, medium hair, school bag , holding bag Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 832x512, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ```  ``` ((masterpiece,best quality)),2girls, black kimono, black legwear, black ribbon, black hair, cherry blossoms, day, flower, hair bun, hair ribbon, japanese clothes, kimono, long hair, looking at viewer, looking back, multiple girls, obi, outdoors, red eyes, red hair, ribbon, sandals, single hair bun, stairs, standing, statue, torii, tree, white kimono, yellow eyes Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 640x960, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```  ``` ((masterpiece,best quality)),1girl, bangs, blue eyes, blurry background, branch, brown hair, dappled sunlight, flower, from side, hair flower, hair ornament, japanese clothes, kimono, leaf, (maple leaf:1.9), obi, outdoors, sash, solo, sunlight, upper body Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```  ``` ((masterpiece,best quality))1girl, solo, black skirt, blue eyes, electric guitar, guitar, headphones, holding, holding plectrum, instrument, long hair, , music, one side up, pink hair, playing guiter, pleated skirt, black shirt, indoors Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```  ``` ((masterpiece,best quality)), 1girl, food, fruit, solo, skirt, shop, indoors, jacket, shopping, basket, jewelry, shirt, shelf, short hair, black hair, plaid skirt, black jacket, dutch angle, yellow eyes, looking at viewer Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```

facebook/dpr-question_encoder-single-nq-base

facebook

transformers

feature-extraction

--- language: en license: cc-by-nc-4.0 tags: - dpr datasets: - nq_open inference: false --- # `dpr-question_encoder-single-nq-base` ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** [Dense Passage Retrieval (DPR)](https://github.com/facebookresearch/DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-question_encoder-single-nq-base` is the question encoder trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open) ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)). - **Developed by:** See [GitHub repo](https://github.com/facebookresearch/DPR) for model developers - **Model Type:** BERT-based encoder - **Language(s):** [CC-BY-NC-4.0](https://github.com/facebookresearch/DPR/blob/main/LICENSE), also see [Code of Conduct](https://github.com/facebookresearch/DPR/blob/main/CODE_OF_CONDUCT.md) - **License:** English - **Related Models:** - [`dpr-ctx_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) - [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base) - [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base) - [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base) - [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base) - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/2004.04906) - [GitHub Repo](https://github.com/facebookresearch/DPR) - [Hugging Face DPR docs](https://huggingface.co/docs/transformers/main/en/model_doc/dpr) - [BERT Base Uncased Model Card](https://huggingface.co/bert-base-uncased) ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base") model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base") input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"] embeddings = model(input_ids).pooler_output ``` ## Uses #### Direct Use `dpr-question_encoder-single-nq-base`, [`dpr-ctx_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base), and [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base) can be used for the task of open-domain question answering. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models 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. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propogate 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)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Training #### Training Data This model was trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open) ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)). The model authors write that: > [The dataset] was designed for end-to-end question answering. The questions were mined from real Google search queries and the answers were spans in Wikipedia articles identified by annotators. #### Training Procedure The training procedure is described in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf): > Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run-time. > Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval. At run-time, DPR applies a different encoder EQ(·) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector. The authors report that for encoders, they used two independent BERT ([Devlin et al., 2019](https://aclanthology.org/N19-1423/)) networks (base, un-cased) and use FAISS ([Johnson et al., 2017](https://arxiv.org/abs/1702.08734)) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in-batch negatives. ## Evaluation The following evaluation information is extracted from the [associated paper](https://arxiv.org/pdf/2004.04906.pdf). #### Testing Data, Factors and Metrics The model developers report the performance of the model on five QA datasets, using the top-k accuracy (k ∈ {20, 100}). The datasets were [NQ](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), [CuratedTREC (TREC)](https://huggingface.co/datasets/trec), and [SQuAD v1.1](https://huggingface.co/datasets/squad). #### Results | | Top 20 | | | | | Top 100| | | | | |:----:|:------:|:---------:|:--:|:----:|:-----:|:------:|:---------:|:--:|:----:|:-----:| | | NQ | TriviaQA | WQ | TREC | SQuAD | NQ | TriviaQA | WQ | TREC | SQuAD | | | 78.4 | 79.4 |73.2| 79.8 | 63.2 | 85.4 | 85.0 |81.4| 89.1 | 77.2 | ## 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). We present the hardware type and based on the [associated paper](https://arxiv.org/abs/2004.04906). - **Hardware Type:** 8 32GB GPUs - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/abs/2004.04906) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @inproceedings{karpukhin-etal-2020-dense, title = "Dense Passage Retrieval for Open-Domain Question Answering", author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", doi = "10.18653/v1/2020.emnlp-main.550", pages = "6769--6781", } ``` ## Model Card Authors This model card was written by the team at Hugging Face.

sentence-transformers/stsb-xlm-r-multilingual

sentence-transformers

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # sentence-transformers/stsb-xlm-r-multilingual This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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/stsb-xlm-r-multilingual') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```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) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/stsb-xlm-r-multilingual') model = AutoModel.from_pretrained('sentence-transformers/stsb-xlm-r-multilingual') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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/stsb-xlm-r-multilingual) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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}) ) ``` ## 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", } ```

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).

facebook/sam-vit-base

facebook

transformers

--- license: apache-2.0 --- # Model Card for Segment Anything Model (SAM) - ViT Base (ViT-B) version <p> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-architecture.png" alt="Model architecture"> <em> Detailed architecture of Segment Anything Model (SAM).</em> </p> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Citation](#citation) # TL;DR [Link to original repository](https://github.com/facebookresearch/segment-anything) | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-beancans.png" alt="Snow" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-dog-masks.png" alt="Forest" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car-seg.png" alt="Mountains" width="600" height="600"> | |---------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------| The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. The abstract of the paper states: > We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything). # Model Details The SAM model is made up of 3 modules: - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used. - The `PromptEncoder`: generates embeddings for points and bounding boxes - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`. # Usage ## Prompted-Mask-Generation ```python from PIL import Image import requests from transformers import SamModel, SamProcessor model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") input_points = [[[450, 600]]] # 2D localization of a window ``` ```python inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda") outputs = model(**inputs) masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()) scores = outputs.iou_scores ``` Among other arguments to generate masks, you can pass 2D locations on the approximate position of your object of interest, a bounding box wrapping the object of interest (the format should be x, y coordinate of the top right and bottom left point of the bounding box), a segmentation mask. At this time of writing, passing a text as input is not supported by the official model according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844). For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example! ## Automatic-Mask-Generation The model can be used for generating segmentation masks in a "zero-shot" fashion, given an input image. The model is automatically prompt with a grid of `1024` points which are all fed to the model. The pipeline is made for automatic mask generation. The following snippet demonstrates how easy you can run it (on any device! Simply feed the appropriate `points_per_batch` argument) ```python from transformers import pipeline generator = pipeline("mask-generation", device = 0, points_per_batch = 256) image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" outputs = generator(image_url, points_per_batch = 256) ``` Now to display the image: ```python import matplotlib.pyplot as plt from PIL import Image import numpy as np def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) plt.imshow(np.array(raw_image)) ax = plt.gca() for mask in outputs["masks"]: show_mask(mask, ax=ax, random_color=True) plt.axis("off") plt.show() ``` # Citation If you use this model, please use the following BibTeX entry. ``` @article{kirillov2023segany, title={Segment Anything}, author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, journal={arXiv:2304.02643}, year={2023} } ```

kredor/punctuate-all

kredor

transformers

token-classification

This is based on [Oliver Guhr's work](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large). The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. ----- report ----- precision recall f1-score support 0 0.99 0.99 0.99 73317475 . 0.94 0.95 0.95 4484845 , 0.86 0.86 0.86 6100650 ? 0.88 0.85 0.86 136479 - 0.60 0.29 0.39 233630 : 0.71 0.49 0.58 152424 accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503 ----- confusion matrix ----- t/p 0 . , ? - : 0 1.0 0.0 0.0 0.0 0.0 0.0 . 0.0 1.0 0.0 0.0 0.0 0.0 , 0.1 0.0 0.9 0.0 0.0 0.0 ? 0.0 0.1 0.0 0.8 0.0 0.0 - 0.1 0.1 0.5 0.0 0.3 0.0 : 0.0 0.3 0.1 0.0 0.0 0.5

EK12317/Ekmix-Diffusion

EK12317

diffusers

text-to-image

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- ## Example: ”Negative prompt: (worst quality, low quality:1.4)” is really useful in anywhere I think all models are great with correct Hires.fix ## Ekmix-Pastel pastel but lines(with Hires.fix) (Merging the loras into the model.) ~~~ python networks\merge_lora.py --sd_model .\models\model.safetensors --save_to .\lora\2.safetensors --models .\lora\MagicLORA.pt .\lora\Jordan_3.safetensors .\lora\sttabi_v1.4-04.safetensors .\lora\xlimo768.pt .\lora\dpep2.pt --ratios 0.3 1 0.5 0.6 0.35 ~~~  ~~~ masterpiece,best quality,best quality,Amazing,beautiful detailed eyes,1girl,finely detail,Depth offield,extremely detailed CG unity 8k wallpaper,masterpiece,upper body,(vtuber minato aqua),pink hair,blue streaked hair, palace,holy,white long split mop dress ,mature female,standing,medium_breasts,silver-tiara,smile,black high heels,very long hair, body towards aside,jewelry,hair blue flower,grey eyes,close-up, Negative prompt: (worst quality, low quality:1.3) Steps: 30, Sampler: Euler a, CFG scale: 6, Seed: 191289851, Size: 512x768, Model hash: 0526445f65, Denoising strength: 0.5, Eta: 0.5, Clip skip: 2, ENSD: 31337, Hires resize: 856x1280, Hires steps: 30, Hires upscaler: Latent ~~~ pastel but lines(without hires fix) (better!)  ~~~ {masterpiece},{best quality},{1girl,{{loli},black hair,blue eyes,very long hair,hair flower,hanfu,happy}},Amazing,beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG,original,outdoors,beautiful detailed hand,beautiful detailed fingers,{{soaked},{wet through}},{body under water},standing,{beautiful detailed water,beautiful detailed sky,fluttered detailed splashs} Negative prompt: (worst quality, low quality:1.3) Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 6, Seed: 2035526620, Size: 768x512, Model hash: ca485b96f8, Eta: 0.5, Clip skip: 2, ENSD: 31337 ~~~ ## Ekmix-gen4 balance between anime and reality(Merging by block weighted merge.)  ~~~ masterpiece,best quality,best quality,Amazing,beautiful detailed eyes,1girl,finely detail,Depth offield,extremely detailed CG unity 8k wallpaper,masterpiece,upper body,(vtuber minato aqua),pink hair,blue streaked hair, palace,holy,white long split mop dress ,mature female,standing,medium_breasts,silver-tiara,smile,black high heels,very long hair, body towards aside,jewelry,hair blue flower,grey eyes,close-up, ~~~  ~~~ {masterpiece},{best quality},{1girl,{{loli},black hair,blue eyes,very long hair,hair flower,hanfu,happy}},Amazing,beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG,original,outdoors,beautiful detailed hand,beautiful detailed fingers,{{soaked},{wet through}},{body under water},standing,{beautiful detailed water,beautiful detailed sky,fluttered detailed splashs},by Paul Hedley, ~~~ # Great hypernetworks style1 and 2 are my favourite. 3,4 may need retrain. 

Helsinki-NLP/opus-mt-mul-en

Helsinki-NLP

transformers

translation

--- language: - ca - es - os - eo - ro - fy - cy - is - lb - su - an - sq - fr - ht - rm - cv - ig - am - eu - tr - ps - af - ny - ch - uk - sl - lt - tk - sg - ar - lg - bg - be - ka - gd - ja - si - br - mh - km - th - ty - rw - te - mk - or - wo - kl - mr - ru - yo - hu - fo - zh - ti - co - ee - oc - sn - mt - ts - pl - gl - nb - bn - tt - bo - lo - id - gn - nv - hy - kn - to - io - so - vi - da - fj - gv - sm - nl - mi - pt - hi - se - as - ta - et - kw - ga - sv - ln - na - mn - gu - wa - lv - jv - el - my - ba - it - hr - ur - ce - nn - fi - mg - rn - xh - ab - de - cs - he - zu - yi - ml - mul - en tags: - translation license: apache-2.0 --- ### mul-eng * source group: Multiple languages * target group: English * OPUS readme: [mul-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md) * model: transformer * source language(s): abk acm ady afb afh_Latn afr akl_Latn aln amh ang_Latn apc ara arg arq ary arz asm ast avk_Latn awa aze_Latn bak bam_Latn bel bel_Latn ben bho bod bos_Latn bre brx brx_Latn bul bul_Latn cat ceb ces cha che chr chv cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant cor cos crh crh_Latn csb_Latn cym dan deu dsb dtp dws_Latn egl ell enm_Latn epo est eus ewe ext fao fij fin fkv_Latn fra frm_Latn frr fry fuc fuv gan gcf_Latn gil gla gle glg glv gom gos got_Goth grc_Grek grn gsw guj hat hau_Latn haw heb hif_Latn hil hin hnj_Latn hoc hoc_Latn hrv hsb hun hye iba ibo ido ido_Latn ike_Latn ile_Latn ilo ina_Latn ind isl ita izh jav jav_Java jbo jbo_Cyrl jbo_Latn jdt_Cyrl jpn kab kal kan kat kaz_Cyrl kaz_Latn kek_Latn kha khm khm_Latn kin kir_Cyrl kjh kpv krl ksh kum kur_Arab kur_Latn lad lad_Latn lao lat_Latn lav ldn_Latn lfn_Cyrl lfn_Latn lij lin lit liv_Latn lkt lld_Latn lmo ltg ltz lug lzh lzh_Hans mad mah mai mal mar max_Latn mdf mfe mhr mic min mkd mlg mlt mnw moh mon mri mwl mww mya myv nan nau nav nds niu nld nno nob nob_Hebr nog non_Latn nov_Latn npi nya oci ori orv_Cyrl oss ota_Arab ota_Latn pag pan_Guru pap pau pdc pes pes_Latn pes_Thaa pms pnb pol por ppl_Latn prg_Latn pus quc qya qya_Latn rap rif_Latn roh rom ron rue run rus sag sah san_Deva scn sco sgs shs_Latn shy_Latn sin sjn_Latn slv sma sme smo sna snd_Arab som spa sqi srp_Cyrl srp_Latn stq sun swe swg swh tah tam tat tat_Arab tat_Latn tel tet tgk_Cyrl tha tir tlh_Latn tly_Latn tmw_Latn toi_Latn ton tpw_Latn tso tuk tuk_Latn tur tvl tyv tzl tzl_Latn udm uig_Arab uig_Cyrl ukr umb urd uzb_Cyrl uzb_Latn vec vie vie_Hani vol_Latn vro war wln wol wuu xal xho yid yor yue yue_Hans yue_Hant zho zho_Hans zho_Hant zlm_Latn zsm_Latn zul zza * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014-hineng.hin.eng | 8.5 | 0.341 | | newsdev2015-enfi-fineng.fin.eng | 16.8 | 0.441 | | newsdev2016-enro-roneng.ron.eng | 31.3 | 0.580 | | newsdev2016-entr-tureng.tur.eng | 16.4 | 0.422 | | newsdev2017-enlv-laveng.lav.eng | 21.3 | 0.502 | | newsdev2017-enzh-zhoeng.zho.eng | 12.7 | 0.409 | | newsdev2018-enet-esteng.est.eng | 19.8 | 0.467 | | newsdev2019-engu-gujeng.guj.eng | 13.3 | 0.385 | | newsdev2019-enlt-liteng.lit.eng | 19.9 | 0.482 | | newsdiscussdev2015-enfr-fraeng.fra.eng | 26.7 | 0.520 | | newsdiscusstest2015-enfr-fraeng.fra.eng | 29.8 | 0.541 | | newssyscomb2009-ceseng.ces.eng | 21.1 | 0.487 | | newssyscomb2009-deueng.deu.eng | 22.6 | 0.499 | | newssyscomb2009-fraeng.fra.eng | 25.8 | 0.530 | | newssyscomb2009-huneng.hun.eng | 15.1 | 0.430 | | newssyscomb2009-itaeng.ita.eng | 29.4 | 0.555 | | newssyscomb2009-spaeng.spa.eng | 26.1 | 0.534 | | news-test2008-deueng.deu.eng | 21.6 | 0.491 | | news-test2008-fraeng.fra.eng | 22.3 | 0.502 | | news-test2008-spaeng.spa.eng | 23.6 | 0.514 | | newstest2009-ceseng.ces.eng | 19.8 | 0.480 | | newstest2009-deueng.deu.eng | 20.9 | 0.487 | | newstest2009-fraeng.fra.eng | 25.0 | 0.523 | | newstest2009-huneng.hun.eng | 14.7 | 0.425 | | newstest2009-itaeng.ita.eng | 27.6 | 0.542 | | newstest2009-spaeng.spa.eng | 25.7 | 0.530 | | newstest2010-ceseng.ces.eng | 20.6 | 0.491 | | newstest2010-deueng.deu.eng | 23.4 | 0.517 | | newstest2010-fraeng.fra.eng | 26.1 | 0.537 | | newstest2010-spaeng.spa.eng | 29.1 | 0.561 | | newstest2011-ceseng.ces.eng | 21.0 | 0.489 | | newstest2011-deueng.deu.eng | 21.3 | 0.494 | | newstest2011-fraeng.fra.eng | 26.8 | 0.546 | | newstest2011-spaeng.spa.eng | 28.2 | 0.549 | | newstest2012-ceseng.ces.eng | 20.5 | 0.485 | | newstest2012-deueng.deu.eng | 22.3 | 0.503 | | newstest2012-fraeng.fra.eng | 27.5 | 0.545 | | newstest2012-ruseng.rus.eng | 26.6 | 0.532 | | newstest2012-spaeng.spa.eng | 30.3 | 0.567 | | newstest2013-ceseng.ces.eng | 22.5 | 0.498 | | newstest2013-deueng.deu.eng | 25.0 | 0.518 | | newstest2013-fraeng.fra.eng | 27.4 | 0.537 | | newstest2013-ruseng.rus.eng | 21.6 | 0.484 | | newstest2013-spaeng.spa.eng | 28.4 | 0.555 | | newstest2014-csen-ceseng.ces.eng | 24.0 | 0.517 | | newstest2014-deen-deueng.deu.eng | 24.1 | 0.511 | | newstest2014-fren-fraeng.fra.eng | 29.1 | 0.563 | | newstest2014-hien-hineng.hin.eng | 14.0 | 0.414 | | newstest2014-ruen-ruseng.rus.eng | 24.0 | 0.521 | | newstest2015-encs-ceseng.ces.eng | 21.9 | 0.481 | | newstest2015-ende-deueng.deu.eng | 25.5 | 0.519 | | newstest2015-enfi-fineng.fin.eng | 17.4 | 0.441 | | newstest2015-enru-ruseng.rus.eng | 22.4 | 0.494 | | newstest2016-encs-ceseng.ces.eng | 23.0 | 0.500 | | newstest2016-ende-deueng.deu.eng | 30.1 | 0.560 | | newstest2016-enfi-fineng.fin.eng | 18.5 | 0.461 | | newstest2016-enro-roneng.ron.eng | 29.6 | 0.562 | | newstest2016-enru-ruseng.rus.eng | 22.0 | 0.495 | | newstest2016-entr-tureng.tur.eng | 14.8 | 0.415 | | newstest2017-encs-ceseng.ces.eng | 20.2 | 0.475 | | newstest2017-ende-deueng.deu.eng | 26.0 | 0.523 | | newstest2017-enfi-fineng.fin.eng | 19.6 | 0.465 | | newstest2017-enlv-laveng.lav.eng | 16.2 | 0.454 | | newstest2017-enru-ruseng.rus.eng | 24.2 | 0.510 | | newstest2017-entr-tureng.tur.eng | 15.0 | 0.412 | | newstest2017-enzh-zhoeng.zho.eng | 13.7 | 0.412 | | newstest2018-encs-ceseng.ces.eng | 21.2 | 0.486 | | newstest2018-ende-deueng.deu.eng | 31.5 | 0.564 | | newstest2018-enet-esteng.est.eng | 19.7 | 0.473 | | newstest2018-enfi-fineng.fin.eng | 15.1 | 0.418 | | newstest2018-enru-ruseng.rus.eng | 21.3 | 0.490 | | newstest2018-entr-tureng.tur.eng | 15.4 | 0.421 | | newstest2018-enzh-zhoeng.zho.eng | 12.9 | 0.408 | | newstest2019-deen-deueng.deu.eng | 27.0 | 0.529 | | newstest2019-fien-fineng.fin.eng | 17.2 | 0.438 | | newstest2019-guen-gujeng.guj.eng | 9.0 | 0.342 | | newstest2019-lten-liteng.lit.eng | 22.6 | 0.512 | | newstest2019-ruen-ruseng.rus.eng | 24.1 | 0.503 | | newstest2019-zhen-zhoeng.zho.eng | 13.9 | 0.427 | | newstestB2016-enfi-fineng.fin.eng | 15.2 | 0.428 | | newstestB2017-enfi-fineng.fin.eng | 16.8 | 0.442 | | newstestB2017-fien-fineng.fin.eng | 16.8 | 0.442 | | Tatoeba-test.abk-eng.abk.eng | 2.4 | 0.190 | | Tatoeba-test.ady-eng.ady.eng | 1.1 | 0.111 | | Tatoeba-test.afh-eng.afh.eng | 1.7 | 0.108 | | Tatoeba-test.afr-eng.afr.eng | 53.0 | 0.672 | | Tatoeba-test.akl-eng.akl.eng | 5.9 | 0.239 | | Tatoeba-test.amh-eng.amh.eng | 25.6 | 0.464 | | Tatoeba-test.ang-eng.ang.eng | 11.7 | 0.289 | | Tatoeba-test.ara-eng.ara.eng | 26.4 | 0.443 | | Tatoeba-test.arg-eng.arg.eng | 35.9 | 0.473 | | Tatoeba-test.asm-eng.asm.eng | 19.8 | 0.365 | | Tatoeba-test.ast-eng.ast.eng | 31.8 | 0.467 | | Tatoeba-test.avk-eng.avk.eng | 0.4 | 0.119 | | Tatoeba-test.awa-eng.awa.eng | 9.7 | 0.271 | | Tatoeba-test.aze-eng.aze.eng | 37.0 | 0.542 | | Tatoeba-test.bak-eng.bak.eng | 13.9 | 0.395 | | Tatoeba-test.bam-eng.bam.eng | 2.2 | 0.094 | | Tatoeba-test.bel-eng.bel.eng | 36.8 | 0.549 | | Tatoeba-test.ben-eng.ben.eng | 39.7 | 0.546 | | Tatoeba-test.bho-eng.bho.eng | 33.6 | 0.540 | | Tatoeba-test.bod-eng.bod.eng | 1.1 | 0.147 | | Tatoeba-test.bre-eng.bre.eng | 14.2 | 0.303 | | Tatoeba-test.brx-eng.brx.eng | 1.7 | 0.130 | | Tatoeba-test.bul-eng.bul.eng | 46.0 | 0.621 | | Tatoeba-test.cat-eng.cat.eng | 46.6 | 0.636 | | Tatoeba-test.ceb-eng.ceb.eng | 17.4 | 0.347 | | Tatoeba-test.ces-eng.ces.eng | 41.3 | 0.586 | | Tatoeba-test.cha-eng.cha.eng | 7.9 | 0.232 | | Tatoeba-test.che-eng.che.eng | 0.7 | 0.104 | | Tatoeba-test.chm-eng.chm.eng | 7.3 | 0.261 | | Tatoeba-test.chr-eng.chr.eng | 8.8 | 0.244 | | Tatoeba-test.chv-eng.chv.eng | 11.0 | 0.319 | | Tatoeba-test.cor-eng.cor.eng | 5.4 | 0.204 | | Tatoeba-test.cos-eng.cos.eng | 58.2 | 0.643 | | Tatoeba-test.crh-eng.crh.eng | 26.3 | 0.399 | | Tatoeba-test.csb-eng.csb.eng | 18.8 | 0.389 | | Tatoeba-test.cym-eng.cym.eng | 23.4 | 0.407 | | Tatoeba-test.dan-eng.dan.eng | 50.5 | 0.659 | | Tatoeba-test.deu-eng.deu.eng | 39.6 | 0.579 | | Tatoeba-test.dsb-eng.dsb.eng | 24.3 | 0.449 | | Tatoeba-test.dtp-eng.dtp.eng | 1.0 | 0.149 | | Tatoeba-test.dws-eng.dws.eng | 1.6 | 0.061 | | Tatoeba-test.egl-eng.egl.eng | 7.6 | 0.236 | | Tatoeba-test.ell-eng.ell.eng | 55.4 | 0.682 | | Tatoeba-test.enm-eng.enm.eng | 28.0 | 0.489 | | Tatoeba-test.epo-eng.epo.eng | 41.8 | 0.591 | | Tatoeba-test.est-eng.est.eng | 41.5 | 0.581 | | Tatoeba-test.eus-eng.eus.eng | 37.8 | 0.557 | | Tatoeba-test.ewe-eng.ewe.eng | 10.7 | 0.262 | | Tatoeba-test.ext-eng.ext.eng | 25.5 | 0.405 | | Tatoeba-test.fao-eng.fao.eng | 28.7 | 0.469 | | Tatoeba-test.fas-eng.fas.eng | 7.5 | 0.281 | | Tatoeba-test.fij-eng.fij.eng | 24.2 | 0.320 | | Tatoeba-test.fin-eng.fin.eng | 35.8 | 0.534 | | Tatoeba-test.fkv-eng.fkv.eng | 15.5 | 0.434 | | Tatoeba-test.fra-eng.fra.eng | 45.1 | 0.618 | | Tatoeba-test.frm-eng.frm.eng | 29.6 | 0.427 | | Tatoeba-test.frr-eng.frr.eng | 5.5 | 0.138 | | Tatoeba-test.fry-eng.fry.eng | 25.3 | 0.455 | | Tatoeba-test.ful-eng.ful.eng | 1.1 | 0.127 | | Tatoeba-test.gcf-eng.gcf.eng | 16.0 | 0.315 | | Tatoeba-test.gil-eng.gil.eng | 46.7 | 0.587 | | Tatoeba-test.gla-eng.gla.eng | 20.2 | 0.358 | | Tatoeba-test.gle-eng.gle.eng | 43.9 | 0.592 | | Tatoeba-test.glg-eng.glg.eng | 45.1 | 0.623 | | Tatoeba-test.glv-eng.glv.eng | 3.3 | 0.119 | | Tatoeba-test.gos-eng.gos.eng | 20.1 | 0.364 | | Tatoeba-test.got-eng.got.eng | 0.1 | 0.041 | | Tatoeba-test.grc-eng.grc.eng | 2.1 | 0.137 | | Tatoeba-test.grn-eng.grn.eng | 1.7 | 0.152 | | Tatoeba-test.gsw-eng.gsw.eng | 18.2 | 0.334 | | Tatoeba-test.guj-eng.guj.eng | 21.7 | 0.373 | | Tatoeba-test.hat-eng.hat.eng | 34.5 | 0.502 | | Tatoeba-test.hau-eng.hau.eng | 10.5 | 0.295 | | Tatoeba-test.haw-eng.haw.eng | 2.8 | 0.160 | | Tatoeba-test.hbs-eng.hbs.eng | 46.7 | 0.623 | | Tatoeba-test.heb-eng.heb.eng | 33.0 | 0.492 | | Tatoeba-test.hif-eng.hif.eng | 17.0 | 0.391 | | Tatoeba-test.hil-eng.hil.eng | 16.0 | 0.339 | | Tatoeba-test.hin-eng.hin.eng | 36.4 | 0.533 | | Tatoeba-test.hmn-eng.hmn.eng | 0.4 | 0.131 | | Tatoeba-test.hoc-eng.hoc.eng | 0.7 | 0.132 | | Tatoeba-test.hsb-eng.hsb.eng | 41.9 | 0.551 | | Tatoeba-test.hun-eng.hun.eng | 33.2 | 0.510 | | Tatoeba-test.hye-eng.hye.eng | 32.2 | 0.487 | | Tatoeba-test.iba-eng.iba.eng | 9.4 | 0.278 | | Tatoeba-test.ibo-eng.ibo.eng | 5.8 | 0.200 | | Tatoeba-test.ido-eng.ido.eng | 31.7 | 0.503 | | Tatoeba-test.iku-eng.iku.eng | 9.1 | 0.164 | | Tatoeba-test.ile-eng.ile.eng | 42.2 | 0.595 | | Tatoeba-test.ilo-eng.ilo.eng | 29.7 | 0.485 | | Tatoeba-test.ina-eng.ina.eng | 42.1 | 0.607 | | Tatoeba-test.isl-eng.isl.eng | 35.7 | 0.527 | | Tatoeba-test.ita-eng.ita.eng | 54.8 | 0.686 | | Tatoeba-test.izh-eng.izh.eng | 28.3 | 0.526 | | Tatoeba-test.jav-eng.jav.eng | 10.0 | 0.282 | | Tatoeba-test.jbo-eng.jbo.eng | 0.3 | 0.115 | | Tatoeba-test.jdt-eng.jdt.eng | 5.3 | 0.140 | | Tatoeba-test.jpn-eng.jpn.eng | 18.8 | 0.387 | | Tatoeba-test.kab-eng.kab.eng | 3.9 | 0.205 | | Tatoeba-test.kal-eng.kal.eng | 16.9 | 0.329 | | Tatoeba-test.kan-eng.kan.eng | 16.2 | 0.374 | | Tatoeba-test.kat-eng.kat.eng | 31.1 | 0.493 | | Tatoeba-test.kaz-eng.kaz.eng | 24.5 | 0.437 | | Tatoeba-test.kek-eng.kek.eng | 7.4 | 0.192 | | Tatoeba-test.kha-eng.kha.eng | 1.0 | 0.154 | | Tatoeba-test.khm-eng.khm.eng | 12.2 | 0.290 | | Tatoeba-test.kin-eng.kin.eng | 22.5 | 0.355 | | Tatoeba-test.kir-eng.kir.eng | 27.2 | 0.470 | | Tatoeba-test.kjh-eng.kjh.eng | 2.1 | 0.129 | | Tatoeba-test.kok-eng.kok.eng | 4.5 | 0.259 | | Tatoeba-test.kom-eng.kom.eng | 1.4 | 0.099 | | Tatoeba-test.krl-eng.krl.eng | 26.1 | 0.387 | | Tatoeba-test.ksh-eng.ksh.eng | 5.5 | 0.256 | | Tatoeba-test.kum-eng.kum.eng | 9.3 | 0.288 | | Tatoeba-test.kur-eng.kur.eng | 9.6 | 0.208 | | Tatoeba-test.lad-eng.lad.eng | 30.1 | 0.475 | | Tatoeba-test.lah-eng.lah.eng | 11.6 | 0.284 | | Tatoeba-test.lao-eng.lao.eng | 4.5 | 0.214 | | Tatoeba-test.lat-eng.lat.eng | 21.5 | 0.402 | | Tatoeba-test.lav-eng.lav.eng | 40.2 | 0.577 | | Tatoeba-test.ldn-eng.ldn.eng | 0.8 | 0.115 | | Tatoeba-test.lfn-eng.lfn.eng | 23.0 | 0.433 | | Tatoeba-test.lij-eng.lij.eng | 9.3 | 0.287 | | Tatoeba-test.lin-eng.lin.eng | 2.4 | 0.196 | | Tatoeba-test.lit-eng.lit.eng | 44.0 | 0.597 | | Tatoeba-test.liv-eng.liv.eng | 1.6 | 0.115 | | Tatoeba-test.lkt-eng.lkt.eng | 2.0 | 0.113 | | Tatoeba-test.lld-eng.lld.eng | 18.3 | 0.312 | | Tatoeba-test.lmo-eng.lmo.eng | 25.4 | 0.395 | | Tatoeba-test.ltz-eng.ltz.eng | 35.9 | 0.509 | | Tatoeba-test.lug-eng.lug.eng | 5.1 | 0.357 | | Tatoeba-test.mad-eng.mad.eng | 2.8 | 0.123 | | Tatoeba-test.mah-eng.mah.eng | 5.7 | 0.175 | | Tatoeba-test.mai-eng.mai.eng | 56.3 | 0.703 | | Tatoeba-test.mal-eng.mal.eng | 37.5 | 0.534 | | Tatoeba-test.mar-eng.mar.eng | 22.8 | 0.470 | | Tatoeba-test.mdf-eng.mdf.eng | 2.0 | 0.110 | | Tatoeba-test.mfe-eng.mfe.eng | 59.2 | 0.764 | | Tatoeba-test.mic-eng.mic.eng | 9.0 | 0.199 | | Tatoeba-test.mkd-eng.mkd.eng | 44.3 | 0.593 | | Tatoeba-test.mlg-eng.mlg.eng | 31.9 | 0.424 | | Tatoeba-test.mlt-eng.mlt.eng | 38.6 | 0.540 | | Tatoeba-test.mnw-eng.mnw.eng | 2.5 | 0.101 | | Tatoeba-test.moh-eng.moh.eng | 0.3 | 0.110 | | Tatoeba-test.mon-eng.mon.eng | 13.5 | 0.334 | | Tatoeba-test.mri-eng.mri.eng | 8.5 | 0.260 | | Tatoeba-test.msa-eng.msa.eng | 33.9 | 0.520 | | Tatoeba-test.multi.eng | 34.7 | 0.518 | | Tatoeba-test.mwl-eng.mwl.eng | 37.4 | 0.630 | | Tatoeba-test.mya-eng.mya.eng | 15.5 | 0.335 | | Tatoeba-test.myv-eng.myv.eng | 0.8 | 0.118 | | Tatoeba-test.nau-eng.nau.eng | 9.0 | 0.186 | | Tatoeba-test.nav-eng.nav.eng | 1.3 | 0.144 | | Tatoeba-test.nds-eng.nds.eng | 30.7 | 0.495 | | Tatoeba-test.nep-eng.nep.eng | 3.5 | 0.168 | | Tatoeba-test.niu-eng.niu.eng | 42.7 | 0.492 | | Tatoeba-test.nld-eng.nld.eng | 47.9 | 0.640 | | Tatoeba-test.nog-eng.nog.eng | 12.7 | 0.284 | | Tatoeba-test.non-eng.non.eng | 43.8 | 0.586 | | Tatoeba-test.nor-eng.nor.eng | 45.5 | 0.619 | | Tatoeba-test.nov-eng.nov.eng | 26.9 | 0.472 | | Tatoeba-test.nya-eng.nya.eng | 33.2 | 0.456 | | Tatoeba-test.oci-eng.oci.eng | 17.9 | 0.370 | | Tatoeba-test.ori-eng.ori.eng | 14.6 | 0.305 | | Tatoeba-test.orv-eng.orv.eng | 11.0 | 0.283 | | Tatoeba-test.oss-eng.oss.eng | 4.1 | 0.211 | | Tatoeba-test.ota-eng.ota.eng | 4.1 | 0.216 | | Tatoeba-test.pag-eng.pag.eng | 24.3 | 0.468 | | Tatoeba-test.pan-eng.pan.eng | 16.4 | 0.358 | | Tatoeba-test.pap-eng.pap.eng | 53.2 | 0.628 | | Tatoeba-test.pau-eng.pau.eng | 3.7 | 0.173 | | Tatoeba-test.pdc-eng.pdc.eng | 45.3 | 0.569 | | Tatoeba-test.pms-eng.pms.eng | 14.0 | 0.345 | | Tatoeba-test.pol-eng.pol.eng | 41.7 | 0.588 | | Tatoeba-test.por-eng.por.eng | 51.4 | 0.669 | | Tatoeba-test.ppl-eng.ppl.eng | 0.4 | 0.134 | | Tatoeba-test.prg-eng.prg.eng | 4.1 | 0.198 | | Tatoeba-test.pus-eng.pus.eng | 6.7 | 0.233 | | Tatoeba-test.quc-eng.quc.eng | 3.5 | 0.091 | | Tatoeba-test.qya-eng.qya.eng | 0.2 | 0.090 | | Tatoeba-test.rap-eng.rap.eng | 17.5 | 0.230 | | Tatoeba-test.rif-eng.rif.eng | 4.2 | 0.164 | | Tatoeba-test.roh-eng.roh.eng | 24.6 | 0.464 | | Tatoeba-test.rom-eng.rom.eng | 3.4 | 0.212 | | Tatoeba-test.ron-eng.ron.eng | 45.2 | 0.620 | | Tatoeba-test.rue-eng.rue.eng | 21.4 | 0.390 | | Tatoeba-test.run-eng.run.eng | 24.5 | 0.392 | | Tatoeba-test.rus-eng.rus.eng | 42.7 | 0.591 | | Tatoeba-test.sag-eng.sag.eng | 3.4 | 0.187 | | Tatoeba-test.sah-eng.sah.eng | 5.0 | 0.177 | | Tatoeba-test.san-eng.san.eng | 2.0 | 0.172 | | Tatoeba-test.scn-eng.scn.eng | 35.8 | 0.410 | | Tatoeba-test.sco-eng.sco.eng | 34.6 | 0.520 | | Tatoeba-test.sgs-eng.sgs.eng | 21.8 | 0.299 | | Tatoeba-test.shs-eng.shs.eng | 1.8 | 0.122 | | Tatoeba-test.shy-eng.shy.eng | 1.4 | 0.104 | | Tatoeba-test.sin-eng.sin.eng | 20.6 | 0.429 | | Tatoeba-test.sjn-eng.sjn.eng | 1.2 | 0.095 | | Tatoeba-test.slv-eng.slv.eng | 37.0 | 0.545 | | Tatoeba-test.sma-eng.sma.eng | 4.4 | 0.147 | | Tatoeba-test.sme-eng.sme.eng | 8.9 | 0.229 | | Tatoeba-test.smo-eng.smo.eng | 37.7 | 0.483 | | Tatoeba-test.sna-eng.sna.eng | 18.0 | 0.359 | | Tatoeba-test.snd-eng.snd.eng | 28.1 | 0.444 | | Tatoeba-test.som-eng.som.eng | 23.6 | 0.472 | | Tatoeba-test.spa-eng.spa.eng | 47.9 | 0.645 | | Tatoeba-test.sqi-eng.sqi.eng | 46.9 | 0.634 | | Tatoeba-test.stq-eng.stq.eng | 8.1 | 0.379 | | Tatoeba-test.sun-eng.sun.eng | 23.8 | 0.369 | | Tatoeba-test.swa-eng.swa.eng | 6.5 | 0.193 | | Tatoeba-test.swe-eng.swe.eng | 51.4 | 0.655 | | Tatoeba-test.swg-eng.swg.eng | 18.5 | 0.342 | | Tatoeba-test.tah-eng.tah.eng | 25.6 | 0.249 | | Tatoeba-test.tam-eng.tam.eng | 29.1 | 0.437 | | Tatoeba-test.tat-eng.tat.eng | 12.9 | 0.327 | | Tatoeba-test.tel-eng.tel.eng | 21.2 | 0.386 | | Tatoeba-test.tet-eng.tet.eng | 9.2 | 0.215 | | Tatoeba-test.tgk-eng.tgk.eng | 12.7 | 0.374 | | Tatoeba-test.tha-eng.tha.eng | 36.3 | 0.531 | | Tatoeba-test.tir-eng.tir.eng | 9.1 | 0.267 | | Tatoeba-test.tlh-eng.tlh.eng | 0.2 | 0.084 | | Tatoeba-test.tly-eng.tly.eng | 2.1 | 0.128 | | Tatoeba-test.toi-eng.toi.eng | 5.3 | 0.150 | | Tatoeba-test.ton-eng.ton.eng | 39.5 | 0.473 | | Tatoeba-test.tpw-eng.tpw.eng | 1.5 | 0.160 | | Tatoeba-test.tso-eng.tso.eng | 44.7 | 0.526 | | Tatoeba-test.tuk-eng.tuk.eng | 18.6 | 0.401 | | Tatoeba-test.tur-eng.tur.eng | 40.5 | 0.573 | | Tatoeba-test.tvl-eng.tvl.eng | 55.0 | 0.593 | | Tatoeba-test.tyv-eng.tyv.eng | 19.1 | 0.477 | | Tatoeba-test.tzl-eng.tzl.eng | 17.7 | 0.333 | | Tatoeba-test.udm-eng.udm.eng | 3.4 | 0.217 | | Tatoeba-test.uig-eng.uig.eng | 11.4 | 0.289 | | Tatoeba-test.ukr-eng.ukr.eng | 43.1 | 0.595 | | Tatoeba-test.umb-eng.umb.eng | 9.2 | 0.260 | | Tatoeba-test.urd-eng.urd.eng | 23.2 | 0.426 | | Tatoeba-test.uzb-eng.uzb.eng | 19.0 | 0.342 | | Tatoeba-test.vec-eng.vec.eng | 41.1 | 0.409 | | Tatoeba-test.vie-eng.vie.eng | 30.6 | 0.481 | | Tatoeba-test.vol-eng.vol.eng | 1.8 | 0.143 | | Tatoeba-test.war-eng.war.eng | 15.9 | 0.352 | | Tatoeba-test.wln-eng.wln.eng | 12.6 | 0.291 | | Tatoeba-test.wol-eng.wol.eng | 4.4 | 0.138 | | Tatoeba-test.xal-eng.xal.eng | 0.9 | 0.153 | | Tatoeba-test.xho-eng.xho.eng | 35.4 | 0.513 | | Tatoeba-test.yid-eng.yid.eng | 19.4 | 0.387 | | Tatoeba-test.yor-eng.yor.eng | 19.3 | 0.327 | | Tatoeba-test.zho-eng.zho.eng | 25.8 | 0.448 | | Tatoeba-test.zul-eng.zul.eng | 40.9 | 0.567 | | Tatoeba-test.zza-eng.zza.eng | 1.6 | 0.125 | ### System Info: - hf_name: mul-eng - source_languages: mul - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'es', 'os', 'eo', 'ro', 'fy', 'cy', 'is', 'lb', 'su', 'an', 'sq', 'fr', 'ht', 'rm', 'cv', 'ig', 'am', 'eu', 'tr', 'ps', 'af', 'ny', 'ch', 'uk', 'sl', 'lt', 'tk', 'sg', 'ar', 'lg', 'bg', 'be', 'ka', 'gd', 'ja', 'si', 'br', 'mh', 'km', 'th', 'ty', 'rw', 'te', 'mk', 'or', 'wo', 'kl', 'mr', 'ru', 'yo', 'hu', 'fo', 'zh', 'ti', 'co', 'ee', 'oc', 'sn', 'mt', 'ts', 'pl', 'gl', 'nb', 'bn', 'tt', 'bo', 'lo', 'id', 'gn', 'nv', 'hy', 'kn', 'to', 'io', 'so', 'vi', 'da', 'fj', 'gv', 'sm', 'nl', 'mi', 'pt', 'hi', 'se', 'as', 'ta', 'et', 'kw', 'ga', 'sv', 'ln', 'na', 'mn', 'gu', 'wa', 'lv', 'jv', 'el', 'my', 'ba', 'it', 'hr', 'ur', 'ce', 'nn', 'fi', 'mg', 'rn', 'xh', 'ab', 'de', 'cs', 'he', 'zu', 'yi', 'ml', 'mul', 'en'] - src_constituents: {'sjn_Latn', 'cat', 'nan', 'spa', 'ile_Latn', 'pap', 'mwl', 'uzb_Latn', 'mww', 'hil', 'lij', 'avk_Latn', 'lad_Latn', 'lat_Latn', 'bos_Latn', 'oss', 'epo', 'ron', 'fry', 'cym', 'toi_Latn', 'awa', 'swg', 'zsm_Latn', 'zho_Hant', 'gcf_Latn', 'uzb_Cyrl', 'isl', 'lfn_Latn', 'shs_Latn', 'nov_Latn', 'bho', 'ltz', 'lzh', 'kur_Latn', 'sun', 'arg', 'pes_Thaa', 'sqi', 'uig_Arab', 'csb_Latn', 'fra', 'hat', 'liv_Latn', 'non_Latn', 'sco', 'cmn_Hans', 'pnb', 'roh', 'chv', 'ibo', 'bul_Latn', 'amh', 'lfn_Cyrl', 'eus', 'fkv_Latn', 'tur', 'pus', 'afr', 'brx_Latn', 'nya', 'acm', 'ota_Latn', 'cha', 'ukr', 'xal', 'slv', 'lit', 'zho_Hans', 'tmw_Latn', 'kjh', 'ota_Arab', 'war', 'tuk', 'sag', 'myv', 'hsb', 'lzh_Hans', 'ara', 'tly_Latn', 'lug', 'brx', 'bul', 'bel', 'vol_Latn', 'kat', 'gan', 'got_Goth', 'vro', 'ext', 'afh_Latn', 'gla', 'jpn', 'udm', 'mai', 'ary', 'sin', 'tvl', 'hif_Latn', 'cjy_Hant', 'bre', 'ceb', 'mah', 'nob_Hebr', 'crh_Latn', 'prg_Latn', 'khm', 'ang_Latn', 'tha', 'tah', 'tzl', 'aln', 'kin', 'tel', 'ady', 'mkd', 'ori', 'wol', 'aze_Latn', 'jbo', 'niu', 'kal', 'mar', 'vie_Hani', 'arz', 'yue', 'kha', 'san_Deva', 'jbo_Latn', 'gos', 'hau_Latn', 'rus', 'quc', 'cmn', 'yor', 'hun', 'uig_Cyrl', 'fao', 'mnw', 'zho', 'orv_Cyrl', 'iba', 'bel_Latn', 'tir', 'afb', 'crh', 'mic', 'cos', 'swh', 'sah', 'krl', 'ewe', 'apc', 'zza', 'chr', 'grc_Grek', 'tpw_Latn', 'oci', 'mfe', 'sna', 'kir_Cyrl', 'tat_Latn', 'gom', 'ido_Latn', 'sgs', 'pau', 'tgk_Cyrl', 'nog', 'mlt', 'pdc', 'tso', 'srp_Cyrl', 'pol', 'ast', 'glg', 'pms', 'fuc', 'nob', 'qya', 'ben', 'tat', 'kab', 'min', 'srp_Latn', 'wuu', 'dtp', 'jbo_Cyrl', 'tet', 'bod', 'yue_Hans', 'zlm_Latn', 'lao', 'ind', 'grn', 'nav', 'kaz_Cyrl', 'rom', 'hye', 'kan', 'ton', 'ido', 'mhr', 'scn', 'som', 'rif_Latn', 'vie', 'enm_Latn', 'lmo', 'npi', 'pes', 'dan', 'fij', 'ina_Latn', 'cjy_Hans', 'jdt_Cyrl', 'gsw', 'glv', 'khm_Latn', 'smo', 'umb', 'sma', 'gil', 'nld', 'snd_Arab', 'arq', 'mri', 'kur_Arab', 'por', 'hin', 'shy_Latn', 'sme', 'rap', 'tyv', 'dsb', 'moh', 'asm', 'lad', 'yue_Hant', 'kpv', 'tam', 'est', 'frm_Latn', 'hoc_Latn', 'bam_Latn', 'kek_Latn', 'ksh', 'tlh_Latn', 'ltg', 'pan_Guru', 'hnj_Latn', 'cor', 'gle', 'swe', 'lin', 'qya_Latn', 'kum', 'mad', 'cmn_Hant', 'fuv', 'nau', 'mon', 'akl_Latn', 'guj', 'kaz_Latn', 'wln', 'tuk_Latn', 'jav_Java', 'lav', 'jav', 'ell', 'frr', 'mya', 'bak', 'rue', 'ita', 'hrv', 'izh', 'ilo', 'dws_Latn', 'urd', 'stq', 'tat_Arab', 'haw', 'che', 'pag', 'nno', 'fin', 'mlg', 'ppl_Latn', 'run', 'xho', 'abk', 'deu', 'hoc', 'lkt', 'lld_Latn', 'tzl_Latn', 'mdf', 'ike_Latn', 'ces', 'ldn_Latn', 'egl', 'heb', 'vec', 'zul', 'max_Latn', 'pes_Latn', 'yid', 'mal', 'nds'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt - src_alpha3: mul - tgt_alpha3: eng - short_pair: mul-en - chrF2_score: 0.518 - bleu: 34.7 - brevity_penalty: 1.0 - ref_len: 72346.0 - src_name: Multiple languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: mul - tgt_alpha2: en - prefer_old: False - long_pair: mul-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

cerspense/zeroscope_v2_576w

cerspense

diffusers

text-to-video

--- pipeline_tag: text-to-video license: cc-by-nc-4.0 ---  # zeroscope_v2 576w A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained from the [original weights](https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis) using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br /> zeroscope_v2_567w is specifically designed for upscaling with [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) using vid2vid in the [1111 text2video](https://github.com/kabachuha/sd-webui-text2video) extension by [kabachuha](https://github.com/kabachuha). Leveraging this model as a preliminary step allows for superior overall compositions at higher resolutions in zeroscope_v2_XL, permitting faster exploration in 576x320 before transitioning to a high-resolution render. See some [example outputs](https://www.youtube.com/watch?v=HO3APT_0UA4) that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of [dotsimulate](https://www.instagram.com/dotsimulate/))<br /> zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320 ### Using it with the 1111 text2video extension 1. Download files in the zs2_576w folder. 2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory. ### Upscaling recommendations For upscaling, it's recommended to use [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) via vid2vid in the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip. <br /> ### Usage in 🧨 Diffusers Let's first install the libraries required: ```bash $ pip install diffusers transformers accelerate torch ``` Now, generate a video: ```py import torch from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler from diffusers.utils import export_to_video pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() prompt = "Darth Vader is surfing on waves" video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames video_path = export_to_video(video_frames) ``` Here are some results: <table> <tr> Darth vader is surfing on waves. <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif" alt="Darth vader surfing in waves." style="width: 576;" /> </center></td> </tr> </table> ### Known issues Lower resolutions or fewer frames could lead to suboptimal output. <br /> Thanks to [camenduru](https://github.com/camenduru), [kabachuha](https://github.com/kabachuha), [ExponentialML](https://github.com/ExponentialML), [dotsimulate](https://www.instagram.com/dotsimulate/), [VANYA](https://twitter.com/veryVANYA), [polyware](https://twitter.com/polyware_ai), [tin2tin](https://github.com/tin2tin)<br />

ckiplab/bert-base-chinese

ckiplab

transformers

fill-mask

--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - lm-head - 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') ``` 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 。

dccuchile/bert-base-spanish-wwm-uncased

dccuchile

transformers

fill-mask

--- language: - es tags: - masked-lm --- # BETO: Spanish BERT BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) as well as other (not BERT-based) models. ## Download | | | | | |-|:--------:|:-----:|:----:| |BETO uncased|[tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/pytorch_weights.tar.gz) | [vocab](./config/uncased_2M/vocab.txt), [config](./config/uncased_2M/config.json) | |BETO cased| [tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/pytorch_weights.tar.gz) | [vocab](./config/cased_2M/vocab.txt), [config](./config/cased_2M/config.json) | All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. ## Benchmarks The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found [here](#references). |Task | BETO-cased | BETO-uncased | Best Multilingual BERT | Other results | |-------|--------------:|--------------:|--------------------------:|-------------------------------:| |[POS](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1827) | **98.97** | 98.44 | 97.10 [2] | 98.91 [6], 96.71 [3] | |[NER-C](https://www.kaggle.com/nltkdata/conll-corpora) | [**88.43**](https://github.com/gchaperon/beto-benchmarks/blob/master/conll2002/dev_results_beto-cased_conll2002.txt) | 82.67 | 87.38 [2] | 87.18 [3] | |[MLDoc](https://github.com/facebookresearch/MLDoc) | [95.60](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-cased_mldoc.txt) | [**96.12**](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-uncased_mldoc.txt) | 95.70 [2] | 88.75 [4] | |[PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) | 89.05 | 89.55 | 90.70 [8] | |[XNLI](https://github.com/facebookresearch/XNLI) | **82.01** | 80.15 | 78.50 [2] | 80.80 [5], 77.80 [1], 73.15 [4]| ## Example of use For further details on how to use BETO you can visit the [🤗Huggingface Transformers library](https://github.com/huggingface/transformers), starting by the [Quickstart section](https://huggingface.co/transformers/quickstart.html). BETO models can be accessed simply as [`'dccuchile/bert-base-spanish-wwm-cased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) and [`'dccuchile/bert-base-spanish-wwm-uncased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) by using the Transformers library. An example on how to download and use the models in this page can be found in [this colab notebook](https://colab.research.google.com/drive/1uRwg4UmPgYIqGYY4gW_Nsw9782GFJbPt). (We will soon add a more detailed step-by-step tutorial in Spanish for newcommers 😉) ## Acknowledgments We thank [Adereso](https://www.adere.so/) for kindly providing support for traininig BETO-uncased, and the [Millennium Institute for Foundational Research on Data](https://imfd.cl/en/) that provided support for training BETO-cased. Also thanks to Google for helping us with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program. ## Citation [Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf) To cite this resource in a publication please use the following: ``` @inproceedings{CaneteCFP2020, title={Spanish Pre-Trained BERT Model and Evaluation Data}, author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge}, booktitle={PML4DC at ICLR 2020}, year={2020} } ``` ## License Disclaimer The license CC BY 4.0 best describes our intentions for our work. However we are not sure that all the datasets used to train BETO have licenses compatible with CC BY 4.0 (specially for commercial use). Please use at your own discretion and verify that the licenses of the original text resources match your needs. ## References * [1] [Original Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) * [2] [Multilingual BERT on "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT"](https://arxiv.org/pdf/1904.09077.pdf) * [3] [Multilingual BERT on "How Multilingual is Multilingual BERT?"](https://arxiv.org/pdf/1906.01502.pdf) * [4] [LASER](https://arxiv.org/abs/1812.10464) * [5] [XLM (MLM+TLM)](https://arxiv.org/pdf/1901.07291.pdf) * [6] [UDPipe on "75 Languages, 1 Model: Parsing Universal Dependencies Universally"](https://arxiv.org/pdf/1904.02099.pdf) * [7] [Multilingual BERT on "Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation"](https://arxiv.org/pdf/1906.01569.pdf) * [8] [Multilingual BERT on "PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification"](https://arxiv.org/abs/1908.11828)

google/mobilenet_v1_0.75_192

google

transformers

image-classification

--- license: other tags: - vision - image-classification datasets: - imagenet-1k 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 --- # MobileNet V1 MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md). Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v1) 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, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v1_0.75_192") model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v1_0.75_192") inputs = preprocessor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch.

facebook/wmt19-en-de

facebook

transformers

translation

--- language: - en - de tags: - translation - wmt19 - facebook license: apache-2.0 datasets: - wmt19 metrics: - bleu thumbnail: https://huggingface.co/front/thumbnails/facebook.png --- # FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-de. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-en-de" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Maschinelles Lernen ist großartig, oder? ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- en-de | [43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862) | 42.83 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020}, title={Facebook FAIR's WMT19 News Translation Task Submission}, author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, booktitle={Proc. of WMT}, } ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints)

lmsys/vicuna-7b-v1.3

lmsys

transformers

text-generation

--- inference: false --- **NOTE: New version available** Please check out a newer version of the weights [here](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). <br> # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

kresnik/wav2vec2-large-xlsr-korean

kresnik

transformers

automatic-speech-recognition

--- language: ko datasets: - kresnik/zeroth_korean tags: - speech - audio - automatic-speech-recognition license: apache-2.0 model-index: - name: 'Wav2Vec2 XLSR Korean' results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Zeroth Korean type: kresnik/zeroth_korean args: clean metrics: - name: Test WER type: wer value: 4.74 - name: Test CER type: cer value: 1.78 --- ## Evaluation on Zeroth-Korean ASR corpus [Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb) ``` from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import soundfile as sf import torch from jiwer import wer processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean") model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda') ds = load_dataset("kresnik/zeroth_korean", "clean") test_ds = ds['test'] def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch test_ds = test_ds.map(map_to_array) def map_to_pred(batch): inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` ### Expected WER: 4.74% ### Expected CER: 1.78%

plasmo/vox2

plasmo

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion widget: - text: "voxel-ish " --- ### Jak's Voxel-ish Image Pack v.1.2 for Stable Diffusion VERSION 1.2 of Voxel-ish Image Pack brought to you by 184 training images through 8000 training steps, 20% Training text crafted by Jak_TheAI_Artist version history: v1.2 - Fine tuned for better faces. Include Prompt trigger: "voxel-ish" to activate. Tip: add "intricate detail" in prompt to make a semi-realistic image. Sample pictures of this concept: voxel-ish            

Helsinki-NLP/opus-mt-en-zh

Helsinki-NLP

transformers

translation

--- language: - en - zh tags: - translation license: apache-2.0 --- ### eng-zho * source group: English * target group: Chinese * OPUS readme: [eng-zho](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md) * model: transformer * source language(s): eng * target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zho/opus-2020-07-17.zip) * test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zho/opus-2020-07-17.test.txt) * test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zho/opus-2020-07-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng.zho | 31.4 | 0.268 | ### System Info: - hf_name: eng-zho - source_languages: eng - target_languages: zho - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'zh'] - src_constituents: {'eng'} - tgt_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zho/opus-2020-07-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zho/opus-2020-07-17.test.txt - src_alpha3: eng - tgt_alpha3: zho - short_pair: en-zh - chrF2_score: 0.268 - bleu: 31.4 - brevity_penalty: 0.8959999999999999 - ref_len: 110468.0 - src_name: English - tgt_name: Chinese - train_date: 2020-07-17 - src_alpha2: en - tgt_alpha2: zh - prefer_old: False - long_pair: eng-zho - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

facebook/sam-vit-large

facebook

transformers

--- license: apache-2.0 --- # Model Card for Segment Anything Model (SAM) - ViT Large (ViT-L) version <p> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-architecture.png" alt="Model architecture"> <em> Detailed architecture of Segment Anything Model (SAM).</em> </p> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Citation](#citation) # TL;DR [Link to original repository](https://github.com/facebookresearch/segment-anything) | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-beancans.png" alt="Snow" width="600" height="600"> | <img src="https://huggingface.co/facebook/sam-vit-huge/discussions/7" alt="Forest" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car-seg.png" alt="Mountains" width="600" height="600"> | |---------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------| The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. The abstract of the paper states: > We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything). # Model Details The SAM model is made up of 3 modules: - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used. - The `PromptEncoder`: generates embeddings for points and bounding boxes - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`. # Usage ## Prompted-Mask-Generation ```python from PIL import Image import requests from transformers import SamModel, SamProcessor model = SamModel.from_pretrained("facebook/sam-vit-large") processor = SamProcessor.from_pretrained("facebook/sam-vit-large") img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") input_points = [[[450, 600]]] # 2D localization of a window ``` ```python inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda") outputs = model(**inputs) masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()) scores = outputs.iou_scores ``` Among other arguments to generate masks, you can pass 2D locations on the approximate position of your object of interest, a bounding box wrapping the object of interest (the format should be x, y coordinate of the top right and bottom left point of the bounding box), a segmentation mask. At this time of writing, passing a text as input is not supported by the official model according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844). For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example! ## Automatic-Mask-Generation The model can be used for generating segmentation masks in a "zero-shot" fashion, given an input image. The model is automatically prompt with a grid of `1024` points which are all fed to the model. The pipeline is made for automatic mask generation. The following snippet demonstrates how easy you can run it (on any device! Simply feed the appropriate `points_per_batch` argument) ```python from transformers import pipeline generator = pipeline("mask-generation", device = 0, points_per_batch = 256) image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" outputs = generator(image_url, points_per_batch = 256) ``` Now to display the image: ```python import matplotlib.pyplot as plt from PIL import Image import numpy as np def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) plt.imshow(np.array(raw_image)) ax = plt.gca() for mask in outputs["masks"]: show_mask(mask, ax=ax, random_color=True) plt.axis("off") plt.show() ``` # Citation If you use this model, please use the following BibTeX entry. ``` @article{kirillov2023segany, title={Segment Anything}, author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, journal={arXiv:2304.02643}, year={2023} } ```

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" ```

Open-Orca/Mistral-7B-OpenOrca

Open-Orca

transformers

text-generation

--- datasets: - Open-Orca/OpenOrca language: - en library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- <p><h1>🐋 Mistral-7B-OpenOrca 🐋</h1></p>  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # OpenOrca - Mistral - 7B - 8k We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune on top of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707). We use [OpenChat](https://huggingface.co/openchat) packing, trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). This release is trained on a curated filtered subset of most of our GPT-4 augmented data. It is the same subset of our data as was used in our [OpenOrcaxOpenChat-Preview2-13B model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B). **HF Leaderboard evals place this model as #1 for all models smaller than 30B at release time, outperforming all other 7B and 13B models!** This release provides a first: a fully open model with class-breaking performance, capable of running fully accelerated on even moderate consumer GPUs. Our thanks to the Mistral team for leading the way here. We affectionately codename this model: "*MistralOrca*" If you'd like to try the model now, we have it running on fast GPUs unquantized: https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners. We will also give sneak-peak announcements on our Discord, which you can find here: https://AlignmentLab.ai or check the OpenAccess AI Collective Discord for more information about Axolotl trainer here: https://discord.gg/5y8STgB3P3 # Quantized Models Quantized versions of this model are generously made available by [TheBloke](https://huggingface.co/TheBloke). - AWQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ - GPTQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ - GGUF: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF # Prompt Template We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this. This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML. This formatting is also available via a pre-defined [Transformers chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means that lists of messages can be formatted for you with the `apply_chat_template()` method: ```python chat = [ {"role": "system", "content": "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!"} {"role": "user", "content": "How are you?"}, {"role": "assistant", "content": "I am doing well!"}, {"role": "user", "content": "Please tell me about how mistral winds have attracted super-orcas."}, ] tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` which will yield: ``` <|im_start|>system You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers! <|im_end|> <|im_start|>user How are you?<|im_end|> <|im_start|>assistant I am doing well!<|im_end|> <|im_start|>user Please tell me about how mistral winds have attracted super-orcas.<|im_end|> <|im_start|>assistant ``` If you use `tokenize=True` and `return_tensors="pt"` instead, then you will get a tokenized and formatted conversation ready to pass to `model.generate()`. # Inference See [this notebook](https://colab.research.google.com/drive/1yZlLSifCGELAX5GN582kZypHCv0uJuNX?usp=sharing) for inference details. Note that you need the development snapshot of Transformers currently, as support for Mistral hasn't been released into PyPI yet: ``` pip install git+https://github.com/huggingface/transformers ``` # Evaluation ## HuggingFace Leaderboard Performance We have evaluated using the methodology and tools for the HuggingFace Leaderboard, and find that we have dramatically improved upon the base model. We find **106%** of the base model's performance on HF Leaderboard evals, averaging **65.84**. At release time, this beats all 7B and 13B models! This is also **98.6%** of *`Llama2-70b-chat`*'s performance!  | Metric | Value | |-----------------------|-------| | MMLU (5-shot) | 62.24 | | ARC (25-shot) | 64.08 | | HellaSwag (10-shot) | 83.99 | | TruthfulQA (0-shot) | 53.05 | | Avg. | 65.84 | We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. ## AGIEval Performance We compare our results to the base Mistral-7B model (using LM Evaluation Harness). We find **129%** of the base model's performance on AGI Eval, averaging **0.397**. As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.  ## BigBench-Hard Performance We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.  ## GPT4ALL Leaderboard Performance We gain a slight edge over our previous releases, again topping the leaderboard, averaging **72.38**.  ## MT-Bench Performance MT-Bench uses GPT-4 as a judge of model response quality, across a wide range of challenges. We find our performance is *on-par with `Llama2-70b-chat`*, averaging **6.86**.  # Dataset We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset. # Training We trained with 8x A6000 GPUs for 62 hours, completing 4 epochs of full fine tuning on our dataset in one training run. Commodity cost was ~$400. # Citation ```bibtex @software{lian2023mistralorca1 title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset}, author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca}, } @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

vinai/phobert-base-v2

vinai

transformers

fill-mask

#### Table of contents 1. [Introduction](#introduction) 2. [Using PhoBERT with `transformers`](#transformers) - [Installation](#install2) - [Pre-trained models](#models2) - [Example usage](#usage2) 3. [Using PhoBERT with `fairseq`](#fairseq) 4. [Notes](#vncorenlp) # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance. - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. The general architecture and experimental results of PhoBERT can be found in our [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.92/): @inproceedings{phobert, title = {{PhoBERT: Pre-trained language models for Vietnamese}}, author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, year = {2020}, pages = {1037--1042} } **Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software. ## <a name="transformers"></a> Using PhoBERT with `transformers` ### Installation <a name="install2"></a> - Install `transformers` with pip: `pip install transformers`, or [install `transformers` from source](https://huggingface.co/docs/transformers/installation#installing-from-source). <br /> Note that we merged a slow tokenizer for PhoBERT into the main `transformers` branch. The process of merging a fast tokenizer for PhoBERT is in the discussion, as mentioned in [this pull request](https://github.com/huggingface/transformers/pull/17254#issuecomment-1133932067). If users would like to utilize the fast tokenizer, the users might install `transformers` as follows: ``` git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git cd transformers pip3 install -e . ``` - Install `tokenizers` with pip: `pip3 install tokenizers` ### Pre-trained models <a name="models2"></a> Model | #params | Arch. | Max length | Pre-training data ---|---|---|---|--- `vinai/phobert-base` | 135M | base | 256 | 20GB of Wikipedia and News texts `vinai/phobert-large` | 370M | large | 256 | 20GB of Wikipedia and News texts `vinai/phobert-base-v2` | 135M | base | 256 | 20GB of Wikipedia and News texts + 120GB of texts from OSCAR-2301 ### Example usage <a name="usage2"></a> ```python import torch from transformers import AutoModel, AutoTokenizer phobert = AutoModel.from_pretrained("vinai/phobert-base-v2") tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2") # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED! sentence = 'Chúng_tôi là những nghiên_cứu_viên .' input_ids = torch.tensor([tokenizer.encode(sentence)]) with torch.no_grad(): features = phobert(input_ids) # Models outputs are now tuples ## With TensorFlow 2.0+: # from transformers import TFAutoModel # phobert = TFAutoModel.from_pretrained("vinai/phobert-base") ``` ## <a name="fairseq"></a> Using PhoBERT with `fairseq` Please see details at [HERE](https://github.com/VinAIResearch/PhoBERT/blob/master/README_fairseq.md)! ## <a name="vncorenlp"></a> Notes In case the input texts are `raw`, i.e. without word segmentation, a word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT. As PhoBERT employed the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data (including [Vietnamese tone normalization](https://github.com/VinAIResearch/BARTpho/blob/main/VietnameseToneNormalization.md) and word and sentence segmentation), it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts. #### Installation pip install py_vncorenlp #### Example usage <a name="example"></a> ```python import py_vncorenlp # Automatically download VnCoreNLP components from the original repository # and save them in some local machine folder py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp') # Load the word and sentence segmentation component rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp') text = "Ông Nguyễn Khắc Chúc đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây." output = rdrsegmenter.word_segment(text) print(output) # ['Ông Nguyễn_Khắc_Chúc đang làm_việc tại Đại_học Quốc_gia Hà_Nội .', 'Bà Lan , vợ ông Chúc , cũng làm_việc tại đây .'] ``` ## License Copyright (c) 2023 VinAI Research This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

dbmdz/distilbert-base-turkish-cased

dbmdz

transformers

--- language: tr license: mit --- # 🤗 + 📚 dbmdz Distilled Turkish BERT model In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a (cased) distilled model for Turkish 🎉 # 🇹🇷 DistilBERTurk DistilBERTurk is a community-driven cased distilled BERT model for Turkish. DistilBERTurk was trained on 7GB of the original training data that was used for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats), using the cased version of BERTurk as teacher model. *DistilBERTurk* was trained with the official Hugging Face implementation from [here](https://github.com/huggingface/transformers/tree/master/examples/distillation) for 5 days on 4 RTX 2080 TI. More details about distillation can be found in the ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) paper by Sanh et al. (2019). ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository! | Model | Downloads | --------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/distilbert-base-turkish-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/vocab.txt) ## Usage With Transformers >= 2.3 our DistilBERTurk model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased") model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased") ``` ## Results For results on PoS tagging or NER tasks, please refer to [this repository](https://github.com/stefan-it/turkish-bert). For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model. The overall performance difference between DistilBERTurk and the original (teacher) BERTurk model is ~1.18%. # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗

sentence-transformers/bert-base-nli-mean-tokens

sentence-transformers

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/bert-base-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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/bert-base-nli-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```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) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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/bert-base-nli-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (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}) ) ``` ## 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", } ```

allenai/unifiedqa-v2-t5-large-1363200

allenai

transformers

text2text-generation

--- language: en --- # Further details: https://github.com/allenai/unifiedqa

google/mt5-small

google

transformers

text2text-generation

--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's mT5](https://github.com/google-research/multilingual-t5) mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5) Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* ## Abstract The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.

juierror/flan-t5-text2sql-with-schema

juierror

transformers

text2text-generation

--- language: en datasets: - wikisql widget: - text: 'question: get people name with age equal 25 table: id, name, age' license: apache-2.0 --- There are an upgraded version that support multiple tables and support "<" sign [here](https://huggingface.co/juierror/flan-t5-text2sql-with-schema-v2). # How to use ```python from typing import List from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema") model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema") def prepare_input(question: str, table: List[str]): table_prefix = "table:" question_prefix = "question:" join_table = ",".join(table) inputs = f"{question_prefix} {question} {table_prefix} {join_table}" input_ids = tokenizer(inputs, max_length=512, return_tensors="pt").input_ids return input_ids def inference(question: str, table: List[str]) -> str: input_data = prepare_input(question=question, table=table) input_data = input_data.to(model.device) outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700) result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True) return result print(inference(question="get people name with age equal 25", table=["id", "name", "age"])) ```

flair/ner-english-fast

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2003 widget: - text: "George Washington went to Washington" --- ## English NER in Flair (fast model) This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **92,92** (corrected CoNLL-03) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | PER | person name | | LOC | location name | | ORG | organization name | | MISC | other name | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-english-fast") # make example sentence sentence = Sentence("George Washington went to Washington") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [1,2]: "George Washington" [− Labels: PER (0.9515)] Span [5]: "Washington" [− Labels: LOC (0.992)] ``` So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_03 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_03() # 2. what tag do we want to predict? tag_type = 'ner' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # GloVe embeddings WordEmbeddings('glove'), # contextual string embeddings, forward FlairEmbeddings('news-forward-fast'), # contextual string embeddings, backward FlairEmbeddings('news-backward-fast'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

kandinsky-community/kandinsky-2-2-prior

kandinsky-community

diffusers

text-to-image

--- license: apache-2.0 tags: - text-to-image - kandinsky inference: false --- # Kandinsky 2.2 Kandinsky inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) ## Usage Kandinsky 2.2 is available in diffusers! ```python pip install diffusers transformers accelerate ``` ### Text to image ```python from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "portrait of a young women, blue eyes, cinematic" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0] image.save("portrait.png") ```  ### Text Guided Image-to-Image Generation ```python from PIL import Image import requests from io import BytesIO url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) original_image = Image.open(BytesIO(response.content)).convert("RGB") original_image = original_image.resize((768, 512)) ```  ```python from diffusers import AutoPipelineForImage2Image import torch pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, image=original_image, strength=0.3, height=768, width=768).images[0] out.images[0].save("fantasy_land.png") ```  ### Interpolate ```python from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline from diffusers.utils import load_image import PIL import torch pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior.to("cuda") img1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) img2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg" ) # add all the conditions we want to interpolate, can be either text or image images_texts = ["a cat", img1, img2] # specify the weights for each condition in images_texts weights = [0.3, 0.3, 0.4] # We can leave the prompt empty prompt = "" prior_out = pipe_prior.interpolate(images_texts, weights) pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.to("cuda") image = pipe(**prior_out, height=768, width=768).images[0] image.save("starry_cat.png") ```  ### Text Guided Inpainting Generation ```python from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch import numpy as np pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "a hat" init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) mask = np.zeros((768, 768), dtype=np.float32) # Let's mask out an area above the cat's head mask[:250, 250:-250] = 1 out = pipe( prompt=prompt, image=init_image, mask_image=mask, height=768, width=768, num_inference_steps=150, ) image = out.images[0] image.save("cat_with_hat.png") ```  __<font color=red>Breaking change on the mask input:</font>__ We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. We have changed to use white pixels to represent masks instead in order to have a unified mask format across all our pipelines. Please upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to: ```python # For PIL input import PIL.ImageOps mask = PIL.ImageOps.invert(mask) # For PyTorch and Numpy input mask = 1 - mask ``` ### Text-to-Image Generation with ControlNet Conditioning ```python import torch import numpy as np from transformers import pipeline from diffusers.utils import load_image from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline # let's take an image and extract its depth map. def make_hint(image, depth_estimator): image = depth_estimator(image)["depth"] image = np.array(image) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) detected_map = torch.from_numpy(image).float() / 255.0 hint = detected_map.permute(2, 0, 1) return hint img = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png" ).resize((768, 768)) # We can use the `depth-estimation` pipeline from transformers to process the image and retrieve its depth map. depth_estimator = pipeline("depth-estimation") hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda") # Now, we load the prior pipeline and the text-to-image controlnet pipeline pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior = pipe_prior.to("cuda") pipe = KandinskyV22ControlnetPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") # We pass the prompt and negative prompt through the prior to generate image embeddings prompt = "A robot, 4k photo" negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature" generator = torch.Generator(device="cuda").manual_seed(43) image_emb, zero_image_emb = pipe_prior( prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator ).to_tuple() # Now we can pass the image embeddings and the depth image we extracted to the controlnet pipeline. With Kandinsky 2.2, only prior pipelines accept `prompt` input. You do not need to pass the prompt to the controlnet pipeline. images = pipe( image_embeds=image_emb, negative_image_embeds=zero_image_emb, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768, ).images images[0].save("robot_cat.png") ```   ### Image-to-Image Generation with ControlNet Conditioning ```python import torch import numpy as np from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline from diffusers.utils import load_image from transformers import pipeline img = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinskyv22/cat.png" ).resize((768, 768)) def make_hint(image, depth_estimator): image = depth_estimator(image)["depth"] image = np.array(image) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) detected_map = torch.from_numpy(image).float() / 255.0 hint = detected_map.permute(2, 0, 1) return hint depth_estimator = pipeline("depth-estimation") hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda") pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior = pipe_prior.to("cuda") pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") prompt = "A robot, 4k photo" negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature" generator = torch.Generator(device="cuda").manual_seed(43) # run prior pipeline img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator) negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator) # run controlnet img2img pipeline images = pipe( image=img, strength=0.5, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768, ).images images[0].save("robot_cat.png") ``` Here is the output. Compared with the output from our text-to-image controlnet example, it kept a lot more cat facial details from the original image and worked into the robot style we asked for.  ## Model Architecture ### Overview Kandinsky 2.2 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. <p float="left"> <img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/> </p> Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [CLIP-ViT-G model](https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K). The trained image prior model is then used to generate CLIP image embeddings for input text prompts. Both the input text prompts and its CLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image. ### Details The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution). The main Text2Image diffusion model was trained on [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) and then fine-tuned with a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources. The main change in Kandinsky 2.2 is the replacement of CLIP-ViT-G. Its image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing its overall performance. Due to the switch CLIP model, the image prior model was retrained, and the Text2Image diffusion model was fine-tuned for 2000 iterations. Kandinsky 2.2 was trained on data of various resolutions, from 512 x 512 to 1536 x 1536, and also as different aspect ratios. As a result, Kandinsky 2.2 can generate 1024 x 1024 outputs with any aspect ratio. ### Evaluation We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID. FID metric values ​​for generative models on COCO_30k | | FID (30k)| |:------|----:| | eDiff-I (2022) | 6.95 | | Image (2022) | 7.27 | | Kandinsky 2.1 (2023) | 8.21| | Stable Diffusion 2.1 (2022) | 8.59 | | GigaGAN, 512x512 (2023) | 9.09 | | DALL-E 2 (2022) | 10.39 | | GLIDE (2022) | 12.24 | | Kandinsky 1.0 (2022) | 15.40 | | DALL-E (2021) | 17.89 | | Kandinsky 2.0 (2022) | 20.00 | | GLIGEN (2022) | 21.04 | For more information, please refer to the upcoming technical report. ## BibTex If you find this repository useful in your research, please cite: ``` @misc{kandinsky 2.2, title = {kandinsky 2.2}, author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov}, year = {2023}, howpublished = {}, } ```

facebook/hubert-large-ll60k

facebook

transformers

feature-extraction

--- language: en datasets: - libri-light tags: - speech license: apache-2.0 --- # Hubert-Large [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. The model was pretrained on [Libri-Light](https://github.com/facebookresearch/libri-light). [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 See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `HubertForCTC`.

cross-encoder/stsb-roberta-large

cross-encoder

transformers

text-classification

--- license: apache-2.0 --- # Cross-Encoder for Quora Duplicate Questions Detection This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. ## Training Data This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

microsoft/dit-base-finetuned-rvlcdip

microsoft

transformers

image-classification

--- tags: - dit - vision - image-classification datasets: - rvl_cdip widget: - src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/coca_cola_advertisement.png example_title: Advertisement - src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/scientific_publication.png example_title: Scientific publication --- # Document Image Transformer (base-sized model) Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/dit). Note that DiT is identical to the architecture of [BEiT](https://huggingface.co/docs/transformers/model_doc/beit). Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled document images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. ## Intended uses & limitations You can use the raw model for encoding document images into a vector space, but it's mostly meant to be fine-tuned on tasks like document image classification, table detection or document layout analysis. See the [model hub](https://huggingface.co/models?search=microsoft/dit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification import torch from PIL import Image image = Image.open('path_to_your_document_image').convert('RGB') processor = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 16 RVL-CDIP classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` ### BibTeX entry and citation info ```bibtex @article{Lewis2006BuildingAT, title={Building a test collection for complex document information processing}, author={David D. Lewis and Gady Agam and Shlomo Engelson Argamon and Ophir Frieder and David A. Grossman and Jefferson Heard}, journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, year={2006} } ```

timm/ViT-SO400M-14-SigLIP-384

timm

open_clip

zero-shot-image-classification

--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-SO400M-14-SigLIP-384 A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. - **Original:** https://github.com/google-research/big_vision - **Dataset:** WebLI - **Papers:** - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ```python import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384') tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP-384') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_so400m_patch14_siglip_384', pretrained=True, num_classes=0, ) 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(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```

EleutherAI/pythia-70m-deduped

EleutherAI

transformers

text-generation

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> **This is the current release.** Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-70M-deduped ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10^-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10^-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10^-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10^-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10^-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10^-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10^-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10^-4 | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-70M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-70M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-70M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-70M-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-70M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-70M-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-70M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data Pythia-70M-deduped was trained on the Pile **after the dataset has been globally deduplicated**.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

bert-large-cased

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (cased) 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 cased: it makes 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-cased') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] Hello I'm a male model. [SEP]", "score":0.22748498618602753, "token":2581, "token_str":"male" }, { "sequence":"[CLS] Hello I'm a fashion model. [SEP]", "score":0.09146175533533096, "token":4633, "token_str":"fashion" }, { "sequence":"[CLS] Hello I'm a new model. [SEP]", "score":0.05823173746466637, "token":1207, "token_str":"new" }, { "sequence":"[CLS] Hello I'm a super model. [SEP]", "score":0.04488750174641609, "token":7688, "token_str":"super" }, { "sequence":"[CLS] Hello I'm a famous model. [SEP]", "score":0.03271442651748657, "token":2505, "token_str":"famous" } ] ``` 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-cased') model = BertModel.from_pretrained("bert-large-cased") 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-cased') model = TFBertModel.from_pretrained("bert-large-cased") 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-cased') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] The man worked as a doctor. [SEP]", "score":0.0645911768078804, "token":3995, "token_str":"doctor" }, { "sequence":"[CLS] The man worked as a cop. [SEP]", "score":0.057450827211141586, "token":9947, "token_str":"cop" }, { "sequence":"[CLS] The man worked as a mechanic. [SEP]", "score":0.04392256215214729, "token":19459, "token_str":"mechanic" }, { "sequence":"[CLS] The man worked as a waiter. [SEP]", "score":0.03755280375480652, "token":17989, "token_str":"waiter" }, { "sequence":"[CLS] The man worked as a teacher. [SEP]", "score":0.03458863124251366, "token":3218, "token_str":"teacher" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] The woman worked as a nurse. [SEP]", "score":0.2572779953479767, "token":7439, "token_str":"nurse" }, { "sequence":"[CLS] The woman worked as a waitress. [SEP]", "score":0.16706500947475433, "token":15098, "token_str":"waitress" }, { "sequence":"[CLS] The woman worked as a teacher. [SEP]", "score":0.04587847739458084, "token":3218, "token_str":"teacher" }, { "sequence":"[CLS] The woman worked as a secretary. [SEP]", "score":0.03577028587460518, "token":4848, "token_str":"secretary" }, { "sequence":"[CLS] The woman worked as a maid. [SEP]", "score":0.03298963978886604, "token":13487, "token_str":"maid" } ] ``` 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, Cased (Original) | 91.5/84.8 | 86.09 ### 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} } ```

cl-tohoku/bert-base-japanese-char

cl-tohoku

transformers

fill-mask

--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia widget: - text: 仙台は「[MASK]の都」と呼ばれている。 --- # BERT base Japanese (character tokenization) 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 character-level 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 characters. The vocabulary size is 4000. ## 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.

persiannlp/mt5-small-parsinlu-opus-translation_fa_en

persiannlp

transformers

text2text-generation

--- language: - fa - multilingual thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg tags: - machine-translation - mt5 - persian - farsi license: cc-by-nc-sa-4.0 datasets: - parsinlu metrics: - sacrebleu --- # Machine Translation (ترجمه‌ی ماشینی) This is an mT5-based model for machine translation (Persian -> English). Here is an example of how you can run this model: ```python from transformers import MT5ForConditionalGeneration, MT5Tokenizer model_size = "small" model_name = f"persiannlp/mt5-{model_size}-parsinlu-opus-translation_fa_en" tokenizer = MT5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, **generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, **generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("ستایش خدای را که پروردگار جهانیان است.") run_model("در هاید پارک کرنر بر گلدانی ایستاده موعظه می‌کند؛") run_model("وی از تمامی بلاگرها، سازمان‌ها و افرادی که از وی پشتیبانی کرده‌اند، تشکر کرد.") run_model("مشابه سال ۲۰۰۱، تولید آمونیاک بی آب در ایالات متحده در سال ۲۰۰۰ تقریباً ۱۷،۴۰۰،۰۰۰ تن (معادل بدون آب) با مصرف ظاهری ۲۲،۰۰۰،۰۰۰ تن و حدود ۴۶۰۰۰۰۰ با واردات خالص مواجه شد. ") run_model("می خواهم دکترای علوم کامپیوتر راجع به شبکه های اجتماعی را دنبال کنم، چالش حل نشده در شبکه های اجتماعی چیست؟") ``` For more details, visit this page: https://github.com/persiannlp/parsinlu/

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

meta-llama

transformers

text-generation

--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**" extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **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 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)|

setu4993/smaller-LaBSE

setu4993

transformers

sentence-similarity

--- pipeline_tag: sentence-similarity language: - ar - de - en - es - fr - it - ja - ko - nl - pl - pt - ru - th - tr - zh tags: - bert - sentence_embedding - multilingual - google - sentence-similarity - labse license: apache-2.0 datasets: - CommonCrawl - Wikipedia --- # LaBSE ## Model description Smaller Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model distilled from the [original LaBSE model](https://huggingface.co/setu4993/LaBSE) to 15 languages (from the original 109 languages) using the techniques described in the paper ['Load What You Need: Smaller Versions of Multilingual BERT'](https://arxiv.org/abs/2010.05609) by [Ukjae Jeong](https://github.com/jeongukjae/). - Model: [HuggingFace's model hub](https://huggingface.co/setu4993/smaller-LaBSE). - Original model: [TensorFlow Hub](https://tfhub.dev/jeongukjae/smaller_LaBSE_15lang/1). - Distillation source: [GitHub](https://github.com/jeongukjae/smaller-labse). - Conversion from TensorFlow to PyTorch: [GitHub](https://github.com/setu4993/convert-labse-tf-pt). ## Usage Using the model: ```python import torch from transformers import BertModel, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("setu4993/smaller-LaBSE") model = BertModel.from_pretrained("setu4993/smaller-LaBSE") model = model.eval() english_sentences = [ "dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog.", ] english_inputs = tokenizer(english_sentences, return_tensors="pt", padding=True) with torch.no_grad(): english_outputs = model(**english_inputs) ``` To get the sentence embeddings, use the pooler output: ```python english_embeddings = english_outputs.pooler_output ``` Output for other languages: ```python italian_sentences = [ "cane", "I cuccioli sono carini.", "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.", ] japanese_sentences = ["犬", "子犬はいいです", "私は犬と一緒にビーチを散歩するのが好きです"] italian_inputs = tokenizer(italian_sentences, return_tensors="pt", padding=True) japanese_inputs = tokenizer(japanese_sentences, return_tensors="pt", padding=True) with torch.no_grad(): italian_outputs = model(**italian_inputs) japanese_outputs = model(**japanese_inputs) italian_embeddings = italian_outputs.pooler_output japanese_embeddings = japanese_outputs.pooler_output ``` For similarity between sentences, an L2-norm is recommended before calculating the similarity: ```python import torch.nn.functional as F def similarity(embeddings_1, embeddings_2): normalized_embeddings_1 = F.normalize(embeddings_1, p=2) normalized_embeddings_2 = F.normalize(embeddings_2, p=2) return torch.matmul( normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1) ) print(similarity(english_embeddings, italian_embeddings)) print(similarity(english_embeddings, japanese_embeddings)) print(similarity(italian_embeddings, japanese_embeddings)) ``` ## Details Details about data, training, evaluation and performance metrics are available in the [original paper](https://arxiv.org/abs/2007.01852). ### BibTeX entry and citation info ```bibtex @misc{feng2020languageagnostic, title={Language-agnostic BERT Sentence Embedding}, author={Fangxiaoyu Feng and Yinfei Yang and Daniel Cer and Naveen Arivazhagan and Wei Wang}, year={2020}, eprint={2007.01852}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Helsinki-NLP/opus-mt-tc-big-sh-en

Helsinki-NLP

transformers

translation

--- language: - bs_Latn - en - hr - sh - sr_Cyrl - sr_Latn tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-sh-en results: - task: name: Translation hrv-eng type: translation args: hrv-eng dataset: name: flores101-devtest type: flores_101 args: hrv eng devtest metrics: - name: BLEU type: bleu value: 37.1 - task: name: Translation bos_Latn-eng type: translation args: bos_Latn-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bos_Latn-eng metrics: - name: BLEU type: bleu value: 66.5 - task: name: Translation hbs-eng type: translation args: hbs-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-eng metrics: - name: BLEU type: bleu value: 56.4 - task: name: Translation hrv-eng type: translation args: hrv-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrv-eng metrics: - name: BLEU type: bleu value: 58.8 - task: name: Translation srp_Cyrl-eng type: translation args: srp_Cyrl-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Cyrl-eng metrics: - name: BLEU type: bleu value: 44.7 - task: name: Translation srp_Latn-eng type: translation args: srp_Latn-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Latn-eng metrics: - name: BLEU type: bleu value: 58.4 --- # opus-mt-tc-big-sh-en Neural machine translation model for translating from Serbo-Croatian (sh) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-02-25 * source language(s): bos_Latn hrv srp_Cyrl srp_Latn * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.zip) * more information released models: [OPUS-MT hbs-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Ispostavilo se da je istina.", "Ovaj vikend imamo besplatne pozive." ] model_name = "pytorch-models/opus-mt-tc-big-sh-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Turns out it's true. # We got free calls this weekend. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-sh-en") print(pipe("Ispostavilo se da je istina.")) # expected output: Turns out it's true. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | bos_Latn-eng | tatoeba-test-v2021-08-07 | 0.80010 | 66.5 | 301 | 1826 | | hbs-eng | tatoeba-test-v2021-08-07 | 0.71744 | 56.4 | 10017 | 68934 | | hrv-eng | tatoeba-test-v2021-08-07 | 0.73563 | 58.8 | 1480 | 10620 | | srp_Cyrl-eng | tatoeba-test-v2021-08-07 | 0.68248 | 44.7 | 1580 | 10181 | | srp_Latn-eng | tatoeba-test-v2021-08-07 | 0.71781 | 58.4 | 6656 | 46307 | | hrv-eng | flores101-devtest | 0.63948 | 37.1 | 1012 | 24721 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 19:21:10 EEST 2022 * port machine: LM0-400-22516.local

anushehchaudry/llama-2-tiny-random

anushehchaudry

transformers

text-generation

--- license: openrail ---

Yntec/epiCPhotoGasm

Yntec

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Photorealistic - Realism - Girls - epinikion - text-to-image - stable-diffusion - stable-diffusion-diffusers - diffusers --- Original page: https://civitai.com/models/132632?modelVersionId=145885 UPDATE: Now with the MoistMixV2VAE baked in! Comparison and prompt:  (Click for larger) macro studio photo of old antique Victorian marmor figurine of cute Rinoa, chibi Rinoa Heartilly, eyeliner, very slim, arm warmers, necklace, sleeveless coat, black shirt, blue skirt, arm ribbon, bike shorts, boots, long hair, black hair, cozy home in the background with candles and plants, Rinoa on flat round porcelain base, by Michelangelo Samples and prompt:   (hyperrealist painting of a girl as genie with a sun on each shoulder ), 1940, magazine ad, iconic. by Daniel F. Gerhartz and greg rutkowski, aggressive color palette, elegant, dream, fantasy, dynamic lighting, beautiful, poster, wlop, trending on artstation, wallpaper, 4 k, award winning, digital art, very

Helsinki-NLP/opus-mt-en-fr

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-en-fr * source languages: en * target languages: fr * OPUS readme: [en-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fr/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/en-fr/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr/opus-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdiscussdev2015-enfr.en.fr | 33.8 | 0.602 | | newsdiscusstest2015-enfr.en.fr | 40.0 | 0.643 | | newssyscomb2009.en.fr | 29.8 | 0.584 | | news-test2008.en.fr | 27.5 | 0.554 | | newstest2009.en.fr | 29.4 | 0.577 | | newstest2010.en.fr | 32.7 | 0.596 | | newstest2011.en.fr | 34.3 | 0.611 | | newstest2012.en.fr | 31.8 | 0.592 | | newstest2013.en.fr | 33.2 | 0.589 | | Tatoeba.en.fr | 50.5 | 0.672 |

prompthero/openjourney-v4

prompthero

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image pinned: true --- # <u>Openjourney v4</u> ## Trained on +124k Midjourney v4 images, by [PromptHero](https://prompthero.com/?utm_source=huggingface&utm_medium=referral) Trained on Stable Diffusion v1.5 using +124000 images, 12400 steps, 4 epochs +32 training hours. 💡 [Openjourney-v4 prompts](https://prompthero.com/openjourney-prompts?version=4) Pss... "mdjrny-v4 style" is not necessary anymore (yay!) 🎓 **Want to learn how to train Openjourney? 👉🏼 __[Join our course](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral)__ 🔥** <img src="https://s3.us-east-1.amazonaws.com/prompthero-newsletter/Group-66.png" alt="openjourney-v4" width="50%"> # Openjourney Links - [Lora version](https://huggingface.co/prompthero/openjourney-lora) - [Openjourney Dreambooth](https://huggingface.co/prompthero/openjourney)

cl-tohoku/bert-large-japanese

cl-tohoku

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 large Japanese (unidic-lite with whole word masking, jawiki-20200831) 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 Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## 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 This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.

facebook/wav2vec2-xls-r-300m

facebook

transformers

--- language: - multilingual - ab - af - sq - am - ar - hy - as - az - ba - eu - be - bn - bs - br - bg - my - yue - ca - ceb - km - zh - cv - hr - cs - da - dv - nl - en - eo - et - fo - fi - fr - gl - lg - ka - de - el - gn - gu - ht - cnh - ha - haw - he - hi - hu - is - id - ia - ga - it - ja - jv - kb - kn - kk - rw - ky - ko - ku - lo - la - lv - ln - lt - lm - mk - mg - ms - ml - mt - gv - mi - mr - mn - ne - no - nn - oc - or - ps - fa - pl - pt - pa - ro - rm - rm - ru - sah - sa - sco - sr - sn - sd - si - sk - sl - so - hsb - es - su - sw - sv - tl - tg - ta - tt - te - th - bo - tp - tr - tk - uk - ur - uz - vi - vot - war - cy - yi - yo - zu language_bcp47: - zh-HK - zh-TW - fy-NL datasets: - common_voice - multilingual_librispeech tags: - speech - xls_r - xls_r_pretrained license: apache-2.0 --- # Wav2Vec2-XLS-R-300M [Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) counting **300 million** parameters.  XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. When using the model make sure that your speech input is sampled at 16kHz. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out [**this blog**](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information about ASR. [XLS-R Paper](https://arxiv.org/abs/2111.09296) Authors: Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli **Abstract** This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on 436K hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 20%-33% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage See [this google colab](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLS_R_on_Common_Voice.ipynb) for more information on how to fine-tune the model. You can find other pretrained XLS-R models with different numbers of parameters: * [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m) * [1B version version](https://huggingface.co/facebook/wav2vec2-xls-r-1b) * [2B version version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)

colorfulscoop/sbert-base-ja

colorfulscoop

sentence-transformers

sentence-similarity

--- language: ja pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity widget: source_sentence: "走るのが趣味です" sentences: - 外をランニングするのが好きです - 運動はそこそこです - 走るのは嫌いです license: cc-by-sa-4.0 --- # Sentence BERT base Japanese model This repository contains a Sentence BERT base model for Japanese. ## Pretrained model This model utilizes a Japanese BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) as a pretrained model. ## Training data [Japanese SNLI dataset](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) released under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/) is used for training. Original training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared. * Train data: 523,005 samples * Valid data: 10,000 samples * Test data: 3,916 samples ## Model description This model utilizes `SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) . The model detail is as below. ```py >>> from sentence_transformers import SentenceTransformer >>> SentenceTransformer("colorfulscoop/sbert-base-ja") SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (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}) ) ``` ## Training This model finetuned [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8. Note: in a original paper of [Sentence BERT](https://arxiv.org/abs/1908.10084), a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16. Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti. After training, test set accuracy reached to 0.8529. Training code is available in [a GitHub repository](https://github.com/colorfulscoop/sbert-ja). ## Usage First, install dependecies. ```sh $ pip install sentence-transformers==2.0.0 ``` Then initialize `SentenceTransformer` model and use `encode` method to convert to vectors. ```py >>> from sentence_transformers import SentenceTransformer >>> model = SentenceTransformer("colorfulscoop/sbert-base-ja") >>> sentences = ["外をランニングするのが好きです", "海外旅行に行くのが趣味です"] >>> model.encode(sentences) ``` ## License Copyright (c) 2021 Colorful Scoop All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/). **Disclaimer:** Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output. --- This model utilizes the folllowing pretrained model. * **Name:** bert-base-ja * **Credit:** (c) 2021 Colorful Scoop * **License:** [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) * **Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output. * **Link:** https://huggingface.co/colorfulscoop/bert-base-ja --- This model utilizes the following data for fine-tuning. * **Name:** 日本語SNLI(JSNLI)データセット * **Credit:** [https://nlp.ist.i.kyoto-u.ac.jp/index.php?日本語SNLI(JSNLI)データセット](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) * **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) * **Link:** [https://nlp.ist.i.kyoto-u.ac.jp/index.php?日本語SNLI(JSNLI)データセット](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)

beomi/kcbert-base

beomi

transformers

fill-mask

--- language: ko license: apache-2.0 tags: - korean --- # KcBERT: Korean comments BERT ** Updates on 2021.04.07 ** - KcELECTRA가 릴리즈 되었습니다!🤗 - KcELECTRA는 보다 더 많은 데이터셋, 그리고 더 큰 General vocab을 통해 KcBERT 대비 **모든 태스크에서 더 높은 성능**을 보입니다. - 아래 깃헙 링크에서 직접 사용해보세요! - https://github.com/Beomi/KcELECTRA ** Updates on 2021.03.14 ** - KcBERT Paper 인용 표기를 추가하였습니다.(bibtex) - KcBERT-finetune Performance score를 본문에 추가하였습니다. ** Updates on 2020.12.04 ** Huggingface Transformers가 v4.0.0으로 업데이트됨에 따라 Tutorial의 코드가 일부 변경되었습니다. 업데이트된 KcBERT-Large NSMC Finetuning Colab: <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> ** Updates on 2020.09.11 ** KcBERT를 Google Colab에서 TPU를 통해 학습할 수 있는 튜토리얼을 제공합니다! 아래 버튼을 눌러보세요. Colab에서 TPU로 KcBERT Pretrain 해보기: <a href="https://colab.research.google.com/drive/1lYBYtaXqt9S733OXdXvrvC09ysKFN30W"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> 텍스트 분량만 전체 12G 텍스트 중 일부(144MB)로 줄여 학습을 진행합니다. 한국어 데이터셋/코퍼스를 좀더 쉽게 사용할 수 있는 [Korpora](https://github.com/ko-nlp/Korpora) 패키지를 사용합니다. ** Updates on 2020.09.08 ** Github Release를 통해 학습 데이터를 업로드하였습니다. 다만 한 파일당 2GB 이내의 제약으로 인해 분할압축되어있습니다. 아래 링크를 통해 받아주세요. (가입 없이 받을 수 있어요. 분할압축) 만약 한 파일로 받고싶으시거나/Kaggle에서 데이터를 살펴보고 싶으시다면 아래의 캐글 데이터셋을 이용해주세요. - Github릴리즈: https://github.com/Beomi/KcBERT/releases/tag/TrainData_v1 ** Updates on 2020.08.22 ** Pretrain Dataset 공개 - 캐글: https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments (한 파일로 받을 수 있어요. 단일파일) Kaggle에 학습을 위해 정제한(아래 `clean`처리를 거친) Dataset을 공개하였습니다! 직접 다운받으셔서 다양한 Task에 학습을 진행해보세요 :) --- 공개된 한국어 BERT는 대부분 한국어 위키, 뉴스 기사, 책 등 잘 정제된 데이터를 기반으로 학습한 모델입니다. 한편, 실제로 NSMC와 같은 댓글형 데이터셋은 정제되지 않았고 구어체 특징에 신조어가 많으며, 오탈자 등 공식적인 글쓰기에서 나타나지 않는 표현들이 빈번하게 등장합니다. KcBERT는 위와 같은 특성의 데이터셋에 적용하기 위해, 네이버 뉴스에서 댓글과 대댓글을 수집해, 토크나이저와 BERT모델을 처음부터 학습한 Pretrained BERT 모델입니다. KcBERT는 Huggingface의 Transformers 라이브러리를 통해 간편히 불러와 사용할 수 있습니다. (별도의 파일 다운로드가 필요하지 않습니다.) ## KcBERT Performance - Finetune 코드는 https://github.com/Beomi/KcBERT-finetune 에서 찾아보실 수 있습니다. | | Size<br/>(용량) | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | | :-------------------- | :---: | :----------------: | :--------------------: | :----------------: | :------------------: | :-----------------------: | :-------------------------: | :---------------------------: | | KcBERT-Base | 417M | 89.62 | 84.34 | 66.95 | 74.85 | 75.57 | 93.93 | 60.25 / 84.39 | | KcBERT-Large | 1.2G | **90.68** | 85.53 | 70.15 | 76.99 | 77.49 | 94.06 | 62.16 / 86.64 | | KoBERT | 351M | 89.63 | 86.11 | 80.65 | 79.00 | 79.64 | 93.93 | 52.81 / 80.27 | | XLM-Roberta-Base | 1.03G | 89.49 | 86.26 | 82.95 | 79.92 | 79.09 | 93.53 | 64.70 / 88.94 | | HanBERT | 614M | 90.16 | **87.31** | 82.40 | **80.89** | 83.33 | 94.19 | 78.74 / 92.02 | | KoELECTRA-Base | 423M | **90.21** | 86.87 | 81.90 | 80.85 | 83.21 | 94.20 | 61.10 / 89.59 | | KoELECTRA-Base-v2 | 423M | 89.70 | 87.02 | **83.90** | 80.61 | **84.30** | **94.72** | **84.34 / 92.58** | | DistilKoBERT | 108M | 88.41 | 84.13 | 62.55 | 70.55 | 73.21 | 92.48 | 54.12 / 77.80 | \*HanBERT의 Size는 Bert Model과 Tokenizer DB를 합친 것입니다. \***config의 세팅을 그대로 하여 돌린 결과이며, hyperparameter tuning을 추가적으로 할 시 더 좋은 성능이 나올 수 있습니다.** ## How to use ### Requirements - `pytorch <= 1.8.0` - `transformers ~= 3.0.1` - `transformers ~= 4.0.0` 도 호환됩니다. - `emoji ~= 0.6.0` - `soynlp ~= 0.0.493` ```python from transformers import AutoTokenizer, AutoModelWithLMHead # Base Model (108M) tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-base") model = AutoModelWithLMHead.from_pretrained("beomi/kcbert-base") # Large Model (334M) tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-large") model = AutoModelWithLMHead.from_pretrained("beomi/kcbert-large") ``` ### Pretrain & Finetune Colab 링크 모음 #### Pretrain Data - [데이터셋 다운로드(Kaggle, 단일파일, 로그인 필요)](https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments) - [데이터셋 다운로드(Github, 압축 여러파일, 로그인 불필요)](https://github.com/Beomi/KcBERT/releases/tag/TrainData_v1) #### Pretrain Code Colab에서 TPU로 KcBERT Pretrain 해보기: <a href="https://colab.research.google.com/drive/1lYBYtaXqt9S733OXdXvrvC09ysKFN30W"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> #### Finetune Samples **KcBERT-Base** NSMC Finetuning with PyTorch-Lightning (Colab) <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> **KcBERT-Large** NSMC Finetuning with PyTorch-Lightning (Colab) <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> > 위 두 코드는 Pretrain 모델(base, large)와 batch size만 다를 뿐, 나머지 코드는 완전히 동일합니다. ## Train Data & Preprocessing ### Raw Data 학습 데이터는 2019.01.01 ~ 2020.06.15 사이에 작성된 **댓글 많은 뉴스** 기사들의 **댓글과 대댓글**을 모두 수집한 데이터입니다. 데이터 사이즈는 텍스트만 추출시 **약 15.4GB이며, 1억1천만개 이상의 문장**으로 이뤄져 있습니다. ### Preprocessing PLM 학습을 위해서 전처리를 진행한 과정은 다음과 같습니다. 1. 한글 및 영어, 특수문자, 그리고 이모지(🥳)까지! 정규표현식을 통해 한글, 영어, 특수문자를 포함해 Emoji까지 학습 대상에 포함했습니다. 한편, 한글 범위를 `ㄱ-ㅎ가-힣` 으로 지정해 `ㄱ-힣` 내의 한자를 제외했습니다. 2. 댓글 내 중복 문자열 축약 `ㅋㅋㅋㅋㅋ`와 같이 중복된 글자를 `ㅋㅋ`와 같은 것으로 합쳤습니다. 3. Cased Model KcBERT는 영문에 대해서는 대소문자를 유지하는 Cased model입니다. 4. 글자 단위 10글자 이하 제거 10글자 미만의 텍스트는 단일 단어로 이뤄진 경우가 많아 해당 부분을 제외했습니다. 5. 중복 제거 중복적으로 쓰인 댓글을 제거하기 위해 중복 댓글을 하나로 합쳤습니다. 이를 통해 만든 최종 학습 데이터는 **12.5GB, 8.9천만개 문장**입니다. 아래 명령어로 pip로 설치한 뒤, 아래 clean함수로 클리닝을 하면 Downstream task에서 보다 성능이 좋아집니다. (`[UNK]` 감소) ```bash pip install soynlp emoji ``` 아래 `clean` 함수를 Text data에 사용해주세요. ```python import re import emoji from soynlp.normalizer import repeat_normalize emojis = list({y for x in emoji.UNICODE_EMOJI.values() for y in x.keys()}) emojis = ''.join(emojis) pattern = re.compile(f'[^ .,?!/@$%~％·∼()\x00-\x7Fㄱ-ㅣ가-힣{emojis}]+') url_pattern = re.compile( r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)') def clean(x): x = pattern.sub(' ', x) x = url_pattern.sub('', x) x = x.strip() x = repeat_normalize(x, num_repeats=2) return x ``` ### Cleaned Data (Released on Kaggle) 원본 데이터를 위 `clean`함수로 정제한 12GB분량의 txt 파일을 아래 Kaggle Dataset에서 다운받으실 수 있습니다 :) https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments ## Tokenizer Train Tokenizer는 Huggingface의 [Tokenizers](https://github.com/huggingface/tokenizers) 라이브러리를 통해 학습을 진행했습니다. 그 중 `BertWordPieceTokenizer` 를 이용해 학습을 진행했고, Vocab Size는 `30000`으로 진행했습니다. Tokenizer를 학습하는 것에는 `1/10`로 샘플링한 데이터로 학습을 진행했고, 보다 골고루 샘플링하기 위해 일자별로 stratify를 지정한 뒤 햑습을 진행했습니다. ## BERT Model Pretrain - KcBERT Base config ```json { "max_position_embeddings": 300, "hidden_dropout_prob": 0.1, "hidden_act": "gelu", "initializer_range": 0.02, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 30000, "hidden_size": 768, "attention_probs_dropout_prob": 0.1, "directionality": "bidi", "num_attention_heads": 12, "intermediate_size": 3072, "architectures": [ "BertForMaskedLM" ], "model_type": "bert" } ``` - KcBERT Large config ```json { "type_vocab_size": 2, "initializer_range": 0.02, "max_position_embeddings": 300, "vocab_size": 30000, "hidden_size": 1024, "hidden_dropout_prob": 0.1, "model_type": "bert", "directionality": "bidi", "pad_token_id": 0, "layer_norm_eps": 1e-12, "hidden_act": "gelu", "num_hidden_layers": 24, "num_attention_heads": 16, "attention_probs_dropout_prob": 0.1, "intermediate_size": 4096, "architectures": [ "BertForMaskedLM" ] } ``` BERT Model Config는 Base, Large 기본 세팅값을 그대로 사용했습니다. (MLM 15% 등) TPU `v3-8` 을 이용해 각각 3일, N일(Large는 학습 진행 중)을 진행했고, 현재 Huggingface에 공개된 모델은 1m(100만) step을 학습한 ckpt가 업로드 되어있습니다. 모델 학습 Loss는 Step에 따라 초기 200k에 가장 빠르게 Loss가 줄어들다 400k이후로는 조금씩 감소하는 것을 볼 수 있습니다. - Base Model Loss  - Large Model Loss  학습은 GCP의 TPU v3-8을 이용해 학습을 진행했고, 학습 시간은 Base Model 기준 2.5일정도 진행했습니다. Large Model은 약 5일정도 진행한 뒤 가장 낮은 loss를 가진 체크포인트로 정했습니다. ## Example ### HuggingFace MASK LM [HuggingFace kcbert-base 모델](https://huggingface.co/beomi/kcbert-base?text=오늘은+날씨가+[MASK]) 에서 아래와 같이 테스트 해 볼 수 있습니다.  물론 [kcbert-large 모델](https://huggingface.co/beomi/kcbert-large?text=오늘은+날씨가+[MASK]) 에서도 테스트 할 수 있습니다.  ### NSMC Binary Classification [네이버 영화평 코퍼스](https://github.com/e9t/nsmc) 데이터셋을 대상으로 Fine Tuning을 진행해 성능을 간단히 테스트해보았습니다. Base Model을 Fine Tune하는 코드는 <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> 에서 직접 실행해보실 수 있습니다. Large Model을 Fine Tune하는 코드는 <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> 에서 직접 실행해볼 수 있습니다. - GPU는 P100 x1대 기준 1epoch에 2-3시간, TPU는 1epoch에 1시간 내로 소요됩니다. - GPU RTX Titan x4대 기준 30분/epoch 소요됩니다. - 예시 코드는 [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)으로 개발했습니다. #### 실험결과 - KcBERT-Base Model 실험결과: Val acc `.8905`  - KcBERT-Large Model 실험 결과: Val acc `.9089`  > 더 다양한 Downstream Task에 대해 테스트를 진행하고 공개할 예정입니다. ## 인용표기/Citation KcBERT를 인용하실 때는 아래 양식을 통해 인용해주세요. ``` @inproceedings{lee2020kcbert, title={KcBERT: Korean Comments BERT}, author={Lee, Junbum}, booktitle={Proceedings of the 32nd Annual Conference on Human and Cognitive Language Technology}, pages={437--440}, year={2020} } ``` - 논문집 다운로드 링크: http://hclt.kr/dwn/?v=bG5iOmNvbmZlcmVuY2U7aWR4OjMy (*혹은 http://hclt.kr/symp/?lnb=conference ) ## Acknowledgement KcBERT Model을 학습하는 GCP/TPU 환경은 [TFRC](https://www.tensorflow.org/tfrc?hl=ko) 프로그램의 지원을 받았습니다. 모델 학습 과정에서 많은 조언을 주신 [Monologg](https://github.com/monologg/) 님 감사합니다 :) ## Reference ### Github Repos - [BERT by Google](https://github.com/google-research/bert) - [KoBERT by SKT](https://github.com/SKTBrain/KoBERT) - [KoELECTRA by Monologg](https://github.com/monologg/KoELECTRA/) - [Transformers by Huggingface](https://github.com/huggingface/transformers) - [Tokenizers by Hugginface](https://github.com/huggingface/tokenizers) ### Papers - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) ### Blogs - [Monologg님의 KoELECTRA 학습기](https://monologg.kr/categories/NLP/ELECTRA/) - [Colab에서 TPU로 BERT 처음부터 학습시키기 - Tensorflow/Google ver.](https://beomi.github.io/2020/02/26/Train-BERT-from-scratch-on-colab-TPU-Tensorflow-ver/)

dangvantuan/sentence-camembert-large

dangvantuan

transformers

sentence-similarity

--- pipeline_tag: sentence-similarity language: fr datasets: - stsb_multi_mt tags: - Text - Sentence Similarity - Sentence-Embedding - camembert-large license: apache-2.0 model-index: - name: sentence-camembert-large by Van Tuan DANG results: - task: name: Sentence-Embedding type: Text Similarity dataset: name: Text Similarity fr type: stsb_multi_mt args: fr metrics: - name: Test Pearson correlation coefficient type: Pearson_correlation_coefficient value: xx.xx --- ## Description: [**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in queries and documents, offering a powerful semantic search. ## Pre-trained sentence embedding models are state-of-the-art of Sentence Embeddings for French. The model is Fine-tuned using pre-trained [facebook/camembert-large](https://huggingface.co/camembert/camembert-large) and [Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) on dataset [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train) ## Usage The model can be used directly (without a language model) as follows: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/sentence-camembert-large") sentences = ["Un avion est en train de décoller.", "Un homme joue d'une grande flûte.", "Un homme étale du fromage râpé sur une pizza.", "Une personne jette un chat au plafond.", "Une personne est en train de plier un morceau de papier.", ] embeddings = model.encode(sentences) ``` ## Evaluation The model can be evaluated as follows on the French test data of stsb. ```python from sentence_transformers import SentenceTransformer from sentence_transformers.readers import InputExample from datasets import load_dataset def convert_dataset(dataset): dataset_samples=[] for df in dataset: score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1 inp_example = InputExample(texts=[df['sentence1'], df['sentence2']], label=score) dataset_samples.append(inp_example) return dataset_samples # Loading the dataset for evaluation df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev") df_test = load_dataset("stsb_multi_mt", name="fr", split="test") # Convert the dataset for evaluation # For Dev set: dev_samples = convert_dataset(df_dev) val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev') val_evaluator(model, output_path="./") # For Test set: test_samples = convert_dataset(df_test) test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test') test_evaluator(model, output_path="./") ``` **Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correlation | Spearman correlation | #params | | ------------- | ------------- | ------------- |------------- | | [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 88.2 |88.02 | 336M| | [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base) | 86.73|86.54 | 110M | | [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 79.22 | 79.16|135M | | [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 85 | NaN|175B | | [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.75 | 80.44|NaN | - On test | Model | Pearson correlation | Spearman correlation | | ------------- | ------------- | ------------- | | [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 85.9 | 85.8| | [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 82.36 | 81.64| | [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 78.62 | 77.48| | [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 82 | NaN|175B | | [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.05 | 77.56|NaN | ## Citation @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} } @article{martin2020camembert, title={CamemBERT: a Tasty French Language Mode}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} }

vinai/phobert-base

vinai

transformers

fill-mask

# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance. - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744): @article{phobert, title = {{PhoBERT: Pre-trained language models for Vietnamese}}, author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, journal = {Findings of EMNLP}, year = {2020} } **Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software. For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!

cointegrated/rubert-tiny-toxicity

cointegrated

transformers

text-classification

--- language: ["ru"] tags: - russian - classification - toxicity - multilabel widget: - text: "Иди ты нафиг!" --- This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks. The problem is formulated as multilabel classification with the following classes: - `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition. - `insult` - `obscenity` - `threat` - `dangerous`: the text is inappropriate, in the sense of [Babakov et.al.](https://arxiv.org/abs/2103.05345), i.e. it can harm the reputation of the speaker. A text can be considered safe if it is BOTH `non-toxic` and NOT `dangerous`. ## Usage The function below estimates the probability that the text is either toxic OR dangerous: ```python # !pip install transformers sentencepiece --quiet import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification model_checkpoint = 'cointegrated/rubert-tiny-toxicity' tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint) if torch.cuda.is_available(): model.cuda() def text2toxicity(text, aggregate=True): """ Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)""" with torch.no_grad(): inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device) proba = torch.sigmoid(model(**inputs).logits).cpu().numpy() if isinstance(text, str): proba = proba[0] if aggregate: return 1 - proba.T[0] * (1 - proba.T[-1]) return proba print(text2toxicity('я люблю нигеров', True)) # 0.9350118728093193 print(text2toxicity('я люблю нигеров', False)) # [0.9715758 0.0180863 0.0045551 0.00189755 0.9331106 ] print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], True)) # [0.93501186 0.04156357] print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], False)) # [[9.7157580e-01 1.8086294e-02 4.5550885e-03 1.8975559e-03 9.3311059e-01] # [9.9979788e-01 1.9048342e-04 1.5297388e-04 1.7452303e-04 4.1369814e-02]] ``` ## Training The model has been trained on the joint dataset of [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) and [Babakov et.al.](https://arxiv.org/abs/2103.05345) with `Adam` optimizer, the learning rate of `1e-5`, and batch size of `64` for `15` epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is: ``` non-toxic : 0.9937 insult : 0.9912 obscenity : 0.9881 threat : 0.9910 dangerous : 0.8295 ```

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract

microsoft

transformers

fill-mask

--- language: en tags: - exbert license: mit widget: - text: "[MASK] is a tyrosine kinase inhibitor." --- ## MSR BiomedBERT (abstracts only) <div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;"> * This model was previously named **"PubMedBERT (abstracts)"**. * You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name. </div> Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. This BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/). This model achieves state-of-the-art performance on several biomedical NLP tasks, as shown on the [Biomedical Language Understanding and Reasoning Benchmark](https://aka.ms/BLURB). ## Citation If you find BiomedBERT useful in your research, please cite the following paper: ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, } ``` <a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=10&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

succinctly/text2image-prompt-generator

succinctly

transformers

text-generation

--- language: - "en" thumbnail: "https://drive.google.com/uc?export=view&id=1JWwrxQbr1s5vYpIhPna_p2IG1pE5rNiV" tags: - text2image - prompting license: "cc-by-2.0" datasets: - "succinctly/midjourney-prompts" --- This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this dataset was scraped, see [Midjourney User Prompts & Generated Images (250k)](https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage). This prompt generator can be used to auto-complete prompts for any text-to-image model (including the DALL·E family):  Note that, while this model can be used together with any text-to-image model, it occasionally produces Midjourney-specific tags. Users can specify certain requirements via [double-dashed parameters](https://midjourney.gitbook.io/docs/imagine-parameters) (e.g. `--ar 16:9` sets the aspect ratio to 16:9, and `--no snake` asks the model to exclude snakes from the generated image) or set the importance of various entities in the image via [explicit weights](https://midjourney.gitbook.io/docs/user-manual#advanced-text-weights) (e.g. `hot dog::1.5 food::-1` is likely to produce the image of an animal instead of a frankfurter). When using this model, please attribute credit to [Succinctly AI](https://succinctly.ai).

timpal0l/mdeberta-v3-base-squad2

timpal0l

transformers

question-answering

--- datasets: - squad_v2 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 tags: - deberta - deberta-v3 - mdeberta - question-answering - qa - multilingual thumbnail: https://huggingface.co/front/thumbnails/microsoft.png license: mit --- ## This model can be used for Extractive QA It has been finetuned for 3 epochs on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/). ## Usage ```python from transformers import pipeline qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2") question = "Where do I live?" context = "My name is Tim and I live in Sweden." qa_model(question = question, context = context) # {'score': 0.975547730922699, 'start': 28, 'end': 36, 'answer': ' Sweden.'} ``` ## Evaluation on SQuAD2.0 dev set ```bash { "epoch": 3.0, "eval_HasAns_exact": 79.65587044534414, "eval_HasAns_f1": 85.91387795001529, "eval_HasAns_total": 5928, "eval_NoAns_exact": 82.10260723296888, "eval_NoAns_f1": 82.10260723296888, "eval_NoAns_total": 5945, "eval_best_exact": 80.8809904826076, "eval_best_exact_thresh": 0.0, "eval_best_f1": 84.00551406448994, "eval_best_f1_thresh": 0.0, "eval_exact": 80.8809904826076, "eval_f1": 84.00551406449004, "eval_samples": 12508, "eval_total": 11873, "train_loss": 0.7729689576483615, "train_runtime": 9118.953, "train_samples": 134891, "train_samples_per_second": 44.377, "train_steps_per_second": 0.925 } ``` ## 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. mDeBERTa is multilingual version of DeBERTa which use the same structure as DeBERTa and was trained with CC100 multilingual data. The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer. This model was trained using the 2.5T CC100 data as XLM-R.

databricks/dolly-v2-3b

databricks

transformers

text-generation

--- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-3b Model Card ## Summary Databricks' `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-3b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art. It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

NousResearch/Llama-2-7b-hf

NousResearch

transformers

text-generation

--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **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/) ## 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/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

latent-consistency/lcm-lora-sdxl

latent-consistency

diffusers

text-to-image

--- library_name: diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - lora - text-to-image license: openrail++ inference: false --- # Latent Consistency Model (LCM) LoRA: SDXL Latent Consistency Model (LCM) LoRA was proposed in [LCM-LoRA: A universal Stable-Diffusion Acceleration Module](https://arxiv.org/abs/2311.05556) by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.* It is a distilled consistency adapter for [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows to reduce the number of inference steps to only between **2 - 8 steps**. | Model | Params / M | |----------------------------------------------------------------------------|------------| | [lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5) | 67.5 | | [lcm-lora-ssd-1b](https://huggingface.co/latent-consistency/lcm-lora-ssd-1b) | 105 | | [**lcm-lora-sdxl**](https://huggingface.co/latent-consistency/lcm-lora-sdxl) | **197M** | ## Usage LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`. audio dataset from the Hugging Face Hub: ```bash pip install --upgrade pip pip install --upgrade diffusers transformers accelerate peft ``` ***Note: For detailed usage examples we recommend you to check out our official [LCM-LoRA docs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm_lora)*** ### Text-to-Image The adapter can be loaded with it's base model `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps. Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0. ```python import torch from diffusers import LCMScheduler, AutoPipelineForText2Image model_id = "stabilityai/stable-diffusion-xl-base-1.0" adapter_id = "latent-consistency/lcm-lora-sdxl" pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to("cuda") # load and fuse lcm lora pipe.load_lora_weights(adapter_id) pipe.fuse_lora() prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" # disable guidance_scale by passing 0 image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0] ```  ### Inpainting LCM-LoRA can be used for inpainting as well. ```python import torch from diffusers import AutoPipelineForInpainting, LCMScheduler from diffusers.utils import load_image, make_image_grid pipe = AutoPipelineForInpainting.from_pretrained( "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16", ).to("cuda") # set scheduler pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) # load LCM-LoRA pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") pipe.fuse_lora() # load base and mask image init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png").resize((1024, 1024)) mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png").resize((1024, 1024)) prompt = "a castle on top of a mountain, highly detailed, 8k" generator = torch.manual_seed(42) image = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, num_inference_steps=5, guidance_scale=4, ).images[0] make_image_grid([init_image, mask_image, image], rows=1, cols=3) ```  ## Combine with styled LoRAs LCM-LoRA can be combined with other LoRAs to generate styled-images in very few steps (4-8). In the following example, we'll use the LCM-LoRA with the [papercut LoRA](TheLastBen/Papercut_SDXL). To learn more about how to combine LoRAs, refer to [this guide](https://huggingface.co/docs/diffusers/tutorials/using_peft_for_inference#combine-multiple-adapters). ```python import torch from diffusers import DiffusionPipeline, LCMScheduler pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=torch.float16 ).to("cuda") # set scheduler pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) # load LoRAs pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm") pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut") # Combine LoRAs pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8]) prompt = "papercut, a cute fox" generator = torch.manual_seed(0) image = pipe(prompt, num_inference_steps=4, guidance_scale=1, generator=generator).images[0] image ```  ### ControlNet ```python import torch import cv2 import numpy as np from PIL import Image from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, LCMScheduler from diffusers.utils import load_image image = load_image( "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" ).resize((1024, 1024)) image = np.array(image) low_threshold = 100 high_threshold = 200 image = cv2.Canny(image, low_threshold, high_threshold) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) canny_image = Image.fromarray(image) controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0-small", torch_dtype=torch.float16, variant="fp16") pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None, variant="fp16" ).to("cuda") # set scheduler pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) # load LCM-LoRA pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") pipe.fuse_lora() generator = torch.manual_seed(0) image = pipe( "picture of the mona lisa", image=canny_image, num_inference_steps=5, guidance_scale=1.5, controlnet_conditioning_scale=0.5, cross_attention_kwargs={"scale": 1}, generator=generator, ).images[0] make_image_grid([canny_image, image], rows=1, cols=2) ```  <Tip> The inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one. </Tip> ### T2I Adapter This example shows how to use the LCM-LoRA with the [Canny T2I-Adapter](TencentARC/t2i-adapter-canny-sdxl-1.0) and SDXL. ```python import torch import cv2 import numpy as np from PIL import Image from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, LCMScheduler from diffusers.utils import load_image, make_image_grid # Prepare image # Detect the canny map in low resolution to avoid high-frequency details image = load_image( "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg" ).resize((384, 384)) image = np.array(image) low_threshold = 100 high_threshold = 200 image = cv2.Canny(image, low_threshold, high_threshold) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) canny_image = Image.fromarray(image).resize((1024, 1024)) # load adapter adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda") pipe = StableDiffusionXLAdapterPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", adapter=adapter, torch_dtype=torch.float16, variant="fp16", ).to("cuda") # set scheduler pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) # load LCM-LoRA pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") prompt = "Mystical fairy in real, magic, 4k picture, high quality" negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured" generator = torch.manual_seed(0) image = pipe( prompt=prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=4, guidance_scale=1.5, adapter_conditioning_scale=0.8, adapter_conditioning_factor=1, generator=generator, ).images[0] make_image_grid([canny_image, image], rows=1, cols=2) ```  ## Speed Benchmark TODO ## Training TODO

thenlper/gte-large

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - 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: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.44942006292234 - type: cos_sim_spearman value: 79.80930790660699 - type: euclidean_pearson value: 82.93400777494863 - type: euclidean_spearman value: 80.04664991110705 - type: manhattan_pearson value: 82.93551681854949 - type: manhattan_spearman value: 80.03156736837379 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.63574059135726 - type: cos_sim_spearman value: 76.80552915288186 - type: euclidean_pearson value: 82.46368529820518 - type: euclidean_spearman value: 76.60338474719275 - type: manhattan_pearson value: 82.4558617035968 - type: manhattan_spearman value: 76.57936082895705 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.24116811084211 - type: cos_sim_spearman value: 88.10998662068769 - type: euclidean_pearson value: 87.04961732352689 - type: euclidean_spearman value: 88.12543945864087 - type: manhattan_pearson value: 86.9905224528854 - type: manhattan_spearman value: 88.07827944705546 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.74847296555048 - type: cos_sim_spearman value: 82.66200957916445 - type: euclidean_pearson value: 84.48132256004965 - type: euclidean_spearman value: 82.67915286000596 - type: manhattan_pearson value: 84.44950477268334 - type: manhattan_spearman value: 82.63327639173352 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.23056258027053 - type: cos_sim_spearman value: 88.92791680286955 - type: euclidean_pearson value: 88.13819235461933 - type: euclidean_spearman value: 88.87294661361716 - type: manhattan_pearson value: 88.14212133687899 - type: manhattan_spearman value: 88.88551854529777 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.64179522732887 - type: cos_sim_spearman value: 84.25028809903114 - type: euclidean_pearson value: 83.40175015236979 - type: euclidean_spearman value: 84.23369296429406 - type: manhattan_pearson value: 83.43768174261321 - type: manhattan_spearman value: 84.27855229214734 - 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: 88.20378955494732 - type: cos_sim_spearman value: 88.46863559173111 - type: euclidean_pearson value: 88.8249295811663 - type: euclidean_spearman value: 88.6312737724905 - type: manhattan_pearson value: 88.87744466378827 - type: manhattan_spearman value: 88.82908423767314 - 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: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.1976344717285 - type: mrr value: 96.3703145075694 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.511 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.208 - type: map_at_1000 value: 70.22800000000001 - type: map_at_3 value: 66.986 - type: map_at_5 value: 68.529 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.55 - type: mrr_at_100 value: 70.985 - type: mrr_at_1000 value: 71.004 - type: mrr_at_3 value: 68.611 - type: mrr_at_5 value: 69.728 - type: ndcg_at_1 value: 62.333000000000006 - type: ndcg_at_10 value: 74.265 - type: ndcg_at_100 value: 76.361 - type: ndcg_at_1000 value: 76.82900000000001 - type: ndcg_at_3 value: 69.772 - type: ndcg_at_5 value: 71.94800000000001 - type: precision_at_1 value: 62.333000000000006 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 75.2 - type: recall_at_5 value: 80.661 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81683168316832 - type: cos_sim_ap value: 95.74716566563774 - type: cos_sim_f1 value: 90.64238745574103 - type: cos_sim_precision value: 91.7093142272262 - type: cos_sim_recall value: 89.60000000000001 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 91.09013507754594 - type: dot_f1 value: 84.54227113556779 - type: dot_precision value: 84.58458458458459 - type: dot_recall value: 84.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.6324301072609 - type: euclidean_f1 value: 90.58341862845445 - type: euclidean_precision value: 92.76729559748428 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.81980198019802 - type: manhattan_ap value: 95.68510494437183 - type: manhattan_f1 value: 90.58945191313342 - type: manhattan_precision value: 93.79014989293361 - type: manhattan_recall value: 87.6 - type: max_accuracy value: 99.81980198019802 - type: max_ap value: 95.74716566563774 - type: max_f1 value: 90.64238745574103 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.63761899427078 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.572473369697235 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.63000245208579 - type: mrr value: 54.504193722943725 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.300791939416545 - type: cos_sim_spearman value: 31.662904057924123 - type: dot_pearson value: 26.21198530758316 - type: dot_spearman value: 27.006921548904263 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.197 - type: map_at_10 value: 1.752 - type: map_at_100 value: 10.795 - type: map_at_1000 value: 27.18 - type: map_at_3 value: 0.5890000000000001 - type: map_at_5 value: 0.938 - type: mrr_at_1 value: 74 - type: mrr_at_10 value: 85.833 - type: mrr_at_100 value: 85.833 - type: mrr_at_1000 value: 85.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 85.833 - type: ndcg_at_1 value: 69 - type: ndcg_at_10 value: 70.22 - type: ndcg_at_100 value: 55.785 - type: ndcg_at_1000 value: 52.93600000000001 - type: ndcg_at_3 value: 72.084 - type: ndcg_at_5 value: 71.184 - type: precision_at_1 value: 74 - type: precision_at_10 value: 75.2 - type: precision_at_100 value: 57.3 - type: precision_at_1000 value: 23.302 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 75.6 - type: recall_at_1 value: 0.197 - type: recall_at_10 value: 2.019 - type: recall_at_100 value: 14.257 - type: recall_at_1000 value: 50.922 - type: recall_at_3 value: 0.642 - type: recall_at_5 value: 1.043 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.803 - type: map_at_10 value: 10.407 - type: map_at_100 value: 16.948 - type: map_at_1000 value: 18.424 - type: map_at_3 value: 5.405 - type: map_at_5 value: 6.908 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 50.221000000000004 - type: mrr_at_100 value: 51.388 - type: mrr_at_1000 value: 51.402 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.626 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 25.507 - type: ndcg_at_100 value: 38.296 - type: ndcg_at_1000 value: 49.492000000000004 - type: ndcg_at_3 value: 29.006999999999998 - type: ndcg_at_5 value: 25.979000000000003 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.567 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.803 - type: recall_at_10 value: 16.378 - type: recall_at_100 value: 50.489 - type: recall_at_1000 value: 85.013 - type: recall_at_3 value: 6.505 - type: recall_at_5 value: 9.243 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.55579999999999 - type: ap value: 14.206982753316227 - type: f1 value: 54.372142814964285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.57611771363893 - type: f1 value: 56.924172639063144 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.82304915719759 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.92716218632653 - type: cos_sim_ap value: 73.73359122546046 - type: cos_sim_f1 value: 68.42559487116262 - type: cos_sim_precision value: 64.22124508215691 - type: cos_sim_recall value: 73.21899736147758 - type: dot_accuracy value: 80.38981939560112 - type: dot_ap value: 54.61060862444974 - type: dot_f1 value: 53.45710627400769 - type: dot_precision value: 44.87638839125761 - type: dot_recall value: 66.09498680738787 - type: euclidean_accuracy value: 86.02849138701794 - type: euclidean_ap value: 73.95673761922404 - type: euclidean_f1 value: 68.6783042394015 - type: euclidean_precision value: 65.1063829787234 - type: euclidean_recall value: 72.66490765171504 - type: manhattan_accuracy value: 85.9808070572808 - type: manhattan_ap value: 73.9050720058029 - type: manhattan_f1 value: 68.57560618983794 - type: manhattan_precision value: 63.70839936608558 - type: manhattan_recall value: 74.24802110817942 - type: max_accuracy value: 86.02849138701794 - type: max_ap value: 73.95673761922404 - type: max_f1 value: 68.6783042394015 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.72783017037295 - type: cos_sim_ap value: 85.52705223340233 - type: cos_sim_f1 value: 77.91659078492079 - type: cos_sim_precision value: 73.93378032764221 - type: cos_sim_recall value: 82.35294117647058 - type: dot_accuracy value: 85.41739434159972 - type: dot_ap value: 77.17734818118443 - type: dot_f1 value: 71.63473589973144 - type: dot_precision value: 66.96123719622415 - type: dot_recall value: 77.00954727440714 - type: euclidean_accuracy value: 88.68125897465751 - type: euclidean_ap value: 85.47712213906692 - type: euclidean_f1 value: 77.81419950830664 - type: euclidean_precision value: 75.37162649733006 - type: euclidean_recall value: 80.42038805050817 - type: manhattan_accuracy value: 88.67349710870494 - type: manhattan_ap value: 85.46506475241955 - type: manhattan_f1 value: 77.87259084890393 - type: manhattan_precision value: 74.54929577464789 - type: manhattan_recall value: 81.50600554357868 - type: max_accuracy value: 88.72783017037295 - type: max_ap value: 85.52705223340233 - type: max_f1 value: 77.91659078492079 language: - en license: mit --- # gte-large 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-large") model = AutoModel.from_pretrained("thenlper/gte-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']) # (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: ``` @misc{li2023general, title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang}, year={2023}, eprint={2308.03281}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```