Datasets:

ftopal

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huggingface-models

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peft

<!-- 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. --> # GUE_tf_1-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset. It achieves the following results on the evaluation set: - Loss: 0.3450 - F1 Score: 0.8487 - Accuracy: 0.849 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5426 | 0.83 | 200 | 0.5290 | 0.7440 | 0.744 | | 0.4977 | 1.67 | 400 | 0.5235 | 0.7433 | 0.744 | | 0.4907 | 2.5 | 600 | 0.5192 | 0.7419 | 0.742 | | 0.4864 | 3.33 | 800 | 0.5160 | 0.7408 | 0.741 | | 0.4874 | 4.17 | 1000 | 0.5147 | 0.7417 | 0.742 | | 0.4788 | 5.0 | 1200 | 0.5142 | 0.7450 | 0.745 | | 0.4768 | 5.83 | 1400 | 0.5102 | 0.7440 | 0.744 | | 0.477 | 6.67 | 1600 | 0.5068 | 0.746 | 0.746 | | 0.4756 | 7.5 | 1800 | 0.5057 | 0.7496 | 0.75 | | 0.4692 | 8.33 | 2000 | 0.5048 | 0.7470 | 0.747 | | 0.4702 | 9.17 | 2200 | 0.4995 | 0.7520 | 0.752 | | 0.4689 | 10.0 | 2400 | 0.5099 | 0.7520 | 0.753 | | 0.469 | 10.83 | 2600 | 0.5097 | 0.7524 | 0.754 | | 0.4645 | 11.67 | 2800 | 0.5029 | 0.7531 | 0.754 | | 0.4572 | 12.5 | 3000 | 0.4997 | 0.7506 | 0.751 | | 0.4689 | 13.33 | 3200 | 0.4994 | 0.7513 | 0.752 | | 0.4581 | 14.17 | 3400 | 0.4953 | 0.7438 | 0.744 | | 0.4552 | 15.0 | 3600 | 0.5015 | 0.7580 | 0.759 | | 0.4557 | 15.83 | 3800 | 0.4990 | 0.7545 | 0.755 | | 0.4571 | 16.67 | 4000 | 0.5008 | 0.7545 | 0.755 | | 0.4532 | 17.5 | 4200 | 0.5042 | 0.7569 | 0.758 | | 0.4481 | 18.33 | 4400 | 0.5031 | 0.7568 | 0.757 | | 0.4569 | 19.17 | 4600 | 0.4986 | 0.7576 | 0.758 | | 0.4535 | 20.0 | 4800 | 0.4959 | 0.7549 | 0.755 | | 0.4517 | 20.83 | 5000 | 0.5015 | 0.7589 | 0.759 | | 0.448 | 21.67 | 5200 | 0.4988 | 0.7579 | 0.758 | | 0.4457 | 22.5 | 5400 | 0.4977 | 0.7550 | 0.755 | | 0.4477 | 23.33 | 5600 | 0.5039 | 0.7514 | 0.752 | | 0.4487 | 24.17 | 5800 | 0.5021 | 0.7595 | 0.76 | | 0.4487 | 25.0 | 6000 | 0.4963 | 0.7520 | 0.752 | | 0.4456 | 25.83 | 6200 | 0.4956 | 0.7499 | 0.75 | | 0.4443 | 26.67 | 6400 | 0.4957 | 0.7489 | 0.749 | | 0.4454 | 27.5 | 6600 | 0.4992 | 0.7599 | 0.76 | | 0.4431 | 28.33 | 6800 | 0.4964 | 0.7480 | 0.748 | | 0.4416 | 29.17 | 7000 | 0.4987 | 0.7510 | 0.751 | | 0.4424 | 30.0 | 7200 | 0.5007 | 0.7536 | 0.754 | | 0.4434 | 30.83 | 7400 | 0.4988 | 0.7569 | 0.757 | | 0.4373 | 31.67 | 7600 | 0.4978 | 0.7580 | 0.758 | | 0.4432 | 32.5 | 7800 | 0.4988 | 0.7540 | 0.754 | | 0.4391 | 33.33 | 8000 | 0.4969 | 0.7550 | 0.755 | | 0.4447 | 34.17 | 8200 | 0.4996 | 0.7589 | 0.759 | | 0.4396 | 35.0 | 8400 | 0.4987 | 0.7609 | 0.761 | | 0.4424 | 35.83 | 8600 | 0.4968 | 0.7550 | 0.755 | | 0.4443 | 36.67 | 8800 | 0.4973 | 0.7568 | 0.757 | | 0.4376 | 37.5 | 9000 | 0.5016 | 0.7495 | 0.75 | | 0.4362 | 38.33 | 9200 | 0.4981 | 0.7570 | 0.757 | | 0.4408 | 39.17 | 9400 | 0.4968 | 0.7570 | 0.757 | | 0.4375 | 40.0 | 9600 | 0.4979 | 0.7579 | 0.758 | | 0.4402 | 40.83 | 9800 | 0.4969 | 0.7540 | 0.754 | | 0.4382 | 41.67 | 10000 | 0.4972 | 0.7590 | 0.759 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_1-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_1-seqsight_65536_512_47M-L8_f

2024-05-03T16:59:12+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_1-seqsight\_65536\_512\_47M-L8\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset. It achieves the following results on the evaluation set: * Loss: 0.3450 * F1 Score: 0.8487 * Accuracy: 0.849 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-classification

transformers

<!-- 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. --> # output_v3 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8916 - Qwk: 0.7949 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9249 | 1.0 | 1731 | 1.0209 | 0.7428 | | 0.8301 | 2.0 | 3462 | 0.8321 | 0.7973 | | 0.7726 | 3.0 | 5193 | 0.9609 | 0.7834 | | 0.7125 | 4.0 | 6924 | 0.8916 | 0.7949 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-v3-small", "model-index": [{"name": "output_v3", "results": []}]}

lemmein/output_v3

2024-05-03T16:59:28+00:00

TAGS #transformers #safetensors #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-small #license-mit #autotrain_compatible #endpoints_compatible #region-us

output\_v3 ========== This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8916 * Qwk: 0.7949 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: 1e-05 * train\_batch\_size: 8 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 4 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.39.3 * Pytorch 2.1.2 * Datasets 2.18.0 * Tokenizers 0.15.2

automatic-speech-recognition

transformers

<!-- 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. --> # Whisper Tunisien This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the comondov dataset. ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 6.6667 | 20 | 10.2887 | 145.3174 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["ar"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Arbi-Houssem/comondov"], "base_model": "openai/whisper-medium", "model-index": [{"name": "Whisper Tunisien", "results": []}]}

Arbi-Houssem/output

2024-05-03T16:59:33+00:00

TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ar #dataset-Arbi-Houssem/comondov #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #region-us

Whisper Tunisien ================ This model is a fine-tuned version of openai/whisper-medium on the comondov dataset. 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: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 20 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.41.0.dev0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

text-classification

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

dabagyan/bert-sarcasm-model-with-context

2024-05-03T16:59:38+00:00

TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-to-audio

transformers

# MusicGen - Large - 3.3B MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - [small](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [**large** (this checkpoint)](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy: ``` pip install --upgrade pip pip install --upgrade transformers scipy ``` 2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code! ```python from transformers import pipeline import scipy synthesiser = pipeline("text-to-audio", "facebook/musicgen-large") music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True}) scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"]) ``` 3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control. ```python from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-large") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(**inputs, max_new_tokens=256) ``` 4. Listen to the audio samples either in an ipynb notebook: ```python from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```python import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("large") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details **Organization developing the model:** The FAIR team of Meta AI. **Model date:** MusicGen was trained between April 2023 and May 2023. **Model version:** This is the version 1 of the model. **Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. **Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284). **Citation details:** ``` @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} } ``` **License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0. **Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use **Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs **Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. **Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics **Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. **Decision thresholds:** Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Evaluation results Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. | Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity | |---|---|---|---|---| | facebook/musicgen-small | 4.88 | 1.42 | 0.27 | - | | facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - | | **facebook/musicgen-large** | 5.48 | 1.37 | 0.28 | - | | facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 | More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section. ## Limitations and biases **Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. **Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). **Limitations:** - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. **Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. **Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. **Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.

{"license": "cc-by-nc-4.0", "tags": ["musicgen"], "inference": true}

karlwennerstrom/text-to-music

2024-05-03T17:00:24+00:00

TAGS #transformers #pytorch #musicgen #text-to-audio #arxiv-2306.05284 #license-cc-by-nc-4.0 #endpoints_compatible #region-us

MusicGen - Large - 3.3B ======================= MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in Simple and Controllable Music Generation by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: * small * medium * large (this checkpoint) * melody Example ------- Try out MusicGen yourself! * Audiocraft Colab: <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/> * Hugging Face Colab: <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/> * Hugging Face Demo: <a target="\_blank" href="URL <img src="URL alt="Open in HuggingFace"/> Transformers Usage ------------------ You can run MusicGen locally with the Transformers library from version 4.31.0 onwards. 1. First install the Transformers library and scipy: 2. Run inference via the 'Text-to-Audio' (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code! 3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control. 4. Listen to the audio samples either in an ipynb notebook: Or save them as a '.wav' file using a third-party library, e.g. 'scipy': For more details on using the MusicGen model for inference using the Transformers library, refer to the MusicGen docs. Audiocraft Usage ---------------- You can also run MusicGen locally through the original Audiocraft library: 1. First install the 'audiocraft' library 2. Make sure to have 'ffmpeg' installed: 3. Run the following Python code: Model details ------------- Organization developing the model: The FAIR team of Meta AI. Model date: MusicGen was trained between April 2023 and May 2023. Model version: This is the version 1 of the model. Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. Paper or resources for more information: More information can be found in the paper Simple and Controllable Music Generation. Citation details: License: Code is released under MIT, model weights are released under CC-BY-NC 4.0. Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the Github repository of the project, or by opening an issue. Intended use ------------ Primary intended use: The primary use of MusicGen is research on AI-based music generation, including: * Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science * Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. Out-of-scope use cases: The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. Metrics ------- Models performance measures: We used the following objective measure to evaluate the model on a standard music benchmark: * Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) * Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) * CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: * Overall quality of the music samples; * Text relevance to the provided text input; * Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. Decision thresholds: Not applicable. Evaluation datasets ------------------- The model was evaluated on the MusicCaps benchmark and on an in-domain held-out evaluation set, with no artist overlap with the training set. Training datasets ----------------- The model was trained on licensed data using the following sources: the Meta Music Initiative Sound Collection, Shutterstock music collection and the Pond5 music collection. See the paper for more details about the training set and corresponding preprocessing. Evaluation results ------------------ Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source Hybrid Transformer for Music Source Separation (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. More information can be found in the paper Simple and Controllable Music Generation, in the Results section. Limitations and biases ---------------------- Data: The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. Mitigations: Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source Hybrid Transformer for Music Source Separation (HT-Demucs). Limitations: * The model is not able to generate realistic vocals. * The model has been trained with English descriptions and will not perform as well in other languages. * The model does not perform equally well for all music styles and cultures. * The model sometimes generates end of songs, collapsing to silence. * It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. Biases: The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. Risks and harms: Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. Use cases: Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.

text-classification

transformers

<!-- 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. --> # fine_tuned_cb_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2169 - Accuracy: 0.3636 - F1: 0.2430 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.7239 | 3.5714 | 50 | 1.2945 | 0.3182 | 0.1536 | | 0.3879 | 7.1429 | 100 | 1.6236 | 0.4545 | 0.4158 | | 0.1546 | 10.7143 | 150 | 3.1975 | 0.3636 | 0.2430 | | 0.0741 | 14.2857 | 200 | 2.9703 | 0.4545 | 0.3895 | | 0.0323 | 17.8571 | 250 | 3.8104 | 0.3636 | 0.2430 | | 0.0073 | 21.4286 | 300 | 4.0583 | 0.3636 | 0.2430 | | 0.0037 | 25.0 | 350 | 4.3166 | 0.3636 | 0.2430 | | 0.0032 | 28.5714 | 400 | 4.2169 | 0.3636 | 0.2430 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "fine_tuned_cb_bert", "results": []}]}

lenatr99/fine_tuned_cb_bert

2024-05-03T17:00:27+00:00

TAGS #transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

fine\_tuned\_cb\_bert ===================== This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 4.2169 * Accuracy: 0.3636 * F1: 0.2430 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: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # GUE_tf_1-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset. It achieves the following results on the evaluation set: - Loss: 0.3560 - F1 Score: 0.8466 - Accuracy: 0.847 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5345 | 0.83 | 200 | 0.5325 | 0.7312 | 0.733 | | 0.4938 | 1.67 | 400 | 0.5185 | 0.7422 | 0.743 | | 0.4863 | 2.5 | 600 | 0.5107 | 0.7550 | 0.755 | | 0.4809 | 3.33 | 800 | 0.5049 | 0.7437 | 0.744 | | 0.4788 | 4.17 | 1000 | 0.5083 | 0.7518 | 0.754 | | 0.4687 | 5.0 | 1200 | 0.5023 | 0.7544 | 0.755 | | 0.4655 | 5.83 | 1400 | 0.4938 | 0.7450 | 0.745 | | 0.463 | 6.67 | 1600 | 0.4967 | 0.7490 | 0.749 | | 0.4618 | 7.5 | 1800 | 0.4922 | 0.7523 | 0.753 | | 0.4539 | 8.33 | 2000 | 0.4933 | 0.7569 | 0.757 | | 0.454 | 9.17 | 2200 | 0.4876 | 0.7560 | 0.756 | | 0.4526 | 10.0 | 2400 | 0.4948 | 0.7604 | 0.761 | | 0.4519 | 10.83 | 2600 | 0.4926 | 0.7617 | 0.763 | | 0.4475 | 11.67 | 2800 | 0.4907 | 0.7559 | 0.756 | | 0.4382 | 12.5 | 3000 | 0.4924 | 0.7630 | 0.763 | | 0.4491 | 13.33 | 3200 | 0.4939 | 0.7457 | 0.746 | | 0.4398 | 14.17 | 3400 | 0.4853 | 0.7516 | 0.752 | | 0.4363 | 15.0 | 3600 | 0.4910 | 0.7672 | 0.768 | | 0.4353 | 15.83 | 3800 | 0.4913 | 0.7627 | 0.763 | | 0.4364 | 16.67 | 4000 | 0.4920 | 0.7656 | 0.766 | | 0.4324 | 17.5 | 4200 | 0.4928 | 0.7567 | 0.757 | | 0.4252 | 18.33 | 4400 | 0.5010 | 0.7638 | 0.764 | | 0.4366 | 19.17 | 4600 | 0.4923 | 0.7638 | 0.764 | | 0.4309 | 20.0 | 4800 | 0.4919 | 0.7610 | 0.761 | | 0.428 | 20.83 | 5000 | 0.4988 | 0.7630 | 0.763 | | 0.4249 | 21.67 | 5200 | 0.4914 | 0.7670 | 0.767 | | 0.421 | 22.5 | 5400 | 0.4998 | 0.7599 | 0.76 | | 0.4217 | 23.33 | 5600 | 0.4969 | 0.7646 | 0.765 | | 0.4248 | 24.17 | 5800 | 0.4990 | 0.7588 | 0.759 | | 0.4222 | 25.0 | 6000 | 0.4928 | 0.7630 | 0.763 | | 0.4194 | 25.83 | 6200 | 0.4907 | 0.7620 | 0.762 | | 0.4159 | 26.67 | 6400 | 0.4950 | 0.7659 | 0.766 | | 0.4183 | 27.5 | 6600 | 0.4966 | 0.7680 | 0.768 | | 0.4134 | 28.33 | 6800 | 0.4951 | 0.7659 | 0.766 | | 0.4152 | 29.17 | 7000 | 0.4956 | 0.7620 | 0.762 | | 0.4143 | 30.0 | 7200 | 0.4943 | 0.7518 | 0.752 | | 0.4141 | 30.83 | 7400 | 0.4967 | 0.7599 | 0.76 | | 0.4063 | 31.67 | 7600 | 0.5028 | 0.7579 | 0.758 | | 0.4144 | 32.5 | 7800 | 0.4986 | 0.7610 | 0.761 | | 0.4087 | 33.33 | 8000 | 0.4979 | 0.7629 | 0.763 | | 0.4125 | 34.17 | 8200 | 0.4999 | 0.7650 | 0.765 | | 0.4084 | 35.0 | 8400 | 0.4981 | 0.7640 | 0.764 | | 0.411 | 35.83 | 8600 | 0.4975 | 0.7580 | 0.758 | | 0.4117 | 36.67 | 8800 | 0.4977 | 0.7570 | 0.757 | | 0.4042 | 37.5 | 9000 | 0.5037 | 0.7567 | 0.757 | | 0.4046 | 38.33 | 9200 | 0.5019 | 0.7620 | 0.762 | | 0.407 | 39.17 | 9400 | 0.5006 | 0.7650 | 0.765 | | 0.404 | 40.0 | 9600 | 0.5043 | 0.7599 | 0.76 | | 0.4041 | 40.83 | 9800 | 0.5028 | 0.7620 | 0.762 | | 0.4037 | 41.67 | 10000 | 0.5027 | 0.7580 | 0.758 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_1-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_1-seqsight_65536_512_47M-L32_f

2024-05-03T17:00:42+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_1-seqsight\_65536\_512\_47M-L32\_f =========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset. It achieves the following results on the evaluation set: * Loss: 0.3560 * F1 Score: 0.8466 * Accuracy: 0.847 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

# Uploaded model - **Developed by:** animaRegem - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"}

animaRegem/gemma-7b-lora-0_1-malayalam

2024-05-03T17:01:22+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #gemma #trl #en #base_model-unsloth/gemma-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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{"library_name": "transformers", "tags": ["unsloth"]}

animaRegem/gemma-2b-lora-0_1-malayalam-tokenizer

2024-05-03T17:01:36+00:00

TAGS #transformers #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us

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peft

<!-- 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. --> # GUE_tf_4-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3592 - F1 Score: 0.8408 - Accuracy: 0.841 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5524 | 1.34 | 200 | 0.5012 | 0.7490 | 0.749 | | 0.4884 | 2.68 | 400 | 0.4884 | 0.7503 | 0.751 | | 0.4809 | 4.03 | 600 | 0.4846 | 0.7535 | 0.754 | | 0.4723 | 5.37 | 800 | 0.4818 | 0.7550 | 0.755 | | 0.4605 | 6.71 | 1000 | 0.4785 | 0.7562 | 0.757 | | 0.4623 | 8.05 | 1200 | 0.4734 | 0.7670 | 0.767 | | 0.458 | 9.4 | 1400 | 0.4741 | 0.7566 | 0.757 | | 0.457 | 10.74 | 1600 | 0.4798 | 0.7559 | 0.757 | | 0.4518 | 12.08 | 1800 | 0.4766 | 0.7597 | 0.761 | | 0.4501 | 13.42 | 2000 | 0.4673 | 0.7566 | 0.757 | | 0.4479 | 14.77 | 2200 | 0.4684 | 0.7640 | 0.764 | | 0.4487 | 16.11 | 2400 | 0.4664 | 0.7636 | 0.764 | | 0.4443 | 17.45 | 2600 | 0.4687 | 0.7640 | 0.764 | | 0.4431 | 18.79 | 2800 | 0.4678 | 0.7610 | 0.761 | | 0.4454 | 20.13 | 3000 | 0.4639 | 0.7580 | 0.758 | | 0.4384 | 21.48 | 3200 | 0.4688 | 0.7618 | 0.762 | | 0.4413 | 22.82 | 3400 | 0.4657 | 0.7669 | 0.767 | | 0.4389 | 24.16 | 3600 | 0.4631 | 0.7620 | 0.762 | | 0.4391 | 25.5 | 3800 | 0.4676 | 0.7645 | 0.765 | | 0.4374 | 26.85 | 4000 | 0.4624 | 0.7710 | 0.771 | | 0.436 | 28.19 | 4200 | 0.4631 | 0.7660 | 0.766 | | 0.434 | 29.53 | 4400 | 0.4614 | 0.7630 | 0.763 | | 0.4349 | 30.87 | 4600 | 0.4602 | 0.7679 | 0.768 | | 0.4348 | 32.21 | 4800 | 0.4602 | 0.7670 | 0.767 | | 0.43 | 33.56 | 5000 | 0.4626 | 0.7647 | 0.765 | | 0.4317 | 34.9 | 5200 | 0.4601 | 0.7700 | 0.77 | | 0.4345 | 36.24 | 5400 | 0.4570 | 0.7680 | 0.768 | | 0.4285 | 37.58 | 5600 | 0.4581 | 0.7670 | 0.767 | | 0.4292 | 38.93 | 5800 | 0.4563 | 0.7650 | 0.765 | | 0.4294 | 40.27 | 6000 | 0.4574 | 0.7650 | 0.765 | | 0.4272 | 41.61 | 6200 | 0.4580 | 0.7678 | 0.768 | | 0.4283 | 42.95 | 6400 | 0.4558 | 0.7670 | 0.767 | | 0.4296 | 44.3 | 6600 | 0.4553 | 0.7690 | 0.769 | | 0.4236 | 45.64 | 6800 | 0.4552 | 0.7700 | 0.77 | | 0.4276 | 46.98 | 7000 | 0.4557 | 0.7670 | 0.767 | | 0.4287 | 48.32 | 7200 | 0.4534 | 0.7670 | 0.767 | | 0.4249 | 49.66 | 7400 | 0.4563 | 0.7678 | 0.768 | | 0.4235 | 51.01 | 7600 | 0.4532 | 0.7640 | 0.764 | | 0.4265 | 52.35 | 7800 | 0.4539 | 0.7630 | 0.763 | | 0.4211 | 53.69 | 8000 | 0.4534 | 0.7720 | 0.772 | | 0.4253 | 55.03 | 8200 | 0.4546 | 0.7770 | 0.777 | | 0.4232 | 56.38 | 8400 | 0.4547 | 0.7710 | 0.771 | | 0.4248 | 57.72 | 8600 | 0.4541 | 0.7697 | 0.77 | | 0.4218 | 59.06 | 8800 | 0.4536 | 0.7710 | 0.771 | | 0.4235 | 60.4 | 9000 | 0.4524 | 0.7710 | 0.771 | | 0.4232 | 61.74 | 9200 | 0.4526 | 0.7699 | 0.77 | | 0.4238 | 63.09 | 9400 | 0.4524 | 0.7710 | 0.771 | | 0.4265 | 64.43 | 9600 | 0.4520 | 0.7730 | 0.773 | | 0.4192 | 65.77 | 9800 | 0.4526 | 0.7710 | 0.771 | | 0.4209 | 67.11 | 10000 | 0.4525 | 0.7710 | 0.771 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_4-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_65536_512_47M-L1_f

2024-05-03T17:03:11+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_4-seqsight\_65536\_512\_47M-L1\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset. It achieves the following results on the evaluation set: * Loss: 0.3592 * F1 Score: 0.8408 * Accuracy: 0.841 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

# Uploaded model - **Developed by:** xsa-dev - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

xsa-dev/hugs_llama3_technique_ft_16bit_GGUF_1

2024-05-03T17:03:18+00:00

TAGS #transformers #text-generation-inference #unsloth #llama #gguf #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: xsa-dev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

peft

<!-- 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. --> # GUE_tf_4-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3946 - F1 Score: 0.8378 - Accuracy: 0.838 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5197 | 1.34 | 200 | 0.4804 | 0.7559 | 0.756 | | 0.4632 | 2.68 | 400 | 0.4707 | 0.7573 | 0.758 | | 0.451 | 4.03 | 600 | 0.4674 | 0.7706 | 0.771 | | 0.4386 | 5.37 | 800 | 0.4713 | 0.7608 | 0.761 | | 0.4245 | 6.71 | 1000 | 0.4641 | 0.7704 | 0.771 | | 0.4206 | 8.05 | 1200 | 0.4561 | 0.7650 | 0.765 | | 0.4135 | 9.4 | 1400 | 0.4505 | 0.7729 | 0.773 | | 0.4101 | 10.74 | 1600 | 0.4429 | 0.7760 | 0.776 | | 0.398 | 12.08 | 1800 | 0.4503 | 0.7834 | 0.785 | | 0.3924 | 13.42 | 2000 | 0.4314 | 0.7789 | 0.779 | | 0.3862 | 14.77 | 2200 | 0.4378 | 0.7790 | 0.779 | | 0.3818 | 16.11 | 2400 | 0.4344 | 0.7856 | 0.786 | | 0.37 | 17.45 | 2600 | 0.4382 | 0.7819 | 0.782 | | 0.3673 | 18.79 | 2800 | 0.4382 | 0.7930 | 0.793 | | 0.3668 | 20.13 | 3000 | 0.4375 | 0.7919 | 0.792 | | 0.355 | 21.48 | 3200 | 0.4364 | 0.8042 | 0.805 | | 0.3526 | 22.82 | 3400 | 0.4336 | 0.8015 | 0.802 | | 0.3472 | 24.16 | 3600 | 0.4297 | 0.8036 | 0.804 | | 0.3397 | 25.5 | 3800 | 0.4356 | 0.8021 | 0.803 | | 0.3336 | 26.85 | 4000 | 0.4270 | 0.8070 | 0.807 | | 0.3311 | 28.19 | 4200 | 0.4383 | 0.8111 | 0.812 | | 0.3216 | 29.53 | 4400 | 0.4312 | 0.8140 | 0.814 | | 0.3223 | 30.87 | 4600 | 0.4287 | 0.8110 | 0.811 | | 0.3171 | 32.21 | 4800 | 0.4274 | 0.8198 | 0.82 | | 0.3087 | 33.56 | 5000 | 0.4340 | 0.8119 | 0.812 | | 0.3112 | 34.9 | 5200 | 0.4324 | 0.8200 | 0.82 | | 0.3074 | 36.24 | 5400 | 0.4328 | 0.8227 | 0.823 | | 0.3009 | 37.58 | 5600 | 0.4299 | 0.8179 | 0.818 | | 0.295 | 38.93 | 5800 | 0.4297 | 0.8229 | 0.823 | | 0.2955 | 40.27 | 6000 | 0.4356 | 0.8257 | 0.826 | | 0.291 | 41.61 | 6200 | 0.4261 | 0.8248 | 0.825 | | 0.2879 | 42.95 | 6400 | 0.4289 | 0.8180 | 0.818 | | 0.2859 | 44.3 | 6600 | 0.4275 | 0.8246 | 0.825 | | 0.2799 | 45.64 | 6800 | 0.4301 | 0.8209 | 0.821 | | 0.2806 | 46.98 | 7000 | 0.4298 | 0.8258 | 0.826 | | 0.28 | 48.32 | 7200 | 0.4359 | 0.8283 | 0.829 | | 0.2787 | 49.66 | 7400 | 0.4247 | 0.8276 | 0.828 | | 0.2715 | 51.01 | 7600 | 0.4292 | 0.8298 | 0.83 | | 0.2738 | 52.35 | 7800 | 0.4339 | 0.8294 | 0.83 | | 0.2676 | 53.69 | 8000 | 0.4320 | 0.8257 | 0.826 | | 0.2698 | 55.03 | 8200 | 0.4308 | 0.8289 | 0.829 | | 0.2661 | 56.38 | 8400 | 0.4333 | 0.8297 | 0.83 | | 0.2659 | 57.72 | 8600 | 0.4364 | 0.8286 | 0.829 | | 0.265 | 59.06 | 8800 | 0.4285 | 0.8267 | 0.827 | | 0.2613 | 60.4 | 9000 | 0.4340 | 0.8297 | 0.83 | | 0.2622 | 61.74 | 9200 | 0.4372 | 0.8294 | 0.83 | | 0.259 | 63.09 | 9400 | 0.4359 | 0.8346 | 0.835 | | 0.2587 | 64.43 | 9600 | 0.4384 | 0.8324 | 0.833 | | 0.2568 | 65.77 | 9800 | 0.4364 | 0.8326 | 0.833 | | 0.2581 | 67.11 | 10000 | 0.4376 | 0.8325 | 0.833 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_4-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_65536_512_47M-L32_f

2024-05-03T17:03:56+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_4-seqsight\_65536\_512\_47M-L32\_f =========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset. It achieves the following results on the evaluation set: * Loss: 0.3946 * F1 Score: 0.8378 * Accuracy: 0.838 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_tf_4-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3586 - F1 Score: 0.8414 - Accuracy: 0.842 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5313 | 1.34 | 200 | 0.4867 | 0.7530 | 0.753 | | 0.4729 | 2.68 | 400 | 0.4783 | 0.7539 | 0.755 | | 0.4632 | 4.03 | 600 | 0.4764 | 0.7586 | 0.76 | | 0.4539 | 5.37 | 800 | 0.4722 | 0.7579 | 0.758 | | 0.4421 | 6.71 | 1000 | 0.4692 | 0.7671 | 0.768 | | 0.4418 | 8.05 | 1200 | 0.4633 | 0.7627 | 0.763 | | 0.4376 | 9.4 | 1400 | 0.4623 | 0.7610 | 0.761 | | 0.437 | 10.74 | 1600 | 0.4580 | 0.7719 | 0.772 | | 0.4274 | 12.08 | 1800 | 0.4650 | 0.7677 | 0.769 | | 0.4248 | 13.42 | 2000 | 0.4510 | 0.7720 | 0.772 | | 0.4224 | 14.77 | 2200 | 0.4550 | 0.7700 | 0.77 | | 0.4205 | 16.11 | 2400 | 0.4479 | 0.7729 | 0.773 | | 0.4143 | 17.45 | 2600 | 0.4532 | 0.7680 | 0.768 | | 0.413 | 18.79 | 2800 | 0.4500 | 0.7770 | 0.777 | | 0.4137 | 20.13 | 3000 | 0.4524 | 0.7658 | 0.766 | | 0.4041 | 21.48 | 3200 | 0.4516 | 0.7626 | 0.763 | | 0.4082 | 22.82 | 3400 | 0.4464 | 0.7708 | 0.771 | | 0.4037 | 24.16 | 3600 | 0.4444 | 0.7718 | 0.772 | | 0.4025 | 25.5 | 3800 | 0.4515 | 0.7690 | 0.77 | | 0.3983 | 26.85 | 4000 | 0.4446 | 0.7769 | 0.777 | | 0.3976 | 28.19 | 4200 | 0.4387 | 0.7738 | 0.774 | | 0.3931 | 29.53 | 4400 | 0.4395 | 0.7800 | 0.78 | | 0.3931 | 30.87 | 4600 | 0.4362 | 0.7789 | 0.779 | | 0.393 | 32.21 | 4800 | 0.4352 | 0.7820 | 0.782 | | 0.3884 | 33.56 | 5000 | 0.4389 | 0.7770 | 0.777 | | 0.3885 | 34.9 | 5200 | 0.4355 | 0.7770 | 0.777 | | 0.3895 | 36.24 | 5400 | 0.4320 | 0.7809 | 0.781 | | 0.382 | 37.58 | 5600 | 0.4337 | 0.7840 | 0.784 | | 0.3804 | 38.93 | 5800 | 0.4337 | 0.7840 | 0.784 | | 0.3816 | 40.27 | 6000 | 0.4326 | 0.7879 | 0.788 | | 0.3756 | 41.61 | 6200 | 0.4336 | 0.7950 | 0.795 | | 0.3769 | 42.95 | 6400 | 0.4329 | 0.7850 | 0.785 | | 0.3767 | 44.3 | 6600 | 0.4299 | 0.7939 | 0.794 | | 0.3706 | 45.64 | 6800 | 0.4318 | 0.7890 | 0.789 | | 0.3749 | 46.98 | 7000 | 0.4320 | 0.7910 | 0.791 | | 0.3776 | 48.32 | 7200 | 0.4268 | 0.7909 | 0.791 | | 0.3712 | 49.66 | 7400 | 0.4277 | 0.7920 | 0.792 | | 0.3688 | 51.01 | 7600 | 0.4292 | 0.7930 | 0.793 | | 0.3726 | 52.35 | 7800 | 0.4302 | 0.7919 | 0.792 | | 0.367 | 53.69 | 8000 | 0.4283 | 0.7950 | 0.795 | | 0.3693 | 55.03 | 8200 | 0.4328 | 0.7920 | 0.792 | | 0.3686 | 56.38 | 8400 | 0.4288 | 0.7940 | 0.794 | | 0.3668 | 57.72 | 8600 | 0.4300 | 0.7958 | 0.796 | | 0.3645 | 59.06 | 8800 | 0.4292 | 0.7930 | 0.793 | | 0.3665 | 60.4 | 9000 | 0.4279 | 0.7900 | 0.79 | | 0.3669 | 61.74 | 9200 | 0.4286 | 0.7909 | 0.791 | | 0.3658 | 63.09 | 9400 | 0.4284 | 0.7920 | 0.792 | | 0.3654 | 64.43 | 9600 | 0.4283 | 0.7929 | 0.793 | | 0.3628 | 65.77 | 9800 | 0.4286 | 0.7920 | 0.792 | | 0.3624 | 67.11 | 10000 | 0.4286 | 0.7910 | 0.791 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_4-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_65536_512_47M-L8_f

2024-05-03T17:03:56+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_4-seqsight\_65536\_512\_47M-L8\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset. It achieves the following results on the evaluation set: * Loss: 0.3586 * F1 Score: 0.8414 * Accuracy: 0.842 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_tf_3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5577 - F1 Score: 0.7107 - Accuracy: 0.712 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6292 | 0.93 | 200 | 0.5841 | 0.6911 | 0.691 | | 0.6031 | 1.87 | 400 | 0.5730 | 0.6984 | 0.699 | | 0.598 | 2.8 | 600 | 0.5653 | 0.7042 | 0.708 | | 0.5917 | 3.74 | 800 | 0.5626 | 0.7055 | 0.706 | | 0.5892 | 4.67 | 1000 | 0.5581 | 0.7149 | 0.717 | | 0.5869 | 5.61 | 1200 | 0.5559 | 0.7156 | 0.717 | | 0.583 | 6.54 | 1400 | 0.5525 | 0.7224 | 0.725 | | 0.5847 | 7.48 | 1600 | 0.5568 | 0.7131 | 0.713 | | 0.5835 | 8.41 | 1800 | 0.5518 | 0.7112 | 0.712 | | 0.5863 | 9.35 | 2000 | 0.5531 | 0.7186 | 0.719 | | 0.5804 | 10.28 | 2200 | 0.5613 | 0.6986 | 0.699 | | 0.5786 | 11.21 | 2400 | 0.5500 | 0.7256 | 0.727 | | 0.5795 | 12.15 | 2600 | 0.5485 | 0.7174 | 0.719 | | 0.5781 | 13.08 | 2800 | 0.5472 | 0.7237 | 0.726 | | 0.577 | 14.02 | 3000 | 0.5497 | 0.7154 | 0.716 | | 0.5776 | 14.95 | 3200 | 0.5473 | 0.7127 | 0.714 | | 0.5774 | 15.89 | 3400 | 0.5464 | 0.7134 | 0.715 | | 0.5741 | 16.82 | 3600 | 0.5471 | 0.7119 | 0.713 | | 0.5733 | 17.76 | 3800 | 0.5490 | 0.7141 | 0.715 | | 0.5749 | 18.69 | 4000 | 0.5510 | 0.7167 | 0.717 | | 0.5727 | 19.63 | 4200 | 0.5438 | 0.7212 | 0.724 | | 0.5754 | 20.56 | 4400 | 0.5446 | 0.7156 | 0.717 | | 0.5712 | 21.5 | 4600 | 0.5517 | 0.7121 | 0.712 | | 0.5709 | 22.43 | 4800 | 0.5448 | 0.7250 | 0.726 | | 0.5744 | 23.36 | 5000 | 0.5475 | 0.7176 | 0.718 | | 0.5717 | 24.3 | 5200 | 0.5508 | 0.7131 | 0.713 | | 0.5699 | 25.23 | 5400 | 0.5450 | 0.7226 | 0.724 | | 0.5734 | 26.17 | 5600 | 0.5457 | 0.7183 | 0.719 | | 0.5695 | 27.1 | 5800 | 0.5439 | 0.7183 | 0.72 | | 0.569 | 28.04 | 6000 | 0.5439 | 0.7221 | 0.723 | | 0.568 | 28.97 | 6200 | 0.5522 | 0.7059 | 0.706 | | 0.572 | 29.91 | 6400 | 0.5458 | 0.7225 | 0.723 | | 0.5703 | 30.84 | 6600 | 0.5456 | 0.7164 | 0.717 | | 0.5681 | 31.78 | 6800 | 0.5452 | 0.7238 | 0.724 | | 0.5679 | 32.71 | 7000 | 0.5425 | 0.7241 | 0.725 | | 0.572 | 33.64 | 7200 | 0.5433 | 0.7218 | 0.723 | | 0.5652 | 34.58 | 7400 | 0.5510 | 0.7109 | 0.711 | | 0.5702 | 35.51 | 7600 | 0.5463 | 0.7180 | 0.718 | | 0.5678 | 36.45 | 7800 | 0.5453 | 0.7268 | 0.727 | | 0.5686 | 37.38 | 8000 | 0.5444 | 0.7207 | 0.721 | | 0.5625 | 38.32 | 8200 | 0.5423 | 0.7175 | 0.719 | | 0.5671 | 39.25 | 8400 | 0.5440 | 0.7212 | 0.722 | | 0.5668 | 40.19 | 8600 | 0.5440 | 0.7233 | 0.724 | | 0.5653 | 41.12 | 8800 | 0.5445 | 0.7244 | 0.725 | | 0.567 | 42.06 | 9000 | 0.5445 | 0.7285 | 0.729 | | 0.566 | 42.99 | 9200 | 0.5456 | 0.7228 | 0.723 | | 0.5676 | 43.93 | 9400 | 0.5465 | 0.7229 | 0.723 | | 0.5667 | 44.86 | 9600 | 0.5445 | 0.7276 | 0.728 | | 0.5662 | 45.79 | 9800 | 0.5445 | 0.7286 | 0.729 | | 0.5634 | 46.73 | 10000 | 0.5447 | 0.7266 | 0.727 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_3-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_65536_512_47M-L1_f

2024-05-03T17:04:22+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_3-seqsight\_65536\_512\_47M-L1\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5577 * F1 Score: 0.7107 * Accuracy: 0.712 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_tf_3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5490 - F1 Score: 0.6962 - Accuracy: 0.698 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6208 | 0.93 | 200 | 0.5737 | 0.6929 | 0.693 | | 0.5963 | 1.87 | 400 | 0.5696 | 0.6930 | 0.693 | | 0.5907 | 2.8 | 600 | 0.5576 | 0.7162 | 0.718 | | 0.5842 | 3.74 | 800 | 0.5619 | 0.7001 | 0.7 | | 0.5822 | 4.67 | 1000 | 0.5538 | 0.7155 | 0.716 | | 0.5791 | 5.61 | 1200 | 0.5470 | 0.7250 | 0.727 | | 0.5749 | 6.54 | 1400 | 0.5498 | 0.7267 | 0.728 | | 0.5747 | 7.48 | 1600 | 0.5501 | 0.7191 | 0.719 | | 0.573 | 8.41 | 1800 | 0.5464 | 0.7147 | 0.715 | | 0.5762 | 9.35 | 2000 | 0.5457 | 0.7265 | 0.728 | | 0.5689 | 10.28 | 2200 | 0.5498 | 0.7169 | 0.717 | | 0.5662 | 11.21 | 2400 | 0.5440 | 0.7234 | 0.725 | | 0.5668 | 12.15 | 2600 | 0.5410 | 0.7185 | 0.721 | | 0.5634 | 13.08 | 2800 | 0.5422 | 0.7176 | 0.722 | | 0.5631 | 14.02 | 3000 | 0.5416 | 0.7290 | 0.73 | | 0.5618 | 14.95 | 3200 | 0.5383 | 0.7208 | 0.724 | | 0.5617 | 15.89 | 3400 | 0.5381 | 0.7291 | 0.731 | | 0.5597 | 16.82 | 3600 | 0.5400 | 0.7295 | 0.731 | | 0.5567 | 17.76 | 3800 | 0.5420 | 0.7249 | 0.727 | | 0.558 | 18.69 | 4000 | 0.5463 | 0.7289 | 0.729 | | 0.5563 | 19.63 | 4200 | 0.5375 | 0.7251 | 0.728 | | 0.5584 | 20.56 | 4400 | 0.5381 | 0.7264 | 0.728 | | 0.5523 | 21.5 | 4600 | 0.5479 | 0.7140 | 0.714 | | 0.5526 | 22.43 | 4800 | 0.5387 | 0.7275 | 0.729 | | 0.5567 | 23.36 | 5000 | 0.5453 | 0.7251 | 0.725 | | 0.551 | 24.3 | 5200 | 0.5539 | 0.7054 | 0.706 | | 0.5498 | 25.23 | 5400 | 0.5404 | 0.7268 | 0.729 | | 0.5545 | 26.17 | 5600 | 0.5407 | 0.7299 | 0.731 | | 0.5489 | 27.1 | 5800 | 0.5393 | 0.7272 | 0.728 | | 0.5478 | 28.04 | 6000 | 0.5395 | 0.7292 | 0.73 | | 0.5469 | 28.97 | 6200 | 0.5465 | 0.7191 | 0.719 | | 0.5509 | 29.91 | 6400 | 0.5414 | 0.7290 | 0.73 | | 0.5488 | 30.84 | 6600 | 0.5385 | 0.7241 | 0.725 | | 0.5459 | 31.78 | 6800 | 0.5413 | 0.7247 | 0.725 | | 0.5463 | 32.71 | 7000 | 0.5390 | 0.7283 | 0.729 | | 0.5501 | 33.64 | 7200 | 0.5389 | 0.7248 | 0.726 | | 0.5427 | 34.58 | 7400 | 0.5485 | 0.7079 | 0.708 | | 0.5464 | 35.51 | 7600 | 0.5422 | 0.7220 | 0.722 | | 0.5448 | 36.45 | 7800 | 0.5403 | 0.7304 | 0.731 | | 0.5453 | 37.38 | 8000 | 0.5399 | 0.7252 | 0.726 | | 0.5374 | 38.32 | 8200 | 0.5403 | 0.7270 | 0.728 | | 0.5424 | 39.25 | 8400 | 0.5400 | 0.7282 | 0.729 | | 0.5439 | 40.19 | 8600 | 0.5402 | 0.7264 | 0.727 | | 0.541 | 41.12 | 8800 | 0.5409 | 0.7264 | 0.727 | | 0.5428 | 42.06 | 9000 | 0.5407 | 0.7272 | 0.728 | | 0.5414 | 42.99 | 9200 | 0.5419 | 0.7248 | 0.725 | | 0.5432 | 43.93 | 9400 | 0.5418 | 0.7238 | 0.724 | | 0.5424 | 44.86 | 9600 | 0.5401 | 0.7255 | 0.726 | | 0.5406 | 45.79 | 9800 | 0.5407 | 0.7264 | 0.727 | | 0.5384 | 46.73 | 10000 | 0.5410 | 0.7245 | 0.725 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_3-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_65536_512_47M-L8_f

2024-05-03T17:05:07+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_3-seqsight\_65536\_512\_47M-L8\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5490 * F1 Score: 0.6962 * Accuracy: 0.698 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GritLM-7B - bnb 4bits - Model creator: https://huggingface.co/GritLM/ - Original model: https://huggingface.co/GritLM/GritLM-7B/ Original model description: --- pipeline_tag: text-generation inference: true license: apache-2.0 datasets: - GritLM/tulu2 tags: - mteb model-index: - name: GritLM-7B results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.478 - type: map_at_10 value: 54.955 - type: map_at_100 value: 54.955 - type: map_at_1000 value: 54.955 - type: map_at_3 value: 50.888999999999996 - type: map_at_5 value: 53.349999999999994 - type: mrr_at_1 value: 39.757999999999996 - type: mrr_at_10 value: 55.449000000000005 - type: mrr_at_100 value: 55.449000000000005 - type: mrr_at_1000 value: 55.449000000000005 - type: mrr_at_3 value: 51.37500000000001 - type: mrr_at_5 value: 53.822 - type: ndcg_at_1 value: 38.478 - type: ndcg_at_10 value: 63.239999999999995 - type: ndcg_at_100 value: 63.239999999999995 - type: ndcg_at_1000 value: 63.239999999999995 - type: ndcg_at_3 value: 54.935 - type: ndcg_at_5 value: 59.379000000000005 - type: precision_at_1 value: 38.478 - type: precision_at_10 value: 8.933 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 22.214 - type: precision_at_5 value: 15.491 - type: recall_at_1 value: 38.478 - type: recall_at_10 value: 89.331 - type: recall_at_100 value: 89.331 - type: recall_at_1000 value: 89.331 - type: recall_at_3 value: 66.643 - type: recall_at_5 value: 77.45400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 51.67144081472449 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 48.11256154264126 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.1935203751726 - type: cos_sim_spearman value: 86.35497970498659 - type: euclidean_pearson value: 85.46910708503744 - type: euclidean_spearman value: 85.13928935405485 - type: manhattan_pearson value: 85.68373836333303 - type: manhattan_spearman value: 85.40013867117746 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.86793640310432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 39.80291334130727 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.421 - type: map_at_10 value: 52.349000000000004 - type: map_at_100 value: 52.349000000000004 - type: map_at_1000 value: 52.349000000000004 - type: map_at_3 value: 48.17 - type: map_at_5 value: 50.432 - type: mrr_at_1 value: 47.353 - type: mrr_at_10 value: 58.387 - type: mrr_at_100 value: 58.387 - type: mrr_at_1000 value: 58.387 - type: mrr_at_3 value: 56.199 - type: mrr_at_5 value: 57.487 - type: ndcg_at_1 value: 47.353 - type: ndcg_at_10 value: 59.202 - type: ndcg_at_100 value: 58.848 - type: ndcg_at_1000 value: 58.831999999999994 - type: ndcg_at_3 value: 54.112 - type: ndcg_at_5 value: 56.312 - type: precision_at_1 value: 47.353 - type: precision_at_10 value: 11.459 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 26.133 - type: precision_at_5 value: 18.627 - type: recall_at_1 value: 38.421 - type: recall_at_10 value: 71.89 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 71.89 - type: recall_at_3 value: 56.58 - type: recall_at_5 value: 63.125 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.025999999999996 - type: map_at_10 value: 50.590999999999994 - type: map_at_100 value: 51.99700000000001 - type: map_at_1000 value: 52.11599999999999 - type: map_at_3 value: 47.435 - type: map_at_5 value: 49.236000000000004 - type: mrr_at_1 value: 48.28 - type: mrr_at_10 value: 56.814 - type: mrr_at_100 value: 57.446 - type: mrr_at_1000 value: 57.476000000000006 - type: mrr_at_3 value: 54.958 - type: mrr_at_5 value: 56.084999999999994 - type: ndcg_at_1 value: 48.28 - type: ndcg_at_10 value: 56.442 - type: ndcg_at_100 value: 60.651999999999994 - type: ndcg_at_1000 value: 62.187000000000005 - type: ndcg_at_3 value: 52.866 - type: ndcg_at_5 value: 54.515 - type: precision_at_1 value: 48.28 - type: precision_at_10 value: 10.586 - type: precision_at_100 value: 1.6310000000000002 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 25.945 - type: precision_at_5 value: 18.076 - type: recall_at_1 value: 38.025999999999996 - type: recall_at_10 value: 66.11399999999999 - type: recall_at_100 value: 83.339 - type: recall_at_1000 value: 92.413 - type: recall_at_3 value: 54.493 - type: recall_at_5 value: 59.64699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 47.905 - type: map_at_10 value: 61.58 - type: map_at_100 value: 62.605 - type: map_at_1000 value: 62.637 - type: map_at_3 value: 58.074000000000005 - type: map_at_5 value: 60.260000000000005 - type: mrr_at_1 value: 54.42 - type: mrr_at_10 value: 64.847 - type: mrr_at_100 value: 65.403 - type: mrr_at_1000 value: 65.41900000000001 - type: mrr_at_3 value: 62.675000000000004 - type: mrr_at_5 value: 64.101 - type: ndcg_at_1 value: 54.42 - type: ndcg_at_10 value: 67.394 - type: ndcg_at_100 value: 70.846 - type: ndcg_at_1000 value: 71.403 - type: ndcg_at_3 value: 62.025 - type: ndcg_at_5 value: 65.032 - type: precision_at_1 value: 54.42 - type: precision_at_10 value: 10.646 - type: precision_at_100 value: 1.325 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 27.398 - type: precision_at_5 value: 18.796 - type: recall_at_1 value: 47.905 - type: recall_at_10 value: 80.84599999999999 - type: recall_at_100 value: 95.078 - type: recall_at_1000 value: 98.878 - type: recall_at_3 value: 67.05600000000001 - type: recall_at_5 value: 74.261 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.745 - type: map_at_10 value: 41.021 - type: map_at_100 value: 41.021 - type: map_at_1000 value: 41.021 - type: map_at_3 value: 37.714999999999996 - type: map_at_5 value: 39.766 - type: mrr_at_1 value: 33.559 - type: mrr_at_10 value: 43.537 - type: mrr_at_100 value: 43.537 - type: mrr_at_1000 value: 43.537 - type: mrr_at_3 value: 40.546 - type: mrr_at_5 value: 42.439 - type: ndcg_at_1 value: 33.559 - type: ndcg_at_10 value: 46.781 - type: ndcg_at_100 value: 46.781 - type: ndcg_at_1000 value: 46.781 - type: ndcg_at_3 value: 40.516000000000005 - type: ndcg_at_5 value: 43.957 - type: precision_at_1 value: 33.559 - type: precision_at_10 value: 7.198 - type: precision_at_100 value: 0.72 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 17.1 - type: precision_at_5 value: 12.316 - type: recall_at_1 value: 30.745 - type: recall_at_10 value: 62.038000000000004 - type: recall_at_100 value: 62.038000000000004 - type: recall_at_1000 value: 62.038000000000004 - type: recall_at_3 value: 45.378 - type: recall_at_5 value: 53.580000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.637999999999998 - type: map_at_10 value: 31.05 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.05 - type: map_at_3 value: 27.628000000000004 - type: map_at_5 value: 29.767 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 36.131 - type: mrr_at_100 value: 36.131 - type: mrr_at_1000 value: 36.131 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 35.143 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 37.478 - type: ndcg_at_100 value: 37.469 - type: ndcg_at_1000 value: 37.469 - type: ndcg_at_3 value: 31.757999999999996 - type: ndcg_at_5 value: 34.821999999999996 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.188999999999999 - type: precision_at_100 value: 0.719 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 15.837000000000002 - type: precision_at_5 value: 11.841 - type: recall_at_1 value: 19.637999999999998 - type: recall_at_10 value: 51.836000000000006 - type: recall_at_100 value: 51.836000000000006 - type: recall_at_1000 value: 51.836000000000006 - type: recall_at_3 value: 36.384 - type: recall_at_5 value: 43.964 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 34.884 - type: map_at_10 value: 47.88 - type: map_at_100 value: 47.88 - type: map_at_1000 value: 47.88 - type: map_at_3 value: 43.85 - type: map_at_5 value: 46.414 - type: mrr_at_1 value: 43.022 - type: mrr_at_10 value: 53.569 - type: mrr_at_100 value: 53.569 - type: mrr_at_1000 value: 53.569 - type: mrr_at_3 value: 51.075 - type: mrr_at_5 value: 52.725 - type: ndcg_at_1 value: 43.022 - type: ndcg_at_10 value: 54.461000000000006 - type: ndcg_at_100 value: 54.388000000000005 - type: ndcg_at_1000 value: 54.388000000000005 - type: ndcg_at_3 value: 48.864999999999995 - type: ndcg_at_5 value: 52.032000000000004 - type: precision_at_1 value: 43.022 - type: precision_at_10 value: 9.885 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 23.612 - type: precision_at_5 value: 16.997 - type: recall_at_1 value: 34.884 - type: recall_at_10 value: 68.12899999999999 - type: recall_at_100 value: 68.12899999999999 - type: recall_at_1000 value: 68.12899999999999 - type: recall_at_3 value: 52.428 - type: recall_at_5 value: 60.662000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.588 - type: map_at_10 value: 43.85 - type: map_at_100 value: 45.317 - type: map_at_1000 value: 45.408 - type: map_at_3 value: 39.73 - type: map_at_5 value: 42.122 - type: mrr_at_1 value: 38.927 - type: mrr_at_10 value: 49.582 - type: mrr_at_100 value: 50.39 - type: mrr_at_1000 value: 50.426 - type: mrr_at_3 value: 46.518 - type: mrr_at_5 value: 48.271 - type: ndcg_at_1 value: 38.927 - type: ndcg_at_10 value: 50.605999999999995 - type: ndcg_at_100 value: 56.22200000000001 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 44.232 - type: ndcg_at_5 value: 47.233999999999995 - type: precision_at_1 value: 38.927 - type: precision_at_10 value: 9.429 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 21.271 - type: precision_at_5 value: 15.434000000000001 - type: recall_at_1 value: 31.588 - type: recall_at_10 value: 64.836 - type: recall_at_100 value: 88.066 - type: recall_at_1000 value: 97.748 - type: recall_at_3 value: 47.128 - type: recall_at_5 value: 54.954 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.956083333333336 - type: map_at_10 value: 43.33483333333333 - type: map_at_100 value: 44.64883333333333 - type: map_at_1000 value: 44.75 - type: map_at_3 value: 39.87741666666666 - type: map_at_5 value: 41.86766666666667 - type: mrr_at_1 value: 38.06341666666667 - type: mrr_at_10 value: 47.839666666666666 - type: mrr_at_100 value: 48.644000000000005 - type: mrr_at_1000 value: 48.68566666666667 - type: mrr_at_3 value: 45.26358333333334 - type: mrr_at_5 value: 46.790000000000006 - type: ndcg_at_1 value: 38.06341666666667 - type: ndcg_at_10 value: 49.419333333333334 - type: ndcg_at_100 value: 54.50166666666667 - type: ndcg_at_1000 value: 56.161166666666674 - type: ndcg_at_3 value: 43.982416666666666 - type: ndcg_at_5 value: 46.638083333333334 - type: precision_at_1 value: 38.06341666666667 - type: precision_at_10 value: 8.70858333333333 - type: precision_at_100 value: 1.327 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.37816666666667 - type: precision_at_5 value: 14.516333333333334 - type: recall_at_1 value: 31.956083333333336 - type: recall_at_10 value: 62.69458333333334 - type: recall_at_100 value: 84.46433333333334 - type: recall_at_1000 value: 95.58449999999999 - type: recall_at_3 value: 47.52016666666666 - type: recall_at_5 value: 54.36066666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.912 - type: map_at_10 value: 38.291 - type: map_at_100 value: 39.44 - type: map_at_1000 value: 39.528 - type: map_at_3 value: 35.638 - type: map_at_5 value: 37.218 - type: mrr_at_1 value: 32.822 - type: mrr_at_10 value: 41.661 - type: mrr_at_100 value: 42.546 - type: mrr_at_1000 value: 42.603 - type: mrr_at_3 value: 39.238 - type: mrr_at_5 value: 40.726 - type: ndcg_at_1 value: 32.822 - type: ndcg_at_10 value: 43.373 - type: ndcg_at_100 value: 48.638 - type: ndcg_at_1000 value: 50.654999999999994 - type: ndcg_at_3 value: 38.643 - type: ndcg_at_5 value: 41.126000000000005 - type: precision_at_1 value: 32.822 - type: precision_at_10 value: 6.8709999999999996 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.82 - type: precision_at_5 value: 11.718 - type: recall_at_1 value: 28.912 - type: recall_at_10 value: 55.376999999999995 - type: recall_at_100 value: 79.066 - type: recall_at_1000 value: 93.664 - type: recall_at_3 value: 42.569 - type: recall_at_5 value: 48.719 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.181 - type: map_at_10 value: 31.462 - type: map_at_100 value: 32.73 - type: map_at_1000 value: 32.848 - type: map_at_3 value: 28.57 - type: map_at_5 value: 30.182 - type: mrr_at_1 value: 27.185 - type: mrr_at_10 value: 35.846000000000004 - type: mrr_at_100 value: 36.811 - type: mrr_at_1000 value: 36.873 - type: mrr_at_3 value: 33.437 - type: mrr_at_5 value: 34.813 - type: ndcg_at_1 value: 27.185 - type: ndcg_at_10 value: 36.858000000000004 - type: ndcg_at_100 value: 42.501 - type: ndcg_at_1000 value: 44.945 - type: ndcg_at_3 value: 32.066 - type: ndcg_at_5 value: 34.29 - type: precision_at_1 value: 27.185 - type: precision_at_10 value: 6.752 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 15.290000000000001 - type: precision_at_5 value: 11.004999999999999 - type: recall_at_1 value: 22.181 - type: recall_at_10 value: 48.513 - type: recall_at_100 value: 73.418 - type: recall_at_1000 value: 90.306 - type: recall_at_3 value: 35.003 - type: recall_at_5 value: 40.876000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.934999999999995 - type: map_at_10 value: 44.727 - type: map_at_100 value: 44.727 - type: map_at_1000 value: 44.727 - type: map_at_3 value: 40.918 - type: map_at_5 value: 42.961 - type: mrr_at_1 value: 39.646 - type: mrr_at_10 value: 48.898 - type: mrr_at_100 value: 48.898 - type: mrr_at_1000 value: 48.898 - type: mrr_at_3 value: 45.896 - type: mrr_at_5 value: 47.514 - type: ndcg_at_1 value: 39.646 - type: ndcg_at_10 value: 50.817 - type: ndcg_at_100 value: 50.803 - type: ndcg_at_1000 value: 50.803 - type: ndcg_at_3 value: 44.507999999999996 - type: ndcg_at_5 value: 47.259 - type: precision_at_1 value: 39.646 - type: precision_at_10 value: 8.759 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_3 value: 20.274 - type: precision_at_5 value: 14.366000000000001 - type: recall_at_1 value: 33.934999999999995 - type: recall_at_10 value: 65.037 - type: recall_at_100 value: 65.037 - type: recall_at_1000 value: 65.037 - type: recall_at_3 value: 47.439 - type: recall_at_5 value: 54.567 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.058 - type: map_at_10 value: 43.137 - type: map_at_100 value: 43.137 - type: map_at_1000 value: 43.137 - type: map_at_3 value: 39.882 - type: map_at_5 value: 41.379 - type: mrr_at_1 value: 38.933 - type: mrr_at_10 value: 48.344 - type: mrr_at_100 value: 48.344 - type: mrr_at_1000 value: 48.344 - type: mrr_at_3 value: 45.652 - type: mrr_at_5 value: 46.877 - type: ndcg_at_1 value: 38.933 - type: ndcg_at_10 value: 49.964 - type: ndcg_at_100 value: 49.242000000000004 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 44.605 - type: ndcg_at_5 value: 46.501999999999995 - type: precision_at_1 value: 38.933 - type: precision_at_10 value: 9.427000000000001 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 20.685000000000002 - type: precision_at_5 value: 14.585 - type: recall_at_1 value: 32.058 - type: recall_at_10 value: 63.074 - type: recall_at_100 value: 63.074 - type: recall_at_1000 value: 63.074 - type: recall_at_3 value: 47.509 - type: recall_at_5 value: 52.455 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.029000000000003 - type: map_at_10 value: 34.646 - type: map_at_100 value: 34.646 - type: map_at_1000 value: 34.646 - type: map_at_3 value: 31.456 - type: map_at_5 value: 33.138 - type: mrr_at_1 value: 28.281 - type: mrr_at_10 value: 36.905 - type: mrr_at_100 value: 36.905 - type: mrr_at_1000 value: 36.905 - type: mrr_at_3 value: 34.011 - type: mrr_at_5 value: 35.638 - type: ndcg_at_1 value: 28.281 - type: ndcg_at_10 value: 40.159 - type: ndcg_at_100 value: 40.159 - type: ndcg_at_1000 value: 40.159 - type: ndcg_at_3 value: 33.995 - type: ndcg_at_5 value: 36.836999999999996 - type: precision_at_1 value: 28.281 - type: precision_at_10 value: 6.358999999999999 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.064 - type: precision_at_3 value: 14.233 - type: precision_at_5 value: 10.314 - type: recall_at_1 value: 26.029000000000003 - type: recall_at_10 value: 55.08 - type: recall_at_100 value: 55.08 - type: recall_at_1000 value: 55.08 - type: recall_at_3 value: 38.487 - type: recall_at_5 value: 45.308 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.842999999999998 - type: map_at_10 value: 22.101000000000003 - type: map_at_100 value: 24.319 - type: map_at_1000 value: 24.51 - type: map_at_3 value: 18.372 - type: map_at_5 value: 20.323 - type: mrr_at_1 value: 27.948 - type: mrr_at_10 value: 40.321 - type: mrr_at_100 value: 41.262 - type: mrr_at_1000 value: 41.297 - type: mrr_at_3 value: 36.558 - type: mrr_at_5 value: 38.824999999999996 - type: ndcg_at_1 value: 27.948 - type: ndcg_at_10 value: 30.906 - type: ndcg_at_100 value: 38.986 - type: ndcg_at_1000 value: 42.136 - type: ndcg_at_3 value: 24.911 - type: ndcg_at_5 value: 27.168999999999997 - type: precision_at_1 value: 27.948 - type: precision_at_10 value: 9.798 - type: precision_at_100 value: 1.8399999999999999 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 18.328 - type: precision_at_5 value: 14.502 - type: recall_at_1 value: 12.842999999999998 - type: recall_at_10 value: 37.245 - type: recall_at_100 value: 64.769 - type: recall_at_1000 value: 82.055 - type: recall_at_3 value: 23.159 - type: recall_at_5 value: 29.113 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.934000000000001 - type: map_at_10 value: 21.915000000000003 - type: map_at_100 value: 21.915000000000003 - type: map_at_1000 value: 21.915000000000003 - type: map_at_3 value: 14.623 - type: map_at_5 value: 17.841 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 78.994 - type: mrr_at_100 value: 78.994 - type: mrr_at_1000 value: 78.994 - type: mrr_at_3 value: 77.208 - type: mrr_at_5 value: 78.55799999999999 - type: ndcg_at_1 value: 60.62499999999999 - type: ndcg_at_10 value: 46.604 - type: ndcg_at_100 value: 35.653 - type: ndcg_at_1000 value: 35.531 - type: ndcg_at_3 value: 50.605 - type: ndcg_at_5 value: 48.730000000000004 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 37.75 - type: precision_at_100 value: 3.775 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 54.417 - type: precision_at_5 value: 48.15 - type: recall_at_1 value: 8.934000000000001 - type: recall_at_10 value: 28.471000000000004 - type: recall_at_100 value: 28.471000000000004 - type: recall_at_1000 value: 28.471000000000004 - type: recall_at_3 value: 16.019 - type: recall_at_5 value: 21.410999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.81899999999999 - type: map_at_10 value: 78.034 - type: map_at_100 value: 78.034 - type: map_at_1000 value: 78.034 - type: map_at_3 value: 76.43100000000001 - type: map_at_5 value: 77.515 - type: mrr_at_1 value: 71.542 - type: mrr_at_10 value: 81.638 - type: mrr_at_100 value: 81.638 - type: mrr_at_1000 value: 81.638 - type: mrr_at_3 value: 80.403 - type: mrr_at_5 value: 81.256 - type: ndcg_at_1 value: 71.542 - type: ndcg_at_10 value: 82.742 - type: ndcg_at_100 value: 82.741 - type: ndcg_at_1000 value: 82.741 - type: ndcg_at_3 value: 80.039 - type: ndcg_at_5 value: 81.695 - type: precision_at_1 value: 71.542 - type: precision_at_10 value: 10.387 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 31.447999999999997 - type: precision_at_5 value: 19.91 - type: recall_at_1 value: 66.81899999999999 - type: recall_at_10 value: 93.372 - type: recall_at_100 value: 93.372 - type: recall_at_1000 value: 93.372 - type: recall_at_3 value: 86.33 - type: recall_at_5 value: 90.347 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 31.158 - type: map_at_10 value: 52.017 - type: map_at_100 value: 54.259 - type: map_at_1000 value: 54.367 - type: map_at_3 value: 45.738 - type: map_at_5 value: 49.283 - type: mrr_at_1 value: 57.87 - type: mrr_at_10 value: 66.215 - type: mrr_at_100 value: 66.735 - type: mrr_at_1000 value: 66.75 - type: mrr_at_3 value: 64.043 - type: mrr_at_5 value: 65.116 - type: ndcg_at_1 value: 57.87 - type: ndcg_at_10 value: 59.946999999999996 - type: ndcg_at_100 value: 66.31099999999999 - type: ndcg_at_1000 value: 67.75999999999999 - type: ndcg_at_3 value: 55.483000000000004 - type: ndcg_at_5 value: 56.891000000000005 - type: precision_at_1 value: 57.87 - type: precision_at_10 value: 16.497 - type: precision_at_100 value: 2.321 - type: precision_at_1000 value: 0.258 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.067999999999998 - type: recall_at_1 value: 31.158 - type: recall_at_10 value: 67.381 - type: recall_at_100 value: 89.464 - type: recall_at_1000 value: 97.989 - type: recall_at_3 value: 50.553000000000004 - type: recall_at_5 value: 57.824 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 42.073 - type: map_at_10 value: 72.418 - type: map_at_100 value: 73.175 - type: map_at_1000 value: 73.215 - type: map_at_3 value: 68.791 - type: map_at_5 value: 71.19 - type: mrr_at_1 value: 84.146 - type: mrr_at_10 value: 88.994 - type: mrr_at_100 value: 89.116 - type: mrr_at_1000 value: 89.12 - type: mrr_at_3 value: 88.373 - type: mrr_at_5 value: 88.82 - type: ndcg_at_1 value: 84.146 - type: ndcg_at_10 value: 79.404 - type: ndcg_at_100 value: 81.83200000000001 - type: ndcg_at_1000 value: 82.524 - type: ndcg_at_3 value: 74.595 - type: ndcg_at_5 value: 77.474 - type: precision_at_1 value: 84.146 - type: precision_at_10 value: 16.753999999999998 - type: precision_at_100 value: 1.8599999999999999 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 48.854 - type: precision_at_5 value: 31.579 - type: recall_at_1 value: 42.073 - type: recall_at_10 value: 83.768 - type: recall_at_100 value: 93.018 - type: recall_at_1000 value: 97.481 - type: recall_at_3 value: 73.282 - type: recall_at_5 value: 78.947 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.698 - type: map_at_10 value: 34.585 - type: map_at_100 value: 35.782000000000004 - type: map_at_1000 value: 35.825 - type: map_at_3 value: 30.397999999999996 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 22.192 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 36.218 - type: mrr_at_1000 value: 36.256 - type: mrr_at_3 value: 30.986000000000004 - type: mrr_at_5 value: 33.268 - type: ndcg_at_1 value: 22.192 - type: ndcg_at_10 value: 41.957 - type: ndcg_at_100 value: 47.658 - type: ndcg_at_1000 value: 48.697 - type: ndcg_at_3 value: 33.433 - type: ndcg_at_5 value: 37.551 - type: precision_at_1 value: 22.192 - type: precision_at_10 value: 6.781 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.365 - type: precision_at_5 value: 10.713000000000001 - type: recall_at_1 value: 21.698 - type: recall_at_10 value: 64.79 - type: recall_at_100 value: 91.071 - type: recall_at_1000 value: 98.883 - type: recall_at_3 value: 41.611 - type: recall_at_5 value: 51.459999999999994 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.52153488185864 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 36.80090398444147 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.920999999999999 - type: map_at_10 value: 16.049 - type: map_at_100 value: 16.049 - type: map_at_1000 value: 16.049 - type: map_at_3 value: 11.865 - type: map_at_5 value: 13.657 - type: mrr_at_1 value: 53.87 - type: mrr_at_10 value: 62.291 - type: mrr_at_100 value: 62.291 - type: mrr_at_1000 value: 62.291 - type: mrr_at_3 value: 60.681 - type: mrr_at_5 value: 61.61 - type: ndcg_at_1 value: 51.23799999999999 - type: ndcg_at_10 value: 40.892 - type: ndcg_at_100 value: 26.951999999999998 - type: ndcg_at_1000 value: 26.474999999999998 - type: ndcg_at_3 value: 46.821 - type: ndcg_at_5 value: 44.333 - type: precision_at_1 value: 53.251000000000005 - type: precision_at_10 value: 30.124000000000002 - type: precision_at_100 value: 3.012 - type: precision_at_1000 value: 0.301 - type: precision_at_3 value: 43.55 - type: precision_at_5 value: 38.266 - type: recall_at_1 value: 6.920999999999999 - type: recall_at_10 value: 20.852 - type: recall_at_100 value: 20.852 - type: recall_at_1000 value: 20.852 - type: recall_at_3 value: 13.628000000000002 - type: recall_at_5 value: 16.273 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 46.827999999999996 - type: map_at_10 value: 63.434000000000005 - type: map_at_100 value: 63.434000000000005 - type: map_at_1000 value: 63.434000000000005 - type: map_at_3 value: 59.794000000000004 - type: map_at_5 value: 62.08 - type: mrr_at_1 value: 52.288999999999994 - type: mrr_at_10 value: 65.95 - type: mrr_at_100 value: 65.95 - type: mrr_at_1000 value: 65.95 - type: mrr_at_3 value: 63.413 - type: mrr_at_5 value: 65.08 - type: ndcg_at_1 value: 52.288999999999994 - type: ndcg_at_10 value: 70.301 - type: ndcg_at_100 value: 70.301 - type: ndcg_at_1000 value: 70.301 - type: ndcg_at_3 value: 63.979 - type: ndcg_at_5 value: 67.582 - type: precision_at_1 value: 52.288999999999994 - type: precision_at_10 value: 10.576 - type: precision_at_100 value: 1.058 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 28.177000000000003 - type: precision_at_5 value: 19.073 - type: recall_at_1 value: 46.827999999999996 - type: recall_at_10 value: 88.236 - type: recall_at_100 value: 88.236 - type: recall_at_1000 value: 88.236 - type: recall_at_3 value: 72.371 - type: recall_at_5 value: 80.56 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.652 - type: map_at_10 value: 85.953 - type: map_at_100 value: 85.953 - type: map_at_1000 value: 85.953 - type: map_at_3 value: 83.05399999999999 - type: map_at_5 value: 84.89 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.473 - type: mrr_at_100 value: 88.473 - type: mrr_at_1000 value: 88.473 - type: mrr_at_3 value: 87.592 - type: mrr_at_5 value: 88.211 - type: ndcg_at_1 value: 82.44 - type: ndcg_at_10 value: 89.467 - type: ndcg_at_100 value: 89.33 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 86.822 - type: ndcg_at_5 value: 88.307 - type: precision_at_1 value: 82.44 - type: precision_at_10 value: 13.616 - type: precision_at_100 value: 1.362 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 38.117000000000004 - type: precision_at_5 value: 25.05 - type: recall_at_1 value: 71.652 - type: recall_at_10 value: 96.224 - type: recall_at_100 value: 96.224 - type: recall_at_1000 value: 96.224 - type: recall_at_3 value: 88.571 - type: recall_at_5 value: 92.812 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 61.295010338050474 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 67.26380819328142 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.683 - type: map_at_10 value: 14.924999999999999 - type: map_at_100 value: 17.532 - type: map_at_1000 value: 17.875 - type: map_at_3 value: 10.392 - type: map_at_5 value: 12.592 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 39.951 - type: mrr_at_100 value: 41.025 - type: mrr_at_1000 value: 41.056 - type: mrr_at_3 value: 36.317 - type: mrr_at_5 value: 38.412 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.410999999999998 - type: ndcg_at_100 value: 33.79 - type: ndcg_at_1000 value: 39.035 - type: ndcg_at_3 value: 22.845 - type: ndcg_at_5 value: 20.080000000000002 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 12.790000000000001 - type: precision_at_100 value: 2.633 - type: precision_at_1000 value: 0.388 - type: precision_at_3 value: 21.367 - type: precision_at_5 value: 17.7 - type: recall_at_1 value: 5.683 - type: recall_at_10 value: 25.91 - type: recall_at_100 value: 53.443 - type: recall_at_1000 value: 78.73 - type: recall_at_3 value: 13.003 - type: recall_at_5 value: 17.932000000000002 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.677978681023 - type: cos_sim_spearman value: 83.13093441058189 - type: euclidean_pearson value: 83.35535759341572 - type: euclidean_spearman value: 83.42583744219611 - type: manhattan_pearson value: 83.2243124045889 - type: manhattan_spearman value: 83.39801618652632 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.68960206569666 - type: cos_sim_spearman value: 77.3368966488535 - type: euclidean_pearson value: 77.62828980560303 - type: euclidean_spearman value: 76.77951481444651 - type: manhattan_pearson value: 77.88637240839041 - type: manhattan_spearman value: 77.22157841466188 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.18745821650724 - type: cos_sim_spearman value: 85.04423285574542 - type: euclidean_pearson value: 85.46604816931023 - type: euclidean_spearman value: 85.5230593932974 - type: manhattan_pearson value: 85.57912805986261 - type: manhattan_spearman value: 85.65955905111873 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.6715333300355 - type: cos_sim_spearman value: 82.9058522514908 - type: euclidean_pearson value: 83.9640357424214 - type: euclidean_spearman value: 83.60415457472637 - type: manhattan_pearson value: 84.05621005853469 - type: manhattan_spearman value: 83.87077724707746 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.82422928098886 - type: cos_sim_spearman value: 88.12660311894628 - type: euclidean_pearson value: 87.50974805056555 - type: euclidean_spearman value: 87.91957275596677 - type: manhattan_pearson value: 87.74119404878883 - type: manhattan_spearman value: 88.2808922165719 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.80605838552093 - type: cos_sim_spearman value: 86.24123388765678 - type: euclidean_pearson value: 85.32648347339814 - type: euclidean_spearman value: 85.60046671950158 - type: manhattan_pearson value: 85.53800168487811 - type: manhattan_spearman value: 85.89542420480763 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.87540978988132 - type: cos_sim_spearman value: 90.12715295099461 - type: euclidean_pearson value: 91.61085993525275 - type: euclidean_spearman value: 91.31835942311758 - type: manhattan_pearson value: 91.57500202032934 - type: manhattan_spearman value: 91.1790925526635 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 69.87136205329556 - type: cos_sim_spearman value: 68.6253154635078 - type: euclidean_pearson value: 68.91536015034222 - type: euclidean_spearman value: 67.63744649352542 - type: manhattan_pearson value: 69.2000713045275 - type: manhattan_spearman value: 68.16002901587316 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.21849551039082 - type: cos_sim_spearman value: 85.6392959372461 - type: euclidean_pearson value: 85.92050852609488 - type: euclidean_spearman value: 85.97205649009734 - type: manhattan_pearson value: 86.1031154802254 - type: manhattan_spearman value: 86.26791155517466 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.83953958636627 - type: mrr value: 96.71167612344082 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 64.994 - type: map_at_10 value: 74.763 - type: map_at_100 value: 75.127 - type: map_at_1000 value: 75.143 - type: map_at_3 value: 71.824 - type: map_at_5 value: 73.71 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 75.749 - type: mrr_at_100 value: 75.922 - type: mrr_at_1000 value: 75.938 - type: mrr_at_3 value: 73.556 - type: mrr_at_5 value: 74.739 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 79.174 - type: ndcg_at_100 value: 80.41 - type: ndcg_at_1000 value: 80.804 - type: ndcg_at_3 value: 74.361 - type: ndcg_at_5 value: 76.861 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.333 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.778 - type: precision_at_5 value: 19.067 - type: recall_at_1 value: 64.994 - type: recall_at_10 value: 91.822 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.878 - type: recall_at_5 value: 85.172 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72079207920792 - type: cos_sim_ap value: 93.00265215525152 - type: cos_sim_f1 value: 85.06596306068602 - type: cos_sim_precision value: 90.05586592178771 - type: cos_sim_recall value: 80.60000000000001 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 91.22371407479089 - type: dot_f1 value: 82.34693877551021 - type: dot_precision value: 84.0625 - type: dot_recall value: 80.7 - type: euclidean_accuracy value: 99.71881188118812 - type: euclidean_ap value: 92.88449963304728 - type: euclidean_f1 value: 85.19480519480518 - type: euclidean_precision value: 88.64864864864866 - type: euclidean_recall value: 82.0 - type: manhattan_accuracy value: 99.73267326732673 - type: manhattan_ap value: 93.23055393056883 - type: manhattan_f1 value: 85.88957055214725 - type: manhattan_precision value: 87.86610878661088 - type: manhattan_recall value: 84.0 - type: max_accuracy value: 99.73267326732673 - type: max_ap value: 93.23055393056883 - type: max_f1 value: 85.88957055214725 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 77.3305735900358 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 41.32967136540674 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.95514866379359 - type: mrr value: 56.95423245055598 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.783007208997144 - type: cos_sim_spearman value: 30.373444721540533 - type: dot_pearson value: 29.210604111143905 - type: dot_spearman value: 29.98809758085659 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.234 - type: map_at_10 value: 1.894 - type: map_at_100 value: 1.894 - type: map_at_1000 value: 1.894 - type: map_at_3 value: 0.636 - type: map_at_5 value: 1.0 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.667 - type: mrr_at_100 value: 93.667 - type: mrr_at_1000 value: 93.667 - type: mrr_at_3 value: 93.667 - type: mrr_at_5 value: 93.667 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 74.798 - type: ndcg_at_100 value: 16.462 - type: ndcg_at_1000 value: 7.0889999999999995 - type: ndcg_at_3 value: 80.754 - type: ndcg_at_5 value: 77.319 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 7.8 - type: precision_at_1000 value: 0.7799999999999999 - type: precision_at_3 value: 83.333 - type: precision_at_5 value: 80.80000000000001 - type: recall_at_1 value: 0.234 - type: recall_at_10 value: 2.093 - type: recall_at_100 value: 2.093 - type: recall_at_1000 value: 2.093 - type: recall_at_3 value: 0.662 - type: recall_at_5 value: 1.0739999999999998 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 10.866000000000001 - type: map_at_100 value: 10.866000000000001 - type: map_at_1000 value: 10.866000000000001 - type: map_at_3 value: 5.909 - type: map_at_5 value: 7.35 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 53.583000000000006 - type: mrr_at_100 value: 53.583000000000006 - type: mrr_at_1000 value: 53.583000000000006 - type: mrr_at_3 value: 49.32 - type: mrr_at_5 value: 51.769 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 27.926000000000002 - type: ndcg_at_100 value: 22.701 - type: ndcg_at_1000 value: 22.701 - type: ndcg_at_3 value: 32.073 - type: ndcg_at_5 value: 28.327999999999996 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 24.694 - type: precision_at_100 value: 2.469 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 17.702 - type: recall_at_100 value: 17.702 - type: recall_at_1000 value: 17.702 - type: recall_at_3 value: 7.208 - type: recall_at_5 value: 9.748999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.79960000000001 - type: ap value: 15.467565415565815 - type: f1 value: 55.28639823443618 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.7792869269949 - type: f1 value: 65.08597154774318 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 55.70352297774293 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 88.27561542588067 - type: cos_sim_ap value: 81.08262141256193 - type: cos_sim_f1 value: 73.82341501361338 - type: cos_sim_precision value: 72.5720112159062 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 86.66030875603504 - type: dot_ap value: 76.6052349228621 - type: dot_f1 value: 70.13897280966768 - type: dot_precision value: 64.70457079152732 - type: dot_recall value: 76.56992084432717 - type: euclidean_accuracy value: 88.37098408535495 - type: euclidean_ap value: 81.12515230092113 - type: euclidean_f1 value: 74.10338225909379 - type: euclidean_precision value: 71.76761433868974 - type: euclidean_recall value: 76.59630606860158 - type: manhattan_accuracy value: 88.34118137926924 - type: manhattan_ap value: 80.95751834536561 - type: manhattan_f1 value: 73.9119496855346 - type: manhattan_precision value: 70.625 - type: manhattan_recall value: 77.5197889182058 - type: max_accuracy value: 88.37098408535495 - type: max_ap value: 81.12515230092113 - type: max_f1 value: 74.10338225909379 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.79896767182831 - type: cos_sim_ap value: 87.40071784061065 - type: cos_sim_f1 value: 79.87753144712087 - type: cos_sim_precision value: 76.67304015296367 - type: cos_sim_recall value: 83.3615645210964 - type: dot_accuracy value: 88.95486474948578 - type: dot_ap value: 86.00227979119943 - type: dot_f1 value: 78.54601474525914 - type: dot_precision value: 75.00525394045535 - type: dot_recall value: 82.43763473975977 - type: euclidean_accuracy value: 89.7892653393876 - type: euclidean_ap value: 87.42174706480819 - type: euclidean_f1 value: 80.07283321194465 - type: euclidean_precision value: 75.96738529574351 - type: euclidean_recall value: 84.6473668001232 - type: manhattan_accuracy value: 89.8474793340319 - type: manhattan_ap value: 87.47814292587448 - type: manhattan_f1 value: 80.15461150280949 - type: manhattan_precision value: 74.88798234468 - type: manhattan_recall value: 86.21804742839544 - type: max_accuracy value: 89.8474793340319 - type: max_ap value: 87.47814292587448 - type: max_f1 value: 80.15461150280949 --- # Model Summary > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm) - **Paper:** https://arxiv.org/abs/2402.09906 - **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview - **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh | Model | Description | |-------|-------------| | [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT | | [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT | # Use The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference). # Citation ```bibtex @misc{muennighoff2024generative, title={Generative Representational Instruction Tuning}, author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela}, year={2024}, eprint={2402.09906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{}

RichardErkhov/GritLM_-_GritLM-7B-4bits

2024-05-03T17:05:23+00:00

TAGS #transformers #safetensors #mistral #text-generation #conversational #custom_code #arxiv-2402.09906 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models GritLM-7B - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- pipeline\_tag: text-generation inference: true license: apache-2.0 datasets: * GritLM/tulu2 tags: * mteb model-index: * name: GritLM-7B results: + task: type: Classification dataset: type: mteb/amazon\_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 + task: type: Classification dataset: type: mteb/amazon\_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 + task: type: Classification dataset: type: mteb/amazon\_reviews\_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 + task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.478 - type: map\_at\_10 value: 54.955 - type: map\_at\_100 value: 54.955 - type: map\_at\_1000 value: 54.955 - type: map\_at\_3 value: 50.888999999999996 - type: map\_at\_5 value: 53.349999999999994 - type: mrr\_at\_1 value: 39.757999999999996 - type: mrr\_at\_10 value: 55.449000000000005 - type: mrr\_at\_100 value: 55.449000000000005 - type: mrr\_at\_1000 value: 55.449000000000005 - type: mrr\_at\_3 value: 51.37500000000001 - type: mrr\_at\_5 value: 53.822 - type: ndcg\_at\_1 value: 38.478 - type: ndcg\_at\_10 value: 63.239999999999995 - type: ndcg\_at\_100 value: 63.239999999999995 - type: ndcg\_at\_1000 value: 63.239999999999995 - type: ndcg\_at\_3 value: 54.935 - type: ndcg\_at\_5 value: 59.379000000000005 - type: precision\_at\_1 value: 38.478 - type: precision\_at\_10 value: 8.933 - type: precision\_at\_100 value: 0.893 - type: precision\_at\_1000 value: 0.089 - type: precision\_at\_3 value: 22.214 - type: precision\_at\_5 value: 15.491 - type: recall\_at\_1 value: 38.478 - type: recall\_at\_10 value: 89.331 - type: recall\_at\_100 value: 89.331 - type: recall\_at\_1000 value: 89.331 - type: recall\_at\_3 value: 66.643 - type: recall\_at\_5 value: 77.45400000000001 + task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v\_measure value: 51.67144081472449 + task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v\_measure value: 48.11256154264126 + task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 + task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos\_sim\_pearson value: 88.1935203751726 - type: cos\_sim\_spearman value: 86.35497970498659 - type: euclidean\_pearson value: 85.46910708503744 - type: euclidean\_spearman value: 85.13928935405485 - type: manhattan\_pearson value: 85.68373836333303 - type: manhattan\_spearman value: 85.40013867117746 + task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v\_measure value: 40.86793640310432 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v\_measure value: 39.80291334130727 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.421 - type: map\_at\_10 value: 52.349000000000004 - type: map\_at\_100 value: 52.349000000000004 - type: map\_at\_1000 value: 52.349000000000004 - type: map\_at\_3 value: 48.17 - type: map\_at\_5 value: 50.432 - type: mrr\_at\_1 value: 47.353 - type: mrr\_at\_10 value: 58.387 - type: mrr\_at\_100 value: 58.387 - type: mrr\_at\_1000 value: 58.387 - type: mrr\_at\_3 value: 56.199 - type: mrr\_at\_5 value: 57.487 - type: ndcg\_at\_1 value: 47.353 - type: ndcg\_at\_10 value: 59.202 - type: ndcg\_at\_100 value: 58.848 - type: ndcg\_at\_1000 value: 58.831999999999994 - type: ndcg\_at\_3 value: 54.112 - type: ndcg\_at\_5 value: 56.312 - type: precision\_at\_1 value: 47.353 - type: precision\_at\_10 value: 11.459 - type: precision\_at\_100 value: 1.146 - type: precision\_at\_1000 value: 0.11499999999999999 - type: precision\_at\_3 value: 26.133 - type: precision\_at\_5 value: 18.627 - type: recall\_at\_1 value: 38.421 - type: recall\_at\_10 value: 71.89 - type: recall\_at\_100 value: 71.89 - type: recall\_at\_1000 value: 71.89 - type: recall\_at\_3 value: 56.58 - type: recall\_at\_5 value: 63.125 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.025999999999996 - type: map\_at\_10 value: 50.590999999999994 - type: map\_at\_100 value: 51.99700000000001 - type: map\_at\_1000 value: 52.11599999999999 - type: map\_at\_3 value: 47.435 - type: map\_at\_5 value: 49.236000000000004 - type: mrr\_at\_1 value: 48.28 - type: mrr\_at\_10 value: 56.814 - type: mrr\_at\_100 value: 57.446 - type: mrr\_at\_1000 value: 57.476000000000006 - type: mrr\_at\_3 value: 54.958 - type: mrr\_at\_5 value: 56.084999999999994 - type: ndcg\_at\_1 value: 48.28 - type: ndcg\_at\_10 value: 56.442 - type: ndcg\_at\_100 value: 60.651999999999994 - type: ndcg\_at\_1000 value: 62.187000000000005 - type: ndcg\_at\_3 value: 52.866 - type: ndcg\_at\_5 value: 54.515 - type: precision\_at\_1 value: 48.28 - type: precision\_at\_10 value: 10.586 - type: precision\_at\_100 value: 1.6310000000000002 - type: precision\_at\_1000 value: 0.20600000000000002 - type: precision\_at\_3 value: 25.945 - type: precision\_at\_5 value: 18.076 - type: recall\_at\_1 value: 38.025999999999996 - type: recall\_at\_10 value: 66.11399999999999 - type: recall\_at\_100 value: 83.339 - type: recall\_at\_1000 value: 92.413 - type: recall\_at\_3 value: 54.493 - type: recall\_at\_5 value: 59.64699999999999 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 47.905 - type: map\_at\_10 value: 61.58 - type: map\_at\_100 value: 62.605 - type: map\_at\_1000 value: 62.637 - type: map\_at\_3 value: 58.074000000000005 - type: map\_at\_5 value: 60.260000000000005 - type: mrr\_at\_1 value: 54.42 - type: mrr\_at\_10 value: 64.847 - type: mrr\_at\_100 value: 65.403 - type: mrr\_at\_1000 value: 65.41900000000001 - type: mrr\_at\_3 value: 62.675000000000004 - type: mrr\_at\_5 value: 64.101 - type: ndcg\_at\_1 value: 54.42 - type: ndcg\_at\_10 value: 67.394 - type: ndcg\_at\_100 value: 70.846 - type: ndcg\_at\_1000 value: 71.403 - type: ndcg\_at\_3 value: 62.025 - type: ndcg\_at\_5 value: 65.032 - type: precision\_at\_1 value: 54.42 - type: precision\_at\_10 value: 10.646 - type: precision\_at\_100 value: 1.325 - type: precision\_at\_1000 value: 0.13999999999999999 - type: precision\_at\_3 value: 27.398 - type: precision\_at\_5 value: 18.796 - type: recall\_at\_1 value: 47.905 - type: recall\_at\_10 value: 80.84599999999999 - type: recall\_at\_100 value: 95.078 - type: recall\_at\_1000 value: 98.878 - type: recall\_at\_3 value: 67.05600000000001 - type: recall\_at\_5 value: 74.261 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 30.745 - type: map\_at\_10 value: 41.021 - type: map\_at\_100 value: 41.021 - type: map\_at\_1000 value: 41.021 - type: map\_at\_3 value: 37.714999999999996 - type: map\_at\_5 value: 39.766 - type: mrr\_at\_1 value: 33.559 - type: mrr\_at\_10 value: 43.537 - type: mrr\_at\_100 value: 43.537 - type: mrr\_at\_1000 value: 43.537 - type: mrr\_at\_3 value: 40.546 - type: mrr\_at\_5 value: 42.439 - type: ndcg\_at\_1 value: 33.559 - type: ndcg\_at\_10 value: 46.781 - type: ndcg\_at\_100 value: 46.781 - type: ndcg\_at\_1000 value: 46.781 - type: ndcg\_at\_3 value: 40.516000000000005 - type: ndcg\_at\_5 value: 43.957 - type: precision\_at\_1 value: 33.559 - type: precision\_at\_10 value: 7.198 - type: precision\_at\_100 value: 0.72 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 17.1 - type: precision\_at\_5 value: 12.316 - type: recall\_at\_1 value: 30.745 - type: recall\_at\_10 value: 62.038000000000004 - type: recall\_at\_100 value: 62.038000000000004 - type: recall\_at\_1000 value: 62.038000000000004 - type: recall\_at\_3 value: 45.378 - type: recall\_at\_5 value: 53.580000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 19.637999999999998 - type: map\_at\_10 value: 31.05 - type: map\_at\_100 value: 31.05 - type: map\_at\_1000 value: 31.05 - type: map\_at\_3 value: 27.628000000000004 - type: map\_at\_5 value: 29.767 - type: mrr\_at\_1 value: 25.0 - type: mrr\_at\_10 value: 36.131 - type: mrr\_at\_100 value: 36.131 - type: mrr\_at\_1000 value: 36.131 - type: mrr\_at\_3 value: 33.333 - type: mrr\_at\_5 value: 35.143 - type: ndcg\_at\_1 value: 25.0 - type: ndcg\_at\_10 value: 37.478 - type: ndcg\_at\_100 value: 37.469 - type: ndcg\_at\_1000 value: 37.469 - type: ndcg\_at\_3 value: 31.757999999999996 - type: ndcg\_at\_5 value: 34.821999999999996 - type: precision\_at\_1 value: 25.0 - type: precision\_at\_10 value: 7.188999999999999 - type: precision\_at\_100 value: 0.719 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 15.837000000000002 - type: precision\_at\_5 value: 11.841 - type: recall\_at\_1 value: 19.637999999999998 - type: recall\_at\_10 value: 51.836000000000006 - type: recall\_at\_100 value: 51.836000000000006 - type: recall\_at\_1000 value: 51.836000000000006 - type: recall\_at\_3 value: 36.384 - type: recall\_at\_5 value: 43.964 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 34.884 - type: map\_at\_10 value: 47.88 - type: map\_at\_100 value: 47.88 - type: map\_at\_1000 value: 47.88 - type: map\_at\_3 value: 43.85 - type: map\_at\_5 value: 46.414 - type: mrr\_at\_1 value: 43.022 - type: mrr\_at\_10 value: 53.569 - type: mrr\_at\_100 value: 53.569 - type: mrr\_at\_1000 value: 53.569 - type: mrr\_at\_3 value: 51.075 - type: mrr\_at\_5 value: 52.725 - type: ndcg\_at\_1 value: 43.022 - type: ndcg\_at\_10 value: 54.461000000000006 - type: ndcg\_at\_100 value: 54.388000000000005 - type: ndcg\_at\_1000 value: 54.388000000000005 - type: ndcg\_at\_3 value: 48.864999999999995 - type: ndcg\_at\_5 value: 52.032000000000004 - type: precision\_at\_1 value: 43.022 - type: precision\_at\_10 value: 9.885 - type: precision\_at\_100 value: 0.988 - type: precision\_at\_1000 value: 0.099 - type: precision\_at\_3 value: 23.612 - type: precision\_at\_5 value: 16.997 - type: recall\_at\_1 value: 34.884 - type: recall\_at\_10 value: 68.12899999999999 - type: recall\_at\_100 value: 68.12899999999999 - type: recall\_at\_1000 value: 68.12899999999999 - type: recall\_at\_3 value: 52.428 - type: recall\_at\_5 value: 60.662000000000006 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.588 - type: map\_at\_10 value: 43.85 - type: map\_at\_100 value: 45.317 - type: map\_at\_1000 value: 45.408 - type: map\_at\_3 value: 39.73 - type: map\_at\_5 value: 42.122 - type: mrr\_at\_1 value: 38.927 - type: mrr\_at\_10 value: 49.582 - type: mrr\_at\_100 value: 50.39 - type: mrr\_at\_1000 value: 50.426 - type: mrr\_at\_3 value: 46.518 - type: mrr\_at\_5 value: 48.271 - type: ndcg\_at\_1 value: 38.927 - type: ndcg\_at\_10 value: 50.605999999999995 - type: ndcg\_at\_100 value: 56.22200000000001 - type: ndcg\_at\_1000 value: 57.724 - type: ndcg\_at\_3 value: 44.232 - type: ndcg\_at\_5 value: 47.233999999999995 - type: precision\_at\_1 value: 38.927 - type: precision\_at\_10 value: 9.429 - type: precision\_at\_100 value: 1.435 - type: precision\_at\_1000 value: 0.172 - type: precision\_at\_3 value: 21.271 - type: precision\_at\_5 value: 15.434000000000001 - type: recall\_at\_1 value: 31.588 - type: recall\_at\_10 value: 64.836 - type: recall\_at\_100 value: 88.066 - type: recall\_at\_1000 value: 97.748 - type: recall\_at\_3 value: 47.128 - type: recall\_at\_5 value: 54.954 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.956083333333336 - type: map\_at\_10 value: 43.33483333333333 - type: map\_at\_100 value: 44.64883333333333 - type: map\_at\_1000 value: 44.75 - type: map\_at\_3 value: 39.87741666666666 - type: map\_at\_5 value: 41.86766666666667 - type: mrr\_at\_1 value: 38.06341666666667 - type: mrr\_at\_10 value: 47.839666666666666 - type: mrr\_at\_100 value: 48.644000000000005 - type: mrr\_at\_1000 value: 48.68566666666667 - type: mrr\_at\_3 value: 45.26358333333334 - type: mrr\_at\_5 value: 46.790000000000006 - type: ndcg\_at\_1 value: 38.06341666666667 - type: ndcg\_at\_10 value: 49.419333333333334 - type: ndcg\_at\_100 value: 54.50166666666667 - type: ndcg\_at\_1000 value: 56.161166666666674 - type: ndcg\_at\_3 value: 43.982416666666666 - type: ndcg\_at\_5 value: 46.638083333333334 - type: precision\_at\_1 value: 38.06341666666667 - type: precision\_at\_10 value: 8.70858333333333 - type: precision\_at\_100 value: 1.327 - type: precision\_at\_1000 value: 0.165 - type: precision\_at\_3 value: 20.37816666666667 - type: precision\_at\_5 value: 14.516333333333334 - type: recall\_at\_1 value: 31.956083333333336 - type: recall\_at\_10 value: 62.69458333333334 - type: recall\_at\_100 value: 84.46433333333334 - type: recall\_at\_1000 value: 95.58449999999999 - type: recall\_at\_3 value: 47.52016666666666 - type: recall\_at\_5 value: 54.36066666666666 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 28.912 - type: map\_at\_10 value: 38.291 - type: map\_at\_100 value: 39.44 - type: map\_at\_1000 value: 39.528 - type: map\_at\_3 value: 35.638 - type: map\_at\_5 value: 37.218 - type: mrr\_at\_1 value: 32.822 - type: mrr\_at\_10 value: 41.661 - type: mrr\_at\_100 value: 42.546 - type: mrr\_at\_1000 value: 42.603 - type: mrr\_at\_3 value: 39.238 - type: mrr\_at\_5 value: 40.726 - type: ndcg\_at\_1 value: 32.822 - type: ndcg\_at\_10 value: 43.373 - type: ndcg\_at\_100 value: 48.638 - type: ndcg\_at\_1000 value: 50.654999999999994 - type: ndcg\_at\_3 value: 38.643 - type: ndcg\_at\_5 value: 41.126000000000005 - type: precision\_at\_1 value: 32.822 - type: precision\_at\_10 value: 6.8709999999999996 - type: precision\_at\_100 value: 1.032 - type: precision\_at\_1000 value: 0.128 - type: precision\_at\_3 value: 16.82 - type: precision\_at\_5 value: 11.718 - type: recall\_at\_1 value: 28.912 - type: recall\_at\_10 value: 55.376999999999995 - type: recall\_at\_100 value: 79.066 - type: recall\_at\_1000 value: 93.664 - type: recall\_at\_3 value: 42.569 - type: recall\_at\_5 value: 48.719 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 22.181 - type: map\_at\_10 value: 31.462 - type: map\_at\_100 value: 32.73 - type: map\_at\_1000 value: 32.848 - type: map\_at\_3 value: 28.57 - type: map\_at\_5 value: 30.182 - type: mrr\_at\_1 value: 27.185 - type: mrr\_at\_10 value: 35.846000000000004 - type: mrr\_at\_100 value: 36.811 - type: mrr\_at\_1000 value: 36.873 - type: mrr\_at\_3 value: 33.437 - type: mrr\_at\_5 value: 34.813 - type: ndcg\_at\_1 value: 27.185 - type: ndcg\_at\_10 value: 36.858000000000004 - type: ndcg\_at\_100 value: 42.501 - type: ndcg\_at\_1000 value: 44.945 - type: ndcg\_at\_3 value: 32.066 - type: ndcg\_at\_5 value: 34.29 - type: precision\_at\_1 value: 27.185 - type: precision\_at\_10 value: 6.752 - type: precision\_at\_100 value: 1.111 - type: precision\_at\_1000 value: 0.151 - type: precision\_at\_3 value: 15.290000000000001 - type: precision\_at\_5 value: 11.004999999999999 - type: recall\_at\_1 value: 22.181 - type: recall\_at\_10 value: 48.513 - type: recall\_at\_100 value: 73.418 - type: recall\_at\_1000 value: 90.306 - type: recall\_at\_3 value: 35.003 - type: recall\_at\_5 value: 40.876000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 33.934999999999995 - type: map\_at\_10 value: 44.727 - type: map\_at\_100 value: 44.727 - type: map\_at\_1000 value: 44.727 - type: map\_at\_3 value: 40.918 - type: map\_at\_5 value: 42.961 - type: mrr\_at\_1 value: 39.646 - type: mrr\_at\_10 value: 48.898 - type: mrr\_at\_100 value: 48.898 - type: mrr\_at\_1000 value: 48.898 - type: mrr\_at\_3 value: 45.896 - type: mrr\_at\_5 value: 47.514 - type: ndcg\_at\_1 value: 39.646 - type: ndcg\_at\_10 value: 50.817 - type: ndcg\_at\_100 value: 50.803 - type: ndcg\_at\_1000 value: 50.803 - type: ndcg\_at\_3 value: 44.507999999999996 - type: ndcg\_at\_5 value: 47.259 - type: precision\_at\_1 value: 39.646 - type: precision\_at\_10 value: 8.759 - type: precision\_at\_100 value: 0.876 - type: precision\_at\_1000 value: 0.08800000000000001 - type: precision\_at\_3 value: 20.274 - type: precision\_at\_5 value: 14.366000000000001 - type: recall\_at\_1 value: 33.934999999999995 - type: recall\_at\_10 value: 65.037 - type: recall\_at\_100 value: 65.037 - type: recall\_at\_1000 value: 65.037 - type: recall\_at\_3 value: 47.439 - type: recall\_at\_5 value: 54.567 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 32.058 - type: map\_at\_10 value: 43.137 - type: map\_at\_100 value: 43.137 - type: map\_at\_1000 value: 43.137 - type: map\_at\_3 value: 39.882 - type: map\_at\_5 value: 41.379 - type: mrr\_at\_1 value: 38.933 - type: mrr\_at\_10 value: 48.344 - type: mrr\_at\_100 value: 48.344 - type: mrr\_at\_1000 value: 48.344 - type: mrr\_at\_3 value: 45.652 - type: mrr\_at\_5 value: 46.877 - type: ndcg\_at\_1 value: 38.933 - type: ndcg\_at\_10 value: 49.964 - type: ndcg\_at\_100 value: 49.242000000000004 - type: ndcg\_at\_1000 value: 49.222 - type: ndcg\_at\_3 value: 44.605 - type: ndcg\_at\_5 value: 46.501999999999995 - type: precision\_at\_1 value: 38.933 - type: precision\_at\_10 value: 9.427000000000001 - type: precision\_at\_100 value: 0.943 - type: precision\_at\_1000 value: 0.094 - type: precision\_at\_3 value: 20.685000000000002 - type: precision\_at\_5 value: 14.585 - type: recall\_at\_1 value: 32.058 - type: recall\_at\_10 value: 63.074 - type: recall\_at\_100 value: 63.074 - type: recall\_at\_1000 value: 63.074 - type: recall\_at\_3 value: 47.509 - type: recall\_at\_5 value: 52.455 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 26.029000000000003 - type: map\_at\_10 value: 34.646 - type: map\_at\_100 value: 34.646 - type: map\_at\_1000 value: 34.646 - type: map\_at\_3 value: 31.456 - type: map\_at\_5 value: 33.138 - type: mrr\_at\_1 value: 28.281 - type: mrr\_at\_10 value: 36.905 - type: mrr\_at\_100 value: 36.905 - type: mrr\_at\_1000 value: 36.905 - type: mrr\_at\_3 value: 34.011 - type: mrr\_at\_5 value: 35.638 - type: ndcg\_at\_1 value: 28.281 - type: ndcg\_at\_10 value: 40.159 - type: ndcg\_at\_100 value: 40.159 - type: ndcg\_at\_1000 value: 40.159 - type: ndcg\_at\_3 value: 33.995 - type: ndcg\_at\_5 value: 36.836999999999996 - type: precision\_at\_1 value: 28.281 - type: precision\_at\_10 value: 6.358999999999999 - type: precision\_at\_100 value: 0.636 - type: precision\_at\_1000 value: 0.064 - type: precision\_at\_3 value: 14.233 - type: precision\_at\_5 value: 10.314 - type: recall\_at\_1 value: 26.029000000000003 - type: recall\_at\_10 value: 55.08 - type: recall\_at\_100 value: 55.08 - type: recall\_at\_1000 value: 55.08 - type: recall\_at\_3 value: 38.487 - type: recall\_at\_5 value: 45.308 + task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 12.842999999999998 - type: map\_at\_10 value: 22.101000000000003 - type: map\_at\_100 value: 24.319 - type: map\_at\_1000 value: 24.51 - type: map\_at\_3 value: 18.372 - type: map\_at\_5 value: 20.323 - type: mrr\_at\_1 value: 27.948 - type: mrr\_at\_10 value: 40.321 - type: mrr\_at\_100 value: 41.262 - type: mrr\_at\_1000 value: 41.297 - type: mrr\_at\_3 value: 36.558 - type: mrr\_at\_5 value: 38.824999999999996 - type: ndcg\_at\_1 value: 27.948 - type: ndcg\_at\_10 value: 30.906 - type: ndcg\_at\_100 value: 38.986 - type: ndcg\_at\_1000 value: 42.136 - type: ndcg\_at\_3 value: 24.911 - type: ndcg\_at\_5 value: 27.168999999999997 - type: precision\_at\_1 value: 27.948 - type: precision\_at\_10 value: 9.798 - type: precision\_at\_100 value: 1.8399999999999999 - type: precision\_at\_1000 value: 0.243 - type: precision\_at\_3 value: 18.328 - type: precision\_at\_5 value: 14.502 - type: recall\_at\_1 value: 12.842999999999998 - type: recall\_at\_10 value: 37.245 - type: recall\_at\_100 value: 64.769 - type: recall\_at\_1000 value: 82.055 - type: recall\_at\_3 value: 23.159 - type: recall\_at\_5 value: 29.113 + task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map\_at\_1 value: 8.934000000000001 - type: map\_at\_10 value: 21.915000000000003 - type: map\_at\_100 value: 21.915000000000003 - type: map\_at\_1000 value: 21.915000000000003 - type: map\_at\_3 value: 14.623 - type: map\_at\_5 value: 17.841 - type: mrr\_at\_1 value: 71.25 - type: mrr\_at\_10 value: 78.994 - type: mrr\_at\_100 value: 78.994 - type: mrr\_at\_1000 value: 78.994 - type: mrr\_at\_3 value: 77.208 - type: mrr\_at\_5 value: 78.55799999999999 - type: ndcg\_at\_1 value: 60.62499999999999 - type: ndcg\_at\_10 value: 46.604 - type: ndcg\_at\_100 value: 35.653 - type: ndcg\_at\_1000 value: 35.531 - type: ndcg\_at\_3 value: 50.605 - type: ndcg\_at\_5 value: 48.730000000000004 - type: precision\_at\_1 value: 71.25 - type: precision\_at\_10 value: 37.75 - type: precision\_at\_100 value: 3.775 - type: precision\_at\_1000 value: 0.377 - type: precision\_at\_3 value: 54.417 - type: precision\_at\_5 value: 48.15 - type: recall\_at\_1 value: 8.934000000000001 - type: recall\_at\_10 value: 28.471000000000004 - type: recall\_at\_100 value: 28.471000000000004 - type: recall\_at\_1000 value: 28.471000000000004 - type: recall\_at\_3 value: 16.019 - type: recall\_at\_5 value: 21.410999999999998 + task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 + task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 66.81899999999999 - type: map\_at\_10 value: 78.034 - type: map\_at\_100 value: 78.034 - type: map\_at\_1000 value: 78.034 - type: map\_at\_3 value: 76.43100000000001 - type: map\_at\_5 value: 77.515 - type: mrr\_at\_1 value: 71.542 - type: mrr\_at\_10 value: 81.638 - type: mrr\_at\_100 value: 81.638 - type: mrr\_at\_1000 value: 81.638 - type: mrr\_at\_3 value: 80.403 - type: mrr\_at\_5 value: 81.256 - type: ndcg\_at\_1 value: 71.542 - type: ndcg\_at\_10 value: 82.742 - type: ndcg\_at\_100 value: 82.741 - type: ndcg\_at\_1000 value: 82.741 - type: ndcg\_at\_3 value: 80.039 - type: ndcg\_at\_5 value: 81.695 - type: precision\_at\_1 value: 71.542 - type: precision\_at\_10 value: 10.387 - type: precision\_at\_100 value: 1.039 - type: precision\_at\_1000 value: 0.104 - type: precision\_at\_3 value: 31.447999999999997 - type: precision\_at\_5 value: 19.91 - type: recall\_at\_1 value: 66.81899999999999 - type: recall\_at\_10 value: 93.372 - type: recall\_at\_100 value: 93.372 - type: recall\_at\_1000 value: 93.372 - type: recall\_at\_3 value: 86.33 - type: recall\_at\_5 value: 90.347 + task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.158 - type: map\_at\_10 value: 52.017 - type: map\_at\_100 value: 54.259 - type: map\_at\_1000 value: 54.367 - type: map\_at\_3 value: 45.738 - type: map\_at\_5 value: 49.283 - type: mrr\_at\_1 value: 57.87 - type: mrr\_at\_10 value: 66.215 - type: mrr\_at\_100 value: 66.735 - type: mrr\_at\_1000 value: 66.75 - type: mrr\_at\_3 value: 64.043 - type: mrr\_at\_5 value: 65.116 - type: ndcg\_at\_1 value: 57.87 - type: ndcg\_at\_10 value: 59.946999999999996 - type: ndcg\_at\_100 value: 66.31099999999999 - type: ndcg\_at\_1000 value: 67.75999999999999 - type: ndcg\_at\_3 value: 55.483000000000004 - type: ndcg\_at\_5 value: 56.891000000000005 - type: precision\_at\_1 value: 57.87 - type: precision\_at\_10 value: 16.497 - type: precision\_at\_100 value: 2.321 - type: precision\_at\_1000 value: 0.258 - type: precision\_at\_3 value: 37.14 - type: precision\_at\_5 value: 27.067999999999998 - type: recall\_at\_1 value: 31.158 - type: recall\_at\_10 value: 67.381 - type: recall\_at\_100 value: 89.464 - type: recall\_at\_1000 value: 97.989 - type: recall\_at\_3 value: 50.553000000000004 - type: recall\_at\_5 value: 57.824 + task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map\_at\_1 value: 42.073 - type: map\_at\_10 value: 72.418 - type: map\_at\_100 value: 73.175 - type: map\_at\_1000 value: 73.215 - type: map\_at\_3 value: 68.791 - type: map\_at\_5 value: 71.19 - type: mrr\_at\_1 value: 84.146 - type: mrr\_at\_10 value: 88.994 - type: mrr\_at\_100 value: 89.116 - type: mrr\_at\_1000 value: 89.12 - type: mrr\_at\_3 value: 88.373 - type: mrr\_at\_5 value: 88.82 - type: ndcg\_at\_1 value: 84.146 - type: ndcg\_at\_10 value: 79.404 - type: ndcg\_at\_100 value: 81.83200000000001 - type: ndcg\_at\_1000 value: 82.524 - type: ndcg\_at\_3 value: 74.595 - type: ndcg\_at\_5 value: 77.474 - type: precision\_at\_1 value: 84.146 - type: precision\_at\_10 value: 16.753999999999998 - type: precision\_at\_100 value: 1.8599999999999999 - type: precision\_at\_1000 value: 0.19499999999999998 - type: precision\_at\_3 value: 48.854 - type: precision\_at\_5 value: 31.579 - type: recall\_at\_1 value: 42.073 - type: recall\_at\_10 value: 83.768 - type: recall\_at\_100 value: 93.018 - type: recall\_at\_1000 value: 97.481 - type: recall\_at\_3 value: 73.282 - type: recall\_at\_5 value: 78.947 + task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 + task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map\_at\_1 value: 21.698 - type: map\_at\_10 value: 34.585 - type: map\_at\_100 value: 35.782000000000004 - type: map\_at\_1000 value: 35.825 - type: map\_at\_3 value: 30.397999999999996 - type: map\_at\_5 value: 32.72 - type: mrr\_at\_1 value: 22.192 - type: mrr\_at\_10 value: 35.085 - type: mrr\_at\_100 value: 36.218 - type: mrr\_at\_1000 value: 36.256 - type: mrr\_at\_3 value: 30.986000000000004 - type: mrr\_at\_5 value: 33.268 - type: ndcg\_at\_1 value: 22.192 - type: ndcg\_at\_10 value: 41.957 - type: ndcg\_at\_100 value: 47.658 - type: ndcg\_at\_1000 value: 48.697 - type: ndcg\_at\_3 value: 33.433 - type: ndcg\_at\_5 value: 37.551 - type: precision\_at\_1 value: 22.192 - type: precision\_at\_10 value: 6.781 - type: precision\_at\_100 value: 0.963 - type: precision\_at\_1000 value: 0.105 - type: precision\_at\_3 value: 14.365 - type: precision\_at\_5 value: 10.713000000000001 - type: recall\_at\_1 value: 21.698 - type: recall\_at\_10 value: 64.79 - type: recall\_at\_100 value: 91.071 - type: recall\_at\_1000 value: 98.883 - type: recall\_at\_3 value: 41.611 - type: recall\_at\_5 value: 51.459999999999994 + task: type: Classification dataset: type: mteb/mtop\_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 + task: type: Classification dataset: type: mteb/mtop\_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 + task: type: Classification dataset: type: mteb/amazon\_massive\_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 + task: type: Classification dataset: type: mteb/amazon\_massive\_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v\_measure value: 36.52153488185864 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v\_measure value: 36.80090398444147 + task: type: Reranking dataset: type: mteb/mind\_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 + task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map\_at\_1 value: 6.920999999999999 - type: map\_at\_10 value: 16.049 - type: map\_at\_100 value: 16.049 - type: map\_at\_1000 value: 16.049 - type: map\_at\_3 value: 11.865 - type: map\_at\_5 value: 13.657 - type: mrr\_at\_1 value: 53.87 - type: mrr\_at\_10 value: 62.291 - type: mrr\_at\_100 value: 62.291 - type: mrr\_at\_1000 value: 62.291 - type: mrr\_at\_3 value: 60.681 - type: mrr\_at\_5 value: 61.61 - type: ndcg\_at\_1 value: 51.23799999999999 - type: ndcg\_at\_10 value: 40.892 - type: ndcg\_at\_100 value: 26.951999999999998 - type: ndcg\_at\_1000 value: 26.474999999999998 - type: ndcg\_at\_3 value: 46.821 - type: ndcg\_at\_5 value: 44.333 - type: precision\_at\_1 value: 53.251000000000005 - type: precision\_at\_10 value: 30.124000000000002 - type: precision\_at\_100 value: 3.012 - type: precision\_at\_1000 value: 0.301 - type: precision\_at\_3 value: 43.55 - type: precision\_at\_5 value: 38.266 - type: recall\_at\_1 value: 6.920999999999999 - type: recall\_at\_10 value: 20.852 - type: recall\_at\_100 value: 20.852 - type: recall\_at\_1000 value: 20.852 - type: recall\_at\_3 value: 13.628000000000002 - type: recall\_at\_5 value: 16.273 + task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map\_at\_1 value: 46.827999999999996 - type: map\_at\_10 value: 63.434000000000005 - type: map\_at\_100 value: 63.434000000000005 - type: map\_at\_1000 value: 63.434000000000005 - type: map\_at\_3 value: 59.794000000000004 - type: map\_at\_5 value: 62.08 - type: mrr\_at\_1 value: 52.288999999999994 - type: mrr\_at\_10 value: 65.95 - type: mrr\_at\_100 value: 65.95 - type: mrr\_at\_1000 value: 65.95 - type: mrr\_at\_3 value: 63.413 - type: mrr\_at\_5 value: 65.08 - type: ndcg\_at\_1 value: 52.288999999999994 - type: ndcg\_at\_10 value: 70.301 - type: ndcg\_at\_100 value: 70.301 - type: ndcg\_at\_1000 value: 70.301 - type: ndcg\_at\_3 value: 63.979 - type: ndcg\_at\_5 value: 67.582 - type: precision\_at\_1 value: 52.288999999999994 - type: precision\_at\_10 value: 10.576 - type: precision\_at\_100 value: 1.058 - type: precision\_at\_1000 value: 0.106 - type: precision\_at\_3 value: 28.177000000000003 - type: precision\_at\_5 value: 19.073 - type: recall\_at\_1 value: 46.827999999999996 - type: recall\_at\_10 value: 88.236 - type: recall\_at\_100 value: 88.236 - type: recall\_at\_1000 value: 88.236 - type: recall\_at\_3 value: 72.371 - type: recall\_at\_5 value: 80.56 + task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 71.652 - type: map\_at\_10 value: 85.953 - type: map\_at\_100 value: 85.953 - type: map\_at\_1000 value: 85.953 - type: map\_at\_3 value: 83.05399999999999 - type: map\_at\_5 value: 84.89 - type: mrr\_at\_1 value: 82.42 - type: mrr\_at\_10 value: 88.473 - type: mrr\_at\_100 value: 88.473 - type: mrr\_at\_1000 value: 88.473 - type: mrr\_at\_3 value: 87.592 - type: mrr\_at\_5 value: 88.211 - type: ndcg\_at\_1 value: 82.44 - type: ndcg\_at\_10 value: 89.467 - type: ndcg\_at\_100 value: 89.33 - type: ndcg\_at\_1000 value: 89.33 - type: ndcg\_at\_3 value: 86.822 - type: ndcg\_at\_5 value: 88.307 - type: precision\_at\_1 value: 82.44 - type: precision\_at\_10 value: 13.616 - type: precision\_at\_100 value: 1.362 - type: precision\_at\_1000 value: 0.136 - type: precision\_at\_3 value: 38.117000000000004 - type: precision\_at\_5 value: 25.05 - type: recall\_at\_1 value: 71.652 - type: recall\_at\_10 value: 96.224 - type: recall\_at\_100 value: 96.224 - type: recall\_at\_1000 value: 96.224 - type: recall\_at\_3 value: 88.571 - type: recall\_at\_5 value: 92.812 + task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v\_measure value: 61.295010338050474 + task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v\_measure value: 67.26380819328142 + task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map\_at\_1 value: 5.683 - type: map\_at\_10 value: 14.924999999999999 - type: map\_at\_100 value: 17.532 - type: map\_at\_1000 value: 17.875 - type: map\_at\_3 value: 10.392 - type: map\_at\_5 value: 12.592 - type: mrr\_at\_1 value: 28.000000000000004 - type: mrr\_at\_10 value: 39.951 - type: mrr\_at\_100 value: 41.025 - type: mrr\_at\_1000 value: 41.056 - type: mrr\_at\_3 value: 36.317 - 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type: cos\_sim\_accuracy value: 89.79896767182831 - type: cos\_sim\_ap value: 87.40071784061065 - type: cos\_sim\_f1 value: 79.87753144712087 - type: cos\_sim\_precision value: 76.67304015296367 - type: cos\_sim\_recall value: 83.3615645210964 - type: dot\_accuracy value: 88.95486474948578 - type: dot\_ap value: 86.00227979119943 - type: dot\_f1 value: 78.54601474525914 - type: dot\_precision value: 75.00525394045535 - type: dot\_recall value: 82.43763473975977 - type: euclidean\_accuracy value: 89.7892653393876 - type: euclidean\_ap value: 87.42174706480819 - type: euclidean\_f1 value: 80.07283321194465 - type: euclidean\_precision value: 75.96738529574351 - type: euclidean\_recall value: 84.6473668001232 - type: manhattan\_accuracy value: 89.8474793340319 - type: manhattan\_ap value: 87.47814292587448 - type: manhattan\_f1 value: 80.15461150280949 - type: manhattan\_precision value: 74.88798234468 - type: manhattan\_recall value: 86.21804742839544 - type: max\_accuracy value: 89.8474793340319 - type: max\_ap value: 87.47814292587448 - type: max\_f1 value: 80.15461150280949 --- Model Summary ============= > > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. > > > * Repository: ContextualAI/gritlm * Paper: URL * Logs: URL * Script: URL Use === The model usage is documented here.

image-classification

transformers

<!-- 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. --> # fine-tuned-model This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7582 - Accuracy: 0.7424 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.7206 | 0.9880 | 62 | 0.7582 | 0.7424 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "fine-tuned-model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7423864203694458, "name": "Accuracy"}]}]}]}

carvalhaes/fine-tuned-model

2024-05-03T17:06:09+00:00

TAGS #transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

fine-tuned-model ================ This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.7582 * Accuracy: 0.7424 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: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # GUE_tf_3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5571 - F1 Score: 0.6983 - Accuracy: 0.701 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6152 | 0.93 | 200 | 0.5646 | 0.7015 | 0.706 | | 0.5923 | 1.87 | 400 | 0.5661 | 0.6898 | 0.69 | | 0.5867 | 2.8 | 600 | 0.5547 | 0.7215 | 0.723 | | 0.5784 | 3.74 | 800 | 0.5597 | 0.7026 | 0.703 | | 0.5757 | 4.67 | 1000 | 0.5493 | 0.7194 | 0.72 | | 0.5707 | 5.61 | 1200 | 0.5421 | 0.7228 | 0.726 | | 0.5658 | 6.54 | 1400 | 0.5426 | 0.7299 | 0.731 | | 0.5638 | 7.48 | 1600 | 0.5426 | 0.7274 | 0.728 | | 0.5608 | 8.41 | 1800 | 0.5390 | 0.7224 | 0.723 | | 0.5628 | 9.35 | 2000 | 0.5391 | 0.7248 | 0.726 | | 0.5553 | 10.28 | 2200 | 0.5445 | 0.7101 | 0.71 | | 0.5525 | 11.21 | 2400 | 0.5418 | 0.7222 | 0.724 | | 0.5518 | 12.15 | 2600 | 0.5403 | 0.7232 | 0.726 | | 0.5459 | 13.08 | 2800 | 0.5447 | 0.7220 | 0.729 | | 0.5457 | 14.02 | 3000 | 0.5390 | 0.7207 | 0.723 | | 0.5439 | 14.95 | 3200 | 0.5381 | 0.7278 | 0.731 | | 0.5425 | 15.89 | 3400 | 0.5380 | 0.7297 | 0.732 | | 0.5397 | 16.82 | 3600 | 0.5406 | 0.7242 | 0.727 | | 0.5351 | 17.76 | 3800 | 0.5399 | 0.7233 | 0.726 | | 0.536 | 18.69 | 4000 | 0.5452 | 0.7218 | 0.722 | | 0.534 | 19.63 | 4200 | 0.5418 | 0.7201 | 0.722 | | 0.5342 | 20.56 | 4400 | 0.5423 | 0.7244 | 0.726 | | 0.5274 | 21.5 | 4600 | 0.5477 | 0.7100 | 0.71 | | 0.5269 | 22.43 | 4800 | 0.5466 | 0.7142 | 0.716 | | 0.5285 | 23.36 | 5000 | 0.5517 | 0.7051 | 0.705 | | 0.5224 | 24.3 | 5200 | 0.5521 | 0.6986 | 0.699 | | 0.5194 | 25.23 | 5400 | 0.5508 | 0.7193 | 0.722 | | 0.5245 | 26.17 | 5600 | 0.5442 | 0.7108 | 0.712 | | 0.5155 | 27.1 | 5800 | 0.5491 | 0.7044 | 0.705 | | 0.5161 | 28.04 | 6000 | 0.5447 | 0.7041 | 0.705 | | 0.5114 | 28.97 | 6200 | 0.5540 | 0.7019 | 0.702 | | 0.5161 | 29.91 | 6400 | 0.5514 | 0.7166 | 0.719 | | 0.5109 | 30.84 | 6600 | 0.5514 | 0.7116 | 0.714 | | 0.5064 | 31.78 | 6800 | 0.5529 | 0.7160 | 0.717 | | 0.509 | 32.71 | 7000 | 0.5523 | 0.7072 | 0.709 | | 0.5095 | 33.64 | 7200 | 0.5537 | 0.7158 | 0.717 | | 0.5019 | 34.58 | 7400 | 0.5588 | 0.6950 | 0.695 | | 0.5042 | 35.51 | 7600 | 0.5562 | 0.692 | 0.692 | | 0.5029 | 36.45 | 7800 | 0.5594 | 0.7062 | 0.707 | | 0.5029 | 37.38 | 8000 | 0.5603 | 0.6975 | 0.698 | | 0.4968 | 38.32 | 8200 | 0.5590 | 0.7049 | 0.706 | | 0.4992 | 39.25 | 8400 | 0.5634 | 0.7008 | 0.702 | | 0.4965 | 40.19 | 8600 | 0.5624 | 0.7002 | 0.701 | | 0.4974 | 41.12 | 8800 | 0.5622 | 0.7025 | 0.703 | | 0.4989 | 42.06 | 9000 | 0.5610 | 0.7072 | 0.708 | | 0.4962 | 42.99 | 9200 | 0.5612 | 0.6988 | 0.699 | | 0.4983 | 43.93 | 9400 | 0.5612 | 0.7018 | 0.702 | | 0.4954 | 44.86 | 9600 | 0.5618 | 0.7024 | 0.703 | | 0.4947 | 45.79 | 9800 | 0.5622 | 0.7033 | 0.704 | | 0.4901 | 46.73 | 10000 | 0.5631 | 0.6995 | 0.7 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_3-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_65536_512_47M-L32_f

2024-05-03T17:06:32+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_3-seqsight\_65536\_512\_47M-L32\_f =========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5571 * F1 Score: 0.6983 * Accuracy: 0.701 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_tf_2-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4711 - F1 Score: 0.7738 - Accuracy: 0.774 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5857 | 1.34 | 200 | 0.5446 | 0.7070 | 0.709 | | 0.5463 | 2.68 | 400 | 0.5318 | 0.7309 | 0.731 | | 0.5395 | 4.03 | 600 | 0.5247 | 0.736 | 0.736 | | 0.5355 | 5.37 | 800 | 0.5259 | 0.7386 | 0.739 | | 0.5311 | 6.71 | 1000 | 0.5198 | 0.7450 | 0.746 | | 0.5268 | 8.05 | 1200 | 0.5181 | 0.7480 | 0.748 | | 0.5236 | 9.4 | 1400 | 0.5168 | 0.7421 | 0.743 | | 0.5227 | 10.74 | 1600 | 0.5135 | 0.7477 | 0.748 | | 0.5221 | 12.08 | 1800 | 0.5155 | 0.7539 | 0.754 | | 0.5201 | 13.42 | 2000 | 0.5100 | 0.7530 | 0.753 | | 0.5189 | 14.77 | 2200 | 0.5123 | 0.7507 | 0.751 | | 0.5132 | 16.11 | 2400 | 0.5106 | 0.7530 | 0.753 | | 0.5175 | 17.45 | 2600 | 0.5099 | 0.7506 | 0.751 | | 0.5124 | 18.79 | 2800 | 0.5082 | 0.7589 | 0.759 | | 0.5106 | 20.13 | 3000 | 0.5086 | 0.7589 | 0.759 | | 0.5117 | 21.48 | 3200 | 0.5107 | 0.7508 | 0.751 | | 0.5132 | 22.82 | 3400 | 0.5076 | 0.7541 | 0.755 | | 0.5099 | 24.16 | 3600 | 0.5068 | 0.7520 | 0.753 | | 0.5063 | 25.5 | 3800 | 0.5087 | 0.7474 | 0.749 | | 0.5105 | 26.85 | 4000 | 0.5084 | 0.7454 | 0.747 | | 0.5057 | 28.19 | 4200 | 0.5059 | 0.7545 | 0.755 | | 0.5064 | 29.53 | 4400 | 0.5066 | 0.7580 | 0.758 | | 0.5029 | 30.87 | 4600 | 0.5057 | 0.7548 | 0.755 | | 0.5057 | 32.21 | 4800 | 0.5065 | 0.7517 | 0.752 | | 0.507 | 33.56 | 5000 | 0.5040 | 0.7580 | 0.758 | | 0.5037 | 34.9 | 5200 | 0.5061 | 0.7559 | 0.756 | | 0.4995 | 36.24 | 5400 | 0.5060 | 0.7500 | 0.751 | | 0.5053 | 37.58 | 5600 | 0.5038 | 0.7556 | 0.756 | | 0.504 | 38.93 | 5800 | 0.5037 | 0.7535 | 0.754 | | 0.5014 | 40.27 | 6000 | 0.5029 | 0.7578 | 0.758 | | 0.4999 | 41.61 | 6200 | 0.5034 | 0.7555 | 0.756 | | 0.5055 | 42.95 | 6400 | 0.5043 | 0.7485 | 0.749 | | 0.5003 | 44.3 | 6600 | 0.5036 | 0.7550 | 0.755 | | 0.4994 | 45.64 | 6800 | 0.5039 | 0.7539 | 0.754 | | 0.4994 | 46.98 | 7000 | 0.5054 | 0.7457 | 0.746 | | 0.4982 | 48.32 | 7200 | 0.5044 | 0.7539 | 0.754 | | 0.4983 | 49.66 | 7400 | 0.5045 | 0.7507 | 0.751 | | 0.4981 | 51.01 | 7600 | 0.5038 | 0.7456 | 0.746 | | 0.4961 | 52.35 | 7800 | 0.5042 | 0.7477 | 0.748 | | 0.4979 | 53.69 | 8000 | 0.5052 | 0.7482 | 0.749 | | 0.4952 | 55.03 | 8200 | 0.5036 | 0.7457 | 0.746 | | 0.4982 | 56.38 | 8400 | 0.5028 | 0.7469 | 0.747 | | 0.497 | 57.72 | 8600 | 0.5038 | 0.7483 | 0.749 | | 0.4963 | 59.06 | 8800 | 0.5029 | 0.7483 | 0.749 | | 0.4952 | 60.4 | 9000 | 0.5030 | 0.7448 | 0.745 | | 0.4966 | 61.74 | 9200 | 0.5034 | 0.7483 | 0.749 | | 0.5011 | 63.09 | 9400 | 0.5028 | 0.7475 | 0.748 | | 0.4959 | 64.43 | 9600 | 0.5032 | 0.7465 | 0.747 | | 0.4991 | 65.77 | 9800 | 0.5031 | 0.7466 | 0.747 | | 0.4941 | 67.11 | 10000 | 0.5033 | 0.7475 | 0.748 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_2-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_65536_512_47M-L1_f

2024-05-03T17:09:11+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_2-seqsight\_65536\_512\_47M-L1\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4711 * F1 Score: 0.7738 * Accuracy: 0.774 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

cilantro9246/hq6ceip

2024-05-03T17:09:36+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # GUE_tf_2-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4753 - F1 Score: 0.7840 - Accuracy: 0.785 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5645 | 1.34 | 200 | 0.5230 | 0.7288 | 0.73 | | 0.5304 | 2.68 | 400 | 0.5167 | 0.746 | 0.746 | | 0.5202 | 4.03 | 600 | 0.5055 | 0.7610 | 0.761 | | 0.5133 | 5.37 | 800 | 0.5065 | 0.7580 | 0.758 | | 0.5071 | 6.71 | 1000 | 0.5095 | 0.7491 | 0.75 | | 0.4993 | 8.05 | 1200 | 0.5024 | 0.7465 | 0.747 | | 0.4951 | 9.4 | 1400 | 0.5096 | 0.7463 | 0.748 | | 0.4902 | 10.74 | 1600 | 0.4929 | 0.752 | 0.752 | | 0.4884 | 12.08 | 1800 | 0.4936 | 0.7540 | 0.754 | | 0.4826 | 13.42 | 2000 | 0.4938 | 0.7550 | 0.755 | | 0.4799 | 14.77 | 2200 | 0.5030 | 0.7482 | 0.751 | | 0.4717 | 16.11 | 2400 | 0.5020 | 0.7490 | 0.749 | | 0.4703 | 17.45 | 2600 | 0.4984 | 0.7536 | 0.755 | | 0.462 | 18.79 | 2800 | 0.4910 | 0.7581 | 0.759 | | 0.4576 | 20.13 | 3000 | 0.4936 | 0.7671 | 0.768 | | 0.4564 | 21.48 | 3200 | 0.5030 | 0.7569 | 0.757 | | 0.4556 | 22.82 | 3400 | 0.4965 | 0.7550 | 0.755 | | 0.4503 | 24.16 | 3600 | 0.4917 | 0.7635 | 0.764 | | 0.4425 | 25.5 | 3800 | 0.5048 | 0.7516 | 0.752 | | 0.444 | 26.85 | 4000 | 0.4995 | 0.7573 | 0.758 | | 0.441 | 28.19 | 4200 | 0.4975 | 0.7599 | 0.76 | | 0.4366 | 29.53 | 4400 | 0.5035 | 0.7527 | 0.753 | | 0.431 | 30.87 | 4600 | 0.4948 | 0.7528 | 0.753 | | 0.4288 | 32.21 | 4800 | 0.5166 | 0.7485 | 0.749 | | 0.4289 | 33.56 | 5000 | 0.5092 | 0.7538 | 0.754 | | 0.4244 | 34.9 | 5200 | 0.5031 | 0.7500 | 0.75 | | 0.4203 | 36.24 | 5400 | 0.4992 | 0.7547 | 0.755 | | 0.4212 | 37.58 | 5600 | 0.4963 | 0.7619 | 0.762 | | 0.4151 | 38.93 | 5800 | 0.5031 | 0.7586 | 0.759 | | 0.4103 | 40.27 | 6000 | 0.5090 | 0.7517 | 0.752 | | 0.4087 | 41.61 | 6200 | 0.5000 | 0.7530 | 0.753 | | 0.413 | 42.95 | 6400 | 0.5046 | 0.7549 | 0.755 | | 0.4031 | 44.3 | 6600 | 0.5112 | 0.7500 | 0.75 | | 0.4049 | 45.64 | 6800 | 0.5135 | 0.7478 | 0.748 | | 0.4038 | 46.98 | 7000 | 0.5129 | 0.7549 | 0.755 | | 0.3993 | 48.32 | 7200 | 0.5133 | 0.7470 | 0.747 | | 0.3966 | 49.66 | 7400 | 0.5064 | 0.7550 | 0.755 | | 0.3959 | 51.01 | 7600 | 0.5116 | 0.7549 | 0.755 | | 0.3894 | 52.35 | 7800 | 0.5182 | 0.7580 | 0.758 | | 0.3944 | 53.69 | 8000 | 0.5128 | 0.7529 | 0.753 | | 0.386 | 55.03 | 8200 | 0.5210 | 0.7460 | 0.746 | | 0.388 | 56.38 | 8400 | 0.5143 | 0.7560 | 0.756 | | 0.3881 | 57.72 | 8600 | 0.5146 | 0.7540 | 0.754 | | 0.3851 | 59.06 | 8800 | 0.5129 | 0.7590 | 0.759 | | 0.3856 | 60.4 | 9000 | 0.5232 | 0.7550 | 0.755 | | 0.3835 | 61.74 | 9200 | 0.5139 | 0.752 | 0.752 | | 0.3853 | 63.09 | 9400 | 0.5165 | 0.7510 | 0.751 | | 0.3805 | 64.43 | 9600 | 0.5156 | 0.7549 | 0.755 | | 0.3854 | 65.77 | 9800 | 0.5193 | 0.7550 | 0.755 | | 0.3776 | 67.11 | 10000 | 0.5180 | 0.756 | 0.756 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_2-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_65536_512_47M-L32_f

2024-05-03T17:09:51+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_2-seqsight\_65536\_512\_47M-L32\_f =========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4753 * F1 Score: 0.7840 * Accuracy: 0.785 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_tf_2-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4667 - F1 Score: 0.7770 - Accuracy: 0.778 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5716 | 1.34 | 200 | 0.5302 | 0.7204 | 0.722 | | 0.5353 | 2.68 | 400 | 0.5215 | 0.7350 | 0.735 | | 0.5271 | 4.03 | 600 | 0.5118 | 0.7479 | 0.748 | | 0.5221 | 5.37 | 800 | 0.5087 | 0.7499 | 0.75 | | 0.5185 | 6.71 | 1000 | 0.5089 | 0.7599 | 0.76 | | 0.5108 | 8.05 | 1200 | 0.5104 | 0.7449 | 0.745 | | 0.5082 | 9.4 | 1400 | 0.5107 | 0.7445 | 0.746 | | 0.5057 | 10.74 | 1600 | 0.5023 | 0.7518 | 0.752 | | 0.505 | 12.08 | 1800 | 0.5077 | 0.7450 | 0.745 | | 0.5005 | 13.42 | 2000 | 0.5044 | 0.7449 | 0.745 | | 0.4996 | 14.77 | 2200 | 0.5082 | 0.7424 | 0.744 | | 0.4936 | 16.11 | 2400 | 0.5090 | 0.7490 | 0.749 | | 0.4946 | 17.45 | 2600 | 0.5053 | 0.7499 | 0.751 | | 0.4885 | 18.79 | 2800 | 0.4999 | 0.7503 | 0.751 | | 0.4859 | 20.13 | 3000 | 0.4994 | 0.7555 | 0.756 | | 0.4861 | 21.48 | 3200 | 0.5075 | 0.7540 | 0.754 | | 0.4876 | 22.82 | 3400 | 0.5025 | 0.7569 | 0.757 | | 0.4833 | 24.16 | 3600 | 0.4986 | 0.7566 | 0.757 | | 0.4774 | 25.5 | 3800 | 0.5025 | 0.7534 | 0.754 | | 0.4819 | 26.85 | 4000 | 0.4993 | 0.7562 | 0.757 | | 0.4783 | 28.19 | 4200 | 0.4959 | 0.762 | 0.762 | | 0.4776 | 29.53 | 4400 | 0.5019 | 0.7580 | 0.758 | | 0.4741 | 30.87 | 4600 | 0.4985 | 0.7639 | 0.764 | | 0.4736 | 32.21 | 4800 | 0.5055 | 0.7564 | 0.757 | | 0.4752 | 33.56 | 5000 | 0.4988 | 0.7518 | 0.752 | | 0.4704 | 34.9 | 5200 | 0.5015 | 0.7589 | 0.759 | | 0.4689 | 36.24 | 5400 | 0.4975 | 0.7686 | 0.769 | | 0.4718 | 37.58 | 5600 | 0.4931 | 0.7547 | 0.755 | | 0.4679 | 38.93 | 5800 | 0.4966 | 0.7587 | 0.759 | | 0.4662 | 40.27 | 6000 | 0.4934 | 0.7608 | 0.761 | | 0.4645 | 41.61 | 6200 | 0.4942 | 0.7520 | 0.752 | | 0.4709 | 42.95 | 6400 | 0.4969 | 0.7609 | 0.761 | | 0.4622 | 44.3 | 6600 | 0.4993 | 0.7540 | 0.754 | | 0.4634 | 45.64 | 6800 | 0.4978 | 0.7520 | 0.752 | | 0.4634 | 46.98 | 7000 | 0.4974 | 0.75 | 0.75 | | 0.4618 | 48.32 | 7200 | 0.4976 | 0.7510 | 0.751 | | 0.4599 | 49.66 | 7400 | 0.4945 | 0.7498 | 0.75 | | 0.4604 | 51.01 | 7600 | 0.4957 | 0.7470 | 0.747 | | 0.4562 | 52.35 | 7800 | 0.4983 | 0.7568 | 0.757 | | 0.4611 | 53.69 | 8000 | 0.4957 | 0.7445 | 0.745 | | 0.4548 | 55.03 | 8200 | 0.4944 | 0.7449 | 0.745 | | 0.4581 | 56.38 | 8400 | 0.4942 | 0.7450 | 0.745 | | 0.4591 | 57.72 | 8600 | 0.4934 | 0.7466 | 0.747 | | 0.4543 | 59.06 | 8800 | 0.4927 | 0.7517 | 0.752 | | 0.4563 | 60.4 | 9000 | 0.4961 | 0.7530 | 0.753 | | 0.4566 | 61.74 | 9200 | 0.4936 | 0.7478 | 0.748 | | 0.4584 | 63.09 | 9400 | 0.4943 | 0.7508 | 0.751 | | 0.4518 | 64.43 | 9600 | 0.4950 | 0.7487 | 0.749 | | 0.4596 | 65.77 | 9800 | 0.4949 | 0.7509 | 0.751 | | 0.452 | 67.11 | 10000 | 0.4949 | 0.7498 | 0.75 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_tf_2-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_65536_512_47M-L8_f

2024-05-03T17:09:51+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_tf\_2-seqsight\_65536\_512\_47M-L8\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4667 * F1 Score: 0.7770 * Accuracy: 0.778 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

## Llamacpp Quantizations of DuckyBlender/racist-phi3 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2783">b2783</a> for quantization. Original model: https://huggingface.co/DuckyBlender/racist-phi3

{"language": ["en"], "tags": ["racist", "nsfw", "not-for-all-audiences"], "datasets": ["DuckyBlender/racist-inputoutput"]}

DuckyBlender/racist-phi3-GGUF

2024-05-03T17:10:18+00:00

TAGS #racist #nsfw #not-for-all-audiences #en #dataset-DuckyBlender/racist-inputoutput #region-us

## Llamacpp Quantizations of DuckyBlender/racist-phi3 Using <a href="URL release <a href="URL for quantization. Original model: URL

peft

<!-- 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. --> # GUE_virus_covid-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.7826 - F1 Score: 0.3230 - Accuracy: 0.3274 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.185 | 0.35 | 200 | 2.1838 | 0.0863 | 0.1327 | | 2.1807 | 0.7 | 400 | 2.1804 | 0.0877 | 0.1363 | | 2.1748 | 1.05 | 600 | 2.1744 | 0.1189 | 0.1503 | | 2.1706 | 1.4 | 800 | 2.1665 | 0.0993 | 0.1460 | | 2.1628 | 1.75 | 1000 | 2.1550 | 0.1339 | 0.1621 | | 2.1571 | 2.09 | 1200 | 2.1498 | 0.1296 | 0.1679 | | 2.1487 | 2.44 | 1400 | 2.1426 | 0.1481 | 0.1654 | | 2.1395 | 2.79 | 1600 | 2.1151 | 0.1770 | 0.1963 | | 2.1143 | 3.14 | 1800 | 2.0605 | 0.1929 | 0.2197 | | 2.0738 | 3.49 | 2000 | 2.0280 | 0.2069 | 0.2267 | | 2.056 | 3.84 | 2200 | 1.9958 | 0.2303 | 0.2449 | | 2.0299 | 4.19 | 2400 | 1.9701 | 0.2333 | 0.2469 | | 2.0076 | 4.54 | 2600 | 1.9480 | 0.2426 | 0.2569 | | 2.0016 | 4.89 | 2800 | 1.9330 | 0.2555 | 0.2660 | | 1.9859 | 5.24 | 3000 | 1.9220 | 0.2567 | 0.2687 | | 1.9754 | 5.58 | 3200 | 1.9137 | 0.2599 | 0.2701 | | 1.9647 | 5.93 | 3400 | 1.8988 | 0.2645 | 0.2757 | | 1.9619 | 6.28 | 3600 | 1.8909 | 0.2744 | 0.2805 | | 1.9479 | 6.63 | 3800 | 1.8845 | 0.2699 | 0.2856 | | 1.9448 | 6.98 | 4000 | 1.8778 | 0.2759 | 0.2870 | | 1.9406 | 7.33 | 4200 | 1.8704 | 0.2794 | 0.2935 | | 1.9341 | 7.68 | 4400 | 1.8636 | 0.2925 | 0.2979 | | 1.9291 | 8.03 | 4600 | 1.8638 | 0.2861 | 0.2937 | | 1.9248 | 8.38 | 4800 | 1.8564 | 0.2829 | 0.2965 | | 1.9284 | 8.73 | 5000 | 1.8568 | 0.2824 | 0.2948 | | 1.9183 | 9.08 | 5200 | 1.8473 | 0.2914 | 0.2941 | | 1.9162 | 9.42 | 5400 | 1.8449 | 0.2834 | 0.3003 | | 1.9152 | 9.77 | 5600 | 1.8363 | 0.2969 | 0.3089 | | 1.9113 | 10.12 | 5800 | 1.8348 | 0.3011 | 0.3086 | | 1.9133 | 10.47 | 6000 | 1.8321 | 0.2902 | 0.2989 | | 1.9053 | 10.82 | 6200 | 1.8315 | 0.3019 | 0.3072 | | 1.8974 | 11.17 | 6400 | 1.8236 | 0.3025 | 0.3066 | | 1.9014 | 11.52 | 6600 | 1.8163 | 0.2985 | 0.3068 | | 1.898 | 11.87 | 6800 | 1.8117 | 0.3064 | 0.3160 | | 1.8863 | 12.22 | 7000 | 1.8083 | 0.3052 | 0.3127 | | 1.8874 | 12.57 | 7200 | 1.8044 | 0.3067 | 0.3119 | | 1.8863 | 12.91 | 7400 | 1.8006 | 0.3120 | 0.3189 | | 1.8767 | 13.26 | 7600 | 1.7952 | 0.3067 | 0.3126 | | 1.8833 | 13.61 | 7800 | 1.7948 | 0.3050 | 0.3098 | | 1.8797 | 13.96 | 8000 | 1.7895 | 0.3114 | 0.3176 | | 1.8645 | 14.31 | 8200 | 1.7869 | 0.3120 | 0.3194 | | 1.8744 | 14.66 | 8400 | 1.7856 | 0.3198 | 0.3239 | | 1.8649 | 15.01 | 8600 | 1.7839 | 0.3153 | 0.3206 | | 1.8736 | 15.36 | 8800 | 1.7824 | 0.3191 | 0.3225 | | 1.8607 | 15.71 | 9000 | 1.7825 | 0.3132 | 0.3192 | | 1.8676 | 16.06 | 9200 | 1.7815 | 0.3143 | 0.3202 | | 1.8671 | 16.4 | 9400 | 1.7803 | 0.3181 | 0.3230 | | 1.8645 | 16.75 | 9600 | 1.7794 | 0.3183 | 0.3220 | | 1.8659 | 17.1 | 9800 | 1.7795 | 0.3168 | 0.3220 | | 1.8662 | 17.45 | 10000 | 1.7790 | 0.3174 | 0.3224 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_virus_covid-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_65536_512_47M-L1_f

2024-05-03T17:10:19+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_virus\_covid-seqsight\_65536\_512\_47M-L1\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.7826 * F1 Score: 0.3230 * Accuracy: 0.3274 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GritLM-7B - bnb 8bits - Model creator: https://huggingface.co/GritLM/ - Original model: https://huggingface.co/GritLM/GritLM-7B/ Original model description: --- pipeline_tag: text-generation inference: true license: apache-2.0 datasets: - GritLM/tulu2 tags: - mteb model-index: - name: GritLM-7B results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.478 - type: map_at_10 value: 54.955 - type: map_at_100 value: 54.955 - type: map_at_1000 value: 54.955 - type: map_at_3 value: 50.888999999999996 - type: map_at_5 value: 53.349999999999994 - type: mrr_at_1 value: 39.757999999999996 - type: mrr_at_10 value: 55.449000000000005 - type: mrr_at_100 value: 55.449000000000005 - type: mrr_at_1000 value: 55.449000000000005 - type: mrr_at_3 value: 51.37500000000001 - type: mrr_at_5 value: 53.822 - type: ndcg_at_1 value: 38.478 - type: ndcg_at_10 value: 63.239999999999995 - type: ndcg_at_100 value: 63.239999999999995 - type: ndcg_at_1000 value: 63.239999999999995 - type: ndcg_at_3 value: 54.935 - type: ndcg_at_5 value: 59.379000000000005 - type: precision_at_1 value: 38.478 - type: precision_at_10 value: 8.933 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 22.214 - type: precision_at_5 value: 15.491 - type: recall_at_1 value: 38.478 - type: recall_at_10 value: 89.331 - type: recall_at_100 value: 89.331 - type: recall_at_1000 value: 89.331 - type: recall_at_3 value: 66.643 - type: recall_at_5 value: 77.45400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 51.67144081472449 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 48.11256154264126 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.1935203751726 - type: cos_sim_spearman value: 86.35497970498659 - type: euclidean_pearson value: 85.46910708503744 - type: euclidean_spearman value: 85.13928935405485 - type: manhattan_pearson value: 85.68373836333303 - type: manhattan_spearman value: 85.40013867117746 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.86793640310432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 39.80291334130727 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.421 - type: map_at_10 value: 52.349000000000004 - type: map_at_100 value: 52.349000000000004 - type: map_at_1000 value: 52.349000000000004 - type: map_at_3 value: 48.17 - type: map_at_5 value: 50.432 - type: mrr_at_1 value: 47.353 - type: mrr_at_10 value: 58.387 - type: mrr_at_100 value: 58.387 - type: mrr_at_1000 value: 58.387 - type: mrr_at_3 value: 56.199 - type: mrr_at_5 value: 57.487 - type: ndcg_at_1 value: 47.353 - type: ndcg_at_10 value: 59.202 - type: ndcg_at_100 value: 58.848 - type: ndcg_at_1000 value: 58.831999999999994 - type: ndcg_at_3 value: 54.112 - type: ndcg_at_5 value: 56.312 - type: precision_at_1 value: 47.353 - type: precision_at_10 value: 11.459 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 26.133 - type: precision_at_5 value: 18.627 - type: recall_at_1 value: 38.421 - type: recall_at_10 value: 71.89 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 71.89 - type: recall_at_3 value: 56.58 - type: recall_at_5 value: 63.125 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.025999999999996 - type: map_at_10 value: 50.590999999999994 - type: map_at_100 value: 51.99700000000001 - type: map_at_1000 value: 52.11599999999999 - type: map_at_3 value: 47.435 - type: map_at_5 value: 49.236000000000004 - type: mrr_at_1 value: 48.28 - type: mrr_at_10 value: 56.814 - type: mrr_at_100 value: 57.446 - type: mrr_at_1000 value: 57.476000000000006 - type: mrr_at_3 value: 54.958 - type: mrr_at_5 value: 56.084999999999994 - type: ndcg_at_1 value: 48.28 - type: ndcg_at_10 value: 56.442 - type: ndcg_at_100 value: 60.651999999999994 - type: ndcg_at_1000 value: 62.187000000000005 - type: ndcg_at_3 value: 52.866 - type: ndcg_at_5 value: 54.515 - type: precision_at_1 value: 48.28 - type: precision_at_10 value: 10.586 - type: precision_at_100 value: 1.6310000000000002 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 25.945 - type: precision_at_5 value: 18.076 - type: recall_at_1 value: 38.025999999999996 - type: recall_at_10 value: 66.11399999999999 - type: recall_at_100 value: 83.339 - type: recall_at_1000 value: 92.413 - type: recall_at_3 value: 54.493 - type: recall_at_5 value: 59.64699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 47.905 - type: map_at_10 value: 61.58 - type: map_at_100 value: 62.605 - type: map_at_1000 value: 62.637 - type: map_at_3 value: 58.074000000000005 - type: map_at_5 value: 60.260000000000005 - type: mrr_at_1 value: 54.42 - type: mrr_at_10 value: 64.847 - type: mrr_at_100 value: 65.403 - type: mrr_at_1000 value: 65.41900000000001 - type: mrr_at_3 value: 62.675000000000004 - type: mrr_at_5 value: 64.101 - type: ndcg_at_1 value: 54.42 - type: ndcg_at_10 value: 67.394 - type: ndcg_at_100 value: 70.846 - type: ndcg_at_1000 value: 71.403 - type: ndcg_at_3 value: 62.025 - type: ndcg_at_5 value: 65.032 - type: precision_at_1 value: 54.42 - type: precision_at_10 value: 10.646 - type: precision_at_100 value: 1.325 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 27.398 - type: precision_at_5 value: 18.796 - type: recall_at_1 value: 47.905 - type: recall_at_10 value: 80.84599999999999 - type: recall_at_100 value: 95.078 - type: recall_at_1000 value: 98.878 - type: recall_at_3 value: 67.05600000000001 - type: recall_at_5 value: 74.261 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.745 - type: map_at_10 value: 41.021 - type: map_at_100 value: 41.021 - type: map_at_1000 value: 41.021 - type: map_at_3 value: 37.714999999999996 - type: map_at_5 value: 39.766 - type: mrr_at_1 value: 33.559 - type: mrr_at_10 value: 43.537 - type: mrr_at_100 value: 43.537 - type: mrr_at_1000 value: 43.537 - type: mrr_at_3 value: 40.546 - type: mrr_at_5 value: 42.439 - type: ndcg_at_1 value: 33.559 - type: ndcg_at_10 value: 46.781 - type: ndcg_at_100 value: 46.781 - type: ndcg_at_1000 value: 46.781 - type: ndcg_at_3 value: 40.516000000000005 - type: ndcg_at_5 value: 43.957 - type: precision_at_1 value: 33.559 - type: precision_at_10 value: 7.198 - type: precision_at_100 value: 0.72 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 17.1 - type: precision_at_5 value: 12.316 - type: recall_at_1 value: 30.745 - type: recall_at_10 value: 62.038000000000004 - type: recall_at_100 value: 62.038000000000004 - type: recall_at_1000 value: 62.038000000000004 - type: recall_at_3 value: 45.378 - type: recall_at_5 value: 53.580000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.637999999999998 - type: map_at_10 value: 31.05 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.05 - type: map_at_3 value: 27.628000000000004 - type: map_at_5 value: 29.767 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 36.131 - type: mrr_at_100 value: 36.131 - type: mrr_at_1000 value: 36.131 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 35.143 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 37.478 - type: ndcg_at_100 value: 37.469 - type: ndcg_at_1000 value: 37.469 - type: ndcg_at_3 value: 31.757999999999996 - type: ndcg_at_5 value: 34.821999999999996 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.188999999999999 - type: precision_at_100 value: 0.719 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 15.837000000000002 - type: precision_at_5 value: 11.841 - type: recall_at_1 value: 19.637999999999998 - type: recall_at_10 value: 51.836000000000006 - type: recall_at_100 value: 51.836000000000006 - type: recall_at_1000 value: 51.836000000000006 - type: recall_at_3 value: 36.384 - type: recall_at_5 value: 43.964 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 34.884 - type: map_at_10 value: 47.88 - type: map_at_100 value: 47.88 - type: map_at_1000 value: 47.88 - type: map_at_3 value: 43.85 - type: map_at_5 value: 46.414 - type: mrr_at_1 value: 43.022 - type: mrr_at_10 value: 53.569 - type: mrr_at_100 value: 53.569 - type: mrr_at_1000 value: 53.569 - type: mrr_at_3 value: 51.075 - type: mrr_at_5 value: 52.725 - type: ndcg_at_1 value: 43.022 - type: ndcg_at_10 value: 54.461000000000006 - type: ndcg_at_100 value: 54.388000000000005 - type: ndcg_at_1000 value: 54.388000000000005 - type: ndcg_at_3 value: 48.864999999999995 - type: ndcg_at_5 value: 52.032000000000004 - type: precision_at_1 value: 43.022 - type: precision_at_10 value: 9.885 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 23.612 - type: precision_at_5 value: 16.997 - type: recall_at_1 value: 34.884 - type: recall_at_10 value: 68.12899999999999 - type: recall_at_100 value: 68.12899999999999 - type: recall_at_1000 value: 68.12899999999999 - type: recall_at_3 value: 52.428 - type: recall_at_5 value: 60.662000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.588 - type: map_at_10 value: 43.85 - type: map_at_100 value: 45.317 - type: map_at_1000 value: 45.408 - type: map_at_3 value: 39.73 - type: map_at_5 value: 42.122 - type: mrr_at_1 value: 38.927 - type: mrr_at_10 value: 49.582 - type: mrr_at_100 value: 50.39 - type: mrr_at_1000 value: 50.426 - type: mrr_at_3 value: 46.518 - type: mrr_at_5 value: 48.271 - type: ndcg_at_1 value: 38.927 - type: ndcg_at_10 value: 50.605999999999995 - type: ndcg_at_100 value: 56.22200000000001 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 44.232 - type: ndcg_at_5 value: 47.233999999999995 - type: precision_at_1 value: 38.927 - type: precision_at_10 value: 9.429 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 21.271 - type: precision_at_5 value: 15.434000000000001 - type: recall_at_1 value: 31.588 - type: recall_at_10 value: 64.836 - type: recall_at_100 value: 88.066 - type: recall_at_1000 value: 97.748 - type: recall_at_3 value: 47.128 - type: recall_at_5 value: 54.954 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.956083333333336 - type: map_at_10 value: 43.33483333333333 - type: map_at_100 value: 44.64883333333333 - type: map_at_1000 value: 44.75 - type: map_at_3 value: 39.87741666666666 - type: map_at_5 value: 41.86766666666667 - type: mrr_at_1 value: 38.06341666666667 - type: mrr_at_10 value: 47.839666666666666 - type: mrr_at_100 value: 48.644000000000005 - type: mrr_at_1000 value: 48.68566666666667 - type: mrr_at_3 value: 45.26358333333334 - type: mrr_at_5 value: 46.790000000000006 - type: ndcg_at_1 value: 38.06341666666667 - type: ndcg_at_10 value: 49.419333333333334 - type: ndcg_at_100 value: 54.50166666666667 - type: ndcg_at_1000 value: 56.161166666666674 - type: ndcg_at_3 value: 43.982416666666666 - type: ndcg_at_5 value: 46.638083333333334 - type: precision_at_1 value: 38.06341666666667 - type: precision_at_10 value: 8.70858333333333 - type: precision_at_100 value: 1.327 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.37816666666667 - type: precision_at_5 value: 14.516333333333334 - type: recall_at_1 value: 31.956083333333336 - type: recall_at_10 value: 62.69458333333334 - type: recall_at_100 value: 84.46433333333334 - type: recall_at_1000 value: 95.58449999999999 - type: recall_at_3 value: 47.52016666666666 - type: recall_at_5 value: 54.36066666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.912 - type: map_at_10 value: 38.291 - type: map_at_100 value: 39.44 - type: map_at_1000 value: 39.528 - type: map_at_3 value: 35.638 - type: map_at_5 value: 37.218 - type: mrr_at_1 value: 32.822 - type: mrr_at_10 value: 41.661 - type: mrr_at_100 value: 42.546 - type: mrr_at_1000 value: 42.603 - type: mrr_at_3 value: 39.238 - type: mrr_at_5 value: 40.726 - type: ndcg_at_1 value: 32.822 - type: ndcg_at_10 value: 43.373 - type: ndcg_at_100 value: 48.638 - type: ndcg_at_1000 value: 50.654999999999994 - type: ndcg_at_3 value: 38.643 - type: ndcg_at_5 value: 41.126000000000005 - type: precision_at_1 value: 32.822 - type: precision_at_10 value: 6.8709999999999996 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.82 - type: precision_at_5 value: 11.718 - type: recall_at_1 value: 28.912 - type: recall_at_10 value: 55.376999999999995 - type: recall_at_100 value: 79.066 - type: recall_at_1000 value: 93.664 - type: recall_at_3 value: 42.569 - type: recall_at_5 value: 48.719 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.181 - type: map_at_10 value: 31.462 - type: map_at_100 value: 32.73 - type: map_at_1000 value: 32.848 - type: map_at_3 value: 28.57 - type: map_at_5 value: 30.182 - type: mrr_at_1 value: 27.185 - type: mrr_at_10 value: 35.846000000000004 - type: mrr_at_100 value: 36.811 - type: mrr_at_1000 value: 36.873 - type: mrr_at_3 value: 33.437 - type: mrr_at_5 value: 34.813 - type: ndcg_at_1 value: 27.185 - type: ndcg_at_10 value: 36.858000000000004 - type: ndcg_at_100 value: 42.501 - type: ndcg_at_1000 value: 44.945 - type: ndcg_at_3 value: 32.066 - type: ndcg_at_5 value: 34.29 - type: precision_at_1 value: 27.185 - type: precision_at_10 value: 6.752 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 15.290000000000001 - type: precision_at_5 value: 11.004999999999999 - type: recall_at_1 value: 22.181 - type: recall_at_10 value: 48.513 - type: recall_at_100 value: 73.418 - type: recall_at_1000 value: 90.306 - type: recall_at_3 value: 35.003 - type: recall_at_5 value: 40.876000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.934999999999995 - type: map_at_10 value: 44.727 - type: map_at_100 value: 44.727 - type: map_at_1000 value: 44.727 - type: map_at_3 value: 40.918 - type: map_at_5 value: 42.961 - type: mrr_at_1 value: 39.646 - type: mrr_at_10 value: 48.898 - type: mrr_at_100 value: 48.898 - type: mrr_at_1000 value: 48.898 - type: mrr_at_3 value: 45.896 - type: mrr_at_5 value: 47.514 - type: ndcg_at_1 value: 39.646 - type: ndcg_at_10 value: 50.817 - type: ndcg_at_100 value: 50.803 - type: ndcg_at_1000 value: 50.803 - type: ndcg_at_3 value: 44.507999999999996 - type: ndcg_at_5 value: 47.259 - type: precision_at_1 value: 39.646 - type: precision_at_10 value: 8.759 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_3 value: 20.274 - type: precision_at_5 value: 14.366000000000001 - type: recall_at_1 value: 33.934999999999995 - type: recall_at_10 value: 65.037 - type: recall_at_100 value: 65.037 - type: recall_at_1000 value: 65.037 - type: recall_at_3 value: 47.439 - type: recall_at_5 value: 54.567 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.058 - type: map_at_10 value: 43.137 - type: map_at_100 value: 43.137 - type: map_at_1000 value: 43.137 - type: map_at_3 value: 39.882 - type: map_at_5 value: 41.379 - type: mrr_at_1 value: 38.933 - type: mrr_at_10 value: 48.344 - type: mrr_at_100 value: 48.344 - type: mrr_at_1000 value: 48.344 - type: mrr_at_3 value: 45.652 - type: mrr_at_5 value: 46.877 - type: ndcg_at_1 value: 38.933 - type: ndcg_at_10 value: 49.964 - type: ndcg_at_100 value: 49.242000000000004 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 44.605 - type: ndcg_at_5 value: 46.501999999999995 - type: precision_at_1 value: 38.933 - type: precision_at_10 value: 9.427000000000001 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 20.685000000000002 - type: precision_at_5 value: 14.585 - type: recall_at_1 value: 32.058 - type: recall_at_10 value: 63.074 - type: recall_at_100 value: 63.074 - type: recall_at_1000 value: 63.074 - type: recall_at_3 value: 47.509 - type: recall_at_5 value: 52.455 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.029000000000003 - type: map_at_10 value: 34.646 - type: map_at_100 value: 34.646 - type: map_at_1000 value: 34.646 - type: map_at_3 value: 31.456 - type: map_at_5 value: 33.138 - type: mrr_at_1 value: 28.281 - type: mrr_at_10 value: 36.905 - type: mrr_at_100 value: 36.905 - type: mrr_at_1000 value: 36.905 - type: mrr_at_3 value: 34.011 - type: mrr_at_5 value: 35.638 - type: ndcg_at_1 value: 28.281 - type: ndcg_at_10 value: 40.159 - type: ndcg_at_100 value: 40.159 - type: ndcg_at_1000 value: 40.159 - type: ndcg_at_3 value: 33.995 - type: ndcg_at_5 value: 36.836999999999996 - type: precision_at_1 value: 28.281 - type: precision_at_10 value: 6.358999999999999 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.064 - type: precision_at_3 value: 14.233 - type: precision_at_5 value: 10.314 - type: recall_at_1 value: 26.029000000000003 - type: recall_at_10 value: 55.08 - type: recall_at_100 value: 55.08 - type: recall_at_1000 value: 55.08 - type: recall_at_3 value: 38.487 - type: recall_at_5 value: 45.308 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.842999999999998 - type: map_at_10 value: 22.101000000000003 - type: map_at_100 value: 24.319 - type: map_at_1000 value: 24.51 - type: map_at_3 value: 18.372 - type: map_at_5 value: 20.323 - type: mrr_at_1 value: 27.948 - type: mrr_at_10 value: 40.321 - type: mrr_at_100 value: 41.262 - type: mrr_at_1000 value: 41.297 - type: mrr_at_3 value: 36.558 - type: mrr_at_5 value: 38.824999999999996 - type: ndcg_at_1 value: 27.948 - type: ndcg_at_10 value: 30.906 - type: ndcg_at_100 value: 38.986 - type: ndcg_at_1000 value: 42.136 - type: ndcg_at_3 value: 24.911 - type: ndcg_at_5 value: 27.168999999999997 - type: precision_at_1 value: 27.948 - type: precision_at_10 value: 9.798 - type: precision_at_100 value: 1.8399999999999999 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 18.328 - type: precision_at_5 value: 14.502 - type: recall_at_1 value: 12.842999999999998 - type: recall_at_10 value: 37.245 - type: recall_at_100 value: 64.769 - type: recall_at_1000 value: 82.055 - type: recall_at_3 value: 23.159 - type: recall_at_5 value: 29.113 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.934000000000001 - type: map_at_10 value: 21.915000000000003 - type: map_at_100 value: 21.915000000000003 - type: map_at_1000 value: 21.915000000000003 - type: map_at_3 value: 14.623 - type: map_at_5 value: 17.841 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 78.994 - type: mrr_at_100 value: 78.994 - type: mrr_at_1000 value: 78.994 - type: mrr_at_3 value: 77.208 - type: mrr_at_5 value: 78.55799999999999 - type: ndcg_at_1 value: 60.62499999999999 - type: ndcg_at_10 value: 46.604 - type: ndcg_at_100 value: 35.653 - type: ndcg_at_1000 value: 35.531 - type: ndcg_at_3 value: 50.605 - type: ndcg_at_5 value: 48.730000000000004 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 37.75 - type: precision_at_100 value: 3.775 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 54.417 - type: precision_at_5 value: 48.15 - type: recall_at_1 value: 8.934000000000001 - type: recall_at_10 value: 28.471000000000004 - type: recall_at_100 value: 28.471000000000004 - type: recall_at_1000 value: 28.471000000000004 - type: recall_at_3 value: 16.019 - type: recall_at_5 value: 21.410999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.81899999999999 - type: map_at_10 value: 78.034 - type: map_at_100 value: 78.034 - type: map_at_1000 value: 78.034 - type: map_at_3 value: 76.43100000000001 - type: map_at_5 value: 77.515 - type: mrr_at_1 value: 71.542 - type: mrr_at_10 value: 81.638 - type: mrr_at_100 value: 81.638 - type: mrr_at_1000 value: 81.638 - type: mrr_at_3 value: 80.403 - type: mrr_at_5 value: 81.256 - type: ndcg_at_1 value: 71.542 - type: ndcg_at_10 value: 82.742 - type: ndcg_at_100 value: 82.741 - type: ndcg_at_1000 value: 82.741 - type: ndcg_at_3 value: 80.039 - type: ndcg_at_5 value: 81.695 - type: precision_at_1 value: 71.542 - type: precision_at_10 value: 10.387 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 31.447999999999997 - type: precision_at_5 value: 19.91 - type: recall_at_1 value: 66.81899999999999 - type: recall_at_10 value: 93.372 - type: recall_at_100 value: 93.372 - type: recall_at_1000 value: 93.372 - type: recall_at_3 value: 86.33 - type: recall_at_5 value: 90.347 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 31.158 - type: map_at_10 value: 52.017 - type: map_at_100 value: 54.259 - type: map_at_1000 value: 54.367 - type: map_at_3 value: 45.738 - type: map_at_5 value: 49.283 - type: mrr_at_1 value: 57.87 - type: mrr_at_10 value: 66.215 - type: mrr_at_100 value: 66.735 - type: mrr_at_1000 value: 66.75 - type: mrr_at_3 value: 64.043 - type: mrr_at_5 value: 65.116 - type: ndcg_at_1 value: 57.87 - type: ndcg_at_10 value: 59.946999999999996 - type: ndcg_at_100 value: 66.31099999999999 - type: ndcg_at_1000 value: 67.75999999999999 - type: ndcg_at_3 value: 55.483000000000004 - type: ndcg_at_5 value: 56.891000000000005 - type: precision_at_1 value: 57.87 - type: precision_at_10 value: 16.497 - type: precision_at_100 value: 2.321 - type: precision_at_1000 value: 0.258 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.067999999999998 - type: recall_at_1 value: 31.158 - type: recall_at_10 value: 67.381 - type: recall_at_100 value: 89.464 - type: recall_at_1000 value: 97.989 - type: recall_at_3 value: 50.553000000000004 - type: recall_at_5 value: 57.824 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 42.073 - type: map_at_10 value: 72.418 - type: map_at_100 value: 73.175 - type: map_at_1000 value: 73.215 - type: map_at_3 value: 68.791 - type: map_at_5 value: 71.19 - type: mrr_at_1 value: 84.146 - type: mrr_at_10 value: 88.994 - type: mrr_at_100 value: 89.116 - type: mrr_at_1000 value: 89.12 - type: mrr_at_3 value: 88.373 - type: mrr_at_5 value: 88.82 - type: ndcg_at_1 value: 84.146 - type: ndcg_at_10 value: 79.404 - type: ndcg_at_100 value: 81.83200000000001 - type: ndcg_at_1000 value: 82.524 - type: ndcg_at_3 value: 74.595 - type: ndcg_at_5 value: 77.474 - type: precision_at_1 value: 84.146 - type: precision_at_10 value: 16.753999999999998 - type: precision_at_100 value: 1.8599999999999999 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 48.854 - type: precision_at_5 value: 31.579 - type: recall_at_1 value: 42.073 - type: recall_at_10 value: 83.768 - type: recall_at_100 value: 93.018 - type: recall_at_1000 value: 97.481 - type: recall_at_3 value: 73.282 - type: recall_at_5 value: 78.947 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.698 - type: map_at_10 value: 34.585 - type: map_at_100 value: 35.782000000000004 - type: map_at_1000 value: 35.825 - type: map_at_3 value: 30.397999999999996 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 22.192 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 36.218 - type: mrr_at_1000 value: 36.256 - type: mrr_at_3 value: 30.986000000000004 - type: mrr_at_5 value: 33.268 - type: ndcg_at_1 value: 22.192 - type: ndcg_at_10 value: 41.957 - type: ndcg_at_100 value: 47.658 - type: ndcg_at_1000 value: 48.697 - type: ndcg_at_3 value: 33.433 - type: ndcg_at_5 value: 37.551 - type: precision_at_1 value: 22.192 - type: precision_at_10 value: 6.781 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.365 - type: precision_at_5 value: 10.713000000000001 - type: recall_at_1 value: 21.698 - type: recall_at_10 value: 64.79 - type: recall_at_100 value: 91.071 - type: recall_at_1000 value: 98.883 - type: recall_at_3 value: 41.611 - type: recall_at_5 value: 51.459999999999994 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.52153488185864 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 36.80090398444147 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.920999999999999 - type: map_at_10 value: 16.049 - type: map_at_100 value: 16.049 - type: map_at_1000 value: 16.049 - type: map_at_3 value: 11.865 - type: map_at_5 value: 13.657 - type: mrr_at_1 value: 53.87 - type: mrr_at_10 value: 62.291 - type: mrr_at_100 value: 62.291 - type: mrr_at_1000 value: 62.291 - type: mrr_at_3 value: 60.681 - type: mrr_at_5 value: 61.61 - type: ndcg_at_1 value: 51.23799999999999 - type: ndcg_at_10 value: 40.892 - type: ndcg_at_100 value: 26.951999999999998 - type: ndcg_at_1000 value: 26.474999999999998 - type: ndcg_at_3 value: 46.821 - type: ndcg_at_5 value: 44.333 - type: precision_at_1 value: 53.251000000000005 - type: precision_at_10 value: 30.124000000000002 - type: precision_at_100 value: 3.012 - type: precision_at_1000 value: 0.301 - type: precision_at_3 value: 43.55 - type: precision_at_5 value: 38.266 - type: recall_at_1 value: 6.920999999999999 - type: recall_at_10 value: 20.852 - type: recall_at_100 value: 20.852 - type: recall_at_1000 value: 20.852 - type: recall_at_3 value: 13.628000000000002 - type: recall_at_5 value: 16.273 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 46.827999999999996 - type: map_at_10 value: 63.434000000000005 - type: map_at_100 value: 63.434000000000005 - type: map_at_1000 value: 63.434000000000005 - type: map_at_3 value: 59.794000000000004 - type: map_at_5 value: 62.08 - type: mrr_at_1 value: 52.288999999999994 - type: mrr_at_10 value: 65.95 - type: mrr_at_100 value: 65.95 - type: mrr_at_1000 value: 65.95 - type: mrr_at_3 value: 63.413 - type: mrr_at_5 value: 65.08 - type: ndcg_at_1 value: 52.288999999999994 - type: ndcg_at_10 value: 70.301 - type: ndcg_at_100 value: 70.301 - type: ndcg_at_1000 value: 70.301 - type: ndcg_at_3 value: 63.979 - type: ndcg_at_5 value: 67.582 - type: precision_at_1 value: 52.288999999999994 - type: precision_at_10 value: 10.576 - type: precision_at_100 value: 1.058 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 28.177000000000003 - type: precision_at_5 value: 19.073 - type: recall_at_1 value: 46.827999999999996 - type: recall_at_10 value: 88.236 - type: recall_at_100 value: 88.236 - type: recall_at_1000 value: 88.236 - type: recall_at_3 value: 72.371 - type: recall_at_5 value: 80.56 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.652 - type: map_at_10 value: 85.953 - type: map_at_100 value: 85.953 - type: map_at_1000 value: 85.953 - type: map_at_3 value: 83.05399999999999 - type: map_at_5 value: 84.89 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.473 - type: mrr_at_100 value: 88.473 - type: mrr_at_1000 value: 88.473 - type: mrr_at_3 value: 87.592 - type: mrr_at_5 value: 88.211 - type: ndcg_at_1 value: 82.44 - type: ndcg_at_10 value: 89.467 - type: ndcg_at_100 value: 89.33 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 86.822 - type: ndcg_at_5 value: 88.307 - type: precision_at_1 value: 82.44 - type: precision_at_10 value: 13.616 - type: precision_at_100 value: 1.362 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 38.117000000000004 - type: precision_at_5 value: 25.05 - type: recall_at_1 value: 71.652 - type: recall_at_10 value: 96.224 - type: recall_at_100 value: 96.224 - type: recall_at_1000 value: 96.224 - type: recall_at_3 value: 88.571 - type: recall_at_5 value: 92.812 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 61.295010338050474 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 67.26380819328142 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.683 - type: map_at_10 value: 14.924999999999999 - type: map_at_100 value: 17.532 - type: map_at_1000 value: 17.875 - type: map_at_3 value: 10.392 - type: map_at_5 value: 12.592 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 39.951 - type: mrr_at_100 value: 41.025 - type: mrr_at_1000 value: 41.056 - type: mrr_at_3 value: 36.317 - type: mrr_at_5 value: 38.412 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.410999999999998 - type: ndcg_at_100 value: 33.79 - type: ndcg_at_1000 value: 39.035 - type: ndcg_at_3 value: 22.845 - type: ndcg_at_5 value: 20.080000000000002 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 12.790000000000001 - type: precision_at_100 value: 2.633 - type: precision_at_1000 value: 0.388 - type: precision_at_3 value: 21.367 - type: precision_at_5 value: 17.7 - type: recall_at_1 value: 5.683 - type: recall_at_10 value: 25.91 - type: recall_at_100 value: 53.443 - type: recall_at_1000 value: 78.73 - type: recall_at_3 value: 13.003 - type: recall_at_5 value: 17.932000000000002 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.677978681023 - type: cos_sim_spearman value: 83.13093441058189 - type: euclidean_pearson value: 83.35535759341572 - type: euclidean_spearman value: 83.42583744219611 - type: manhattan_pearson value: 83.2243124045889 - type: manhattan_spearman value: 83.39801618652632 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.68960206569666 - type: cos_sim_spearman value: 77.3368966488535 - type: euclidean_pearson value: 77.62828980560303 - type: euclidean_spearman value: 76.77951481444651 - type: manhattan_pearson value: 77.88637240839041 - type: manhattan_spearman value: 77.22157841466188 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.18745821650724 - type: cos_sim_spearman value: 85.04423285574542 - type: euclidean_pearson value: 85.46604816931023 - type: euclidean_spearman value: 85.5230593932974 - type: manhattan_pearson value: 85.57912805986261 - type: manhattan_spearman value: 85.65955905111873 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.6715333300355 - type: cos_sim_spearman value: 82.9058522514908 - type: euclidean_pearson value: 83.9640357424214 - type: euclidean_spearman value: 83.60415457472637 - type: manhattan_pearson value: 84.05621005853469 - type: manhattan_spearman value: 83.87077724707746 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.82422928098886 - type: cos_sim_spearman value: 88.12660311894628 - type: euclidean_pearson value: 87.50974805056555 - type: euclidean_spearman value: 87.91957275596677 - type: manhattan_pearson value: 87.74119404878883 - type: manhattan_spearman value: 88.2808922165719 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.80605838552093 - type: cos_sim_spearman value: 86.24123388765678 - type: euclidean_pearson value: 85.32648347339814 - type: euclidean_spearman value: 85.60046671950158 - type: manhattan_pearson value: 85.53800168487811 - type: manhattan_spearman value: 85.89542420480763 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.87540978988132 - type: cos_sim_spearman value: 90.12715295099461 - type: euclidean_pearson value: 91.61085993525275 - type: euclidean_spearman value: 91.31835942311758 - type: manhattan_pearson value: 91.57500202032934 - type: manhattan_spearman value: 91.1790925526635 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 69.87136205329556 - type: cos_sim_spearman value: 68.6253154635078 - type: euclidean_pearson value: 68.91536015034222 - type: euclidean_spearman value: 67.63744649352542 - type: manhattan_pearson value: 69.2000713045275 - type: manhattan_spearman value: 68.16002901587316 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.21849551039082 - type: cos_sim_spearman value: 85.6392959372461 - type: euclidean_pearson value: 85.92050852609488 - type: euclidean_spearman value: 85.97205649009734 - type: manhattan_pearson value: 86.1031154802254 - type: manhattan_spearman value: 86.26791155517466 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.83953958636627 - type: mrr value: 96.71167612344082 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 64.994 - type: map_at_10 value: 74.763 - type: map_at_100 value: 75.127 - type: map_at_1000 value: 75.143 - type: map_at_3 value: 71.824 - type: map_at_5 value: 73.71 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 75.749 - type: mrr_at_100 value: 75.922 - type: mrr_at_1000 value: 75.938 - type: mrr_at_3 value: 73.556 - type: mrr_at_5 value: 74.739 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 79.174 - type: ndcg_at_100 value: 80.41 - type: ndcg_at_1000 value: 80.804 - type: ndcg_at_3 value: 74.361 - type: ndcg_at_5 value: 76.861 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.333 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.778 - type: precision_at_5 value: 19.067 - type: recall_at_1 value: 64.994 - type: recall_at_10 value: 91.822 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.878 - type: recall_at_5 value: 85.172 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72079207920792 - type: cos_sim_ap value: 93.00265215525152 - type: cos_sim_f1 value: 85.06596306068602 - type: cos_sim_precision value: 90.05586592178771 - type: cos_sim_recall value: 80.60000000000001 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 91.22371407479089 - type: dot_f1 value: 82.34693877551021 - type: dot_precision value: 84.0625 - type: dot_recall value: 80.7 - type: euclidean_accuracy value: 99.71881188118812 - type: euclidean_ap value: 92.88449963304728 - type: euclidean_f1 value: 85.19480519480518 - type: euclidean_precision value: 88.64864864864866 - type: euclidean_recall value: 82.0 - type: manhattan_accuracy value: 99.73267326732673 - type: manhattan_ap value: 93.23055393056883 - type: manhattan_f1 value: 85.88957055214725 - type: manhattan_precision value: 87.86610878661088 - type: manhattan_recall value: 84.0 - type: max_accuracy value: 99.73267326732673 - type: max_ap value: 93.23055393056883 - type: max_f1 value: 85.88957055214725 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 77.3305735900358 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 41.32967136540674 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.95514866379359 - type: mrr value: 56.95423245055598 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.783007208997144 - type: cos_sim_spearman value: 30.373444721540533 - type: dot_pearson value: 29.210604111143905 - type: dot_spearman value: 29.98809758085659 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.234 - type: map_at_10 value: 1.894 - type: map_at_100 value: 1.894 - type: map_at_1000 value: 1.894 - type: map_at_3 value: 0.636 - type: map_at_5 value: 1.0 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.667 - type: mrr_at_100 value: 93.667 - type: mrr_at_1000 value: 93.667 - type: mrr_at_3 value: 93.667 - type: mrr_at_5 value: 93.667 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 74.798 - type: ndcg_at_100 value: 16.462 - type: ndcg_at_1000 value: 7.0889999999999995 - type: ndcg_at_3 value: 80.754 - type: ndcg_at_5 value: 77.319 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 7.8 - type: precision_at_1000 value: 0.7799999999999999 - type: precision_at_3 value: 83.333 - type: precision_at_5 value: 80.80000000000001 - type: recall_at_1 value: 0.234 - type: recall_at_10 value: 2.093 - type: recall_at_100 value: 2.093 - type: recall_at_1000 value: 2.093 - type: recall_at_3 value: 0.662 - type: recall_at_5 value: 1.0739999999999998 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 10.866000000000001 - type: map_at_100 value: 10.866000000000001 - type: map_at_1000 value: 10.866000000000001 - type: map_at_3 value: 5.909 - type: map_at_5 value: 7.35 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 53.583000000000006 - type: mrr_at_100 value: 53.583000000000006 - type: mrr_at_1000 value: 53.583000000000006 - type: mrr_at_3 value: 49.32 - type: mrr_at_5 value: 51.769 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 27.926000000000002 - type: ndcg_at_100 value: 22.701 - type: ndcg_at_1000 value: 22.701 - type: ndcg_at_3 value: 32.073 - type: ndcg_at_5 value: 28.327999999999996 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 24.694 - type: precision_at_100 value: 2.469 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 17.702 - type: recall_at_100 value: 17.702 - type: recall_at_1000 value: 17.702 - type: recall_at_3 value: 7.208 - type: recall_at_5 value: 9.748999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.79960000000001 - type: ap value: 15.467565415565815 - type: f1 value: 55.28639823443618 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.7792869269949 - type: f1 value: 65.08597154774318 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 55.70352297774293 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 88.27561542588067 - type: cos_sim_ap value: 81.08262141256193 - type: cos_sim_f1 value: 73.82341501361338 - type: cos_sim_precision value: 72.5720112159062 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 86.66030875603504 - type: dot_ap value: 76.6052349228621 - type: dot_f1 value: 70.13897280966768 - type: dot_precision value: 64.70457079152732 - type: dot_recall value: 76.56992084432717 - type: euclidean_accuracy value: 88.37098408535495 - type: euclidean_ap value: 81.12515230092113 - type: euclidean_f1 value: 74.10338225909379 - type: euclidean_precision value: 71.76761433868974 - type: euclidean_recall value: 76.59630606860158 - type: manhattan_accuracy value: 88.34118137926924 - type: manhattan_ap value: 80.95751834536561 - type: manhattan_f1 value: 73.9119496855346 - type: manhattan_precision value: 70.625 - type: manhattan_recall value: 77.5197889182058 - type: max_accuracy value: 88.37098408535495 - type: max_ap value: 81.12515230092113 - type: max_f1 value: 74.10338225909379 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.79896767182831 - type: cos_sim_ap value: 87.40071784061065 - type: cos_sim_f1 value: 79.87753144712087 - type: cos_sim_precision value: 76.67304015296367 - type: cos_sim_recall value: 83.3615645210964 - type: dot_accuracy value: 88.95486474948578 - type: dot_ap value: 86.00227979119943 - type: dot_f1 value: 78.54601474525914 - type: dot_precision value: 75.00525394045535 - type: dot_recall value: 82.43763473975977 - type: euclidean_accuracy value: 89.7892653393876 - type: euclidean_ap value: 87.42174706480819 - type: euclidean_f1 value: 80.07283321194465 - type: euclidean_precision value: 75.96738529574351 - type: euclidean_recall value: 84.6473668001232 - type: manhattan_accuracy value: 89.8474793340319 - type: manhattan_ap value: 87.47814292587448 - type: manhattan_f1 value: 80.15461150280949 - type: manhattan_precision value: 74.88798234468 - type: manhattan_recall value: 86.21804742839544 - type: max_accuracy value: 89.8474793340319 - type: max_ap value: 87.47814292587448 - type: max_f1 value: 80.15461150280949 --- # Model Summary > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm) - **Paper:** https://arxiv.org/abs/2402.09906 - **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview - **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh | Model | Description | |-------|-------------| | [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT | | [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT | # Use The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference). # Citation ```bibtex @misc{muennighoff2024generative, title={Generative Representational Instruction Tuning}, author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela}, year={2024}, eprint={2402.09906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{}

RichardErkhov/GritLM_-_GritLM-7B-8bits

2024-05-03T17:10:22+00:00

TAGS #transformers #safetensors #mistral #text-generation #conversational #custom_code #arxiv-2402.09906 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models GritLM-7B - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- pipeline\_tag: text-generation inference: true license: apache-2.0 datasets: * GritLM/tulu2 tags: * mteb model-index: * name: GritLM-7B results: + task: type: Classification dataset: type: mteb/amazon\_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 + task: type: Classification dataset: type: mteb/amazon\_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 + task: type: Classification dataset: type: mteb/amazon\_reviews\_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 + task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.478 - type: map\_at\_10 value: 54.955 - type: map\_at\_100 value: 54.955 - type: map\_at\_1000 value: 54.955 - type: map\_at\_3 value: 50.888999999999996 - type: map\_at\_5 value: 53.349999999999994 - type: mrr\_at\_1 value: 39.757999999999996 - type: mrr\_at\_10 value: 55.449000000000005 - type: mrr\_at\_100 value: 55.449000000000005 - type: mrr\_at\_1000 value: 55.449000000000005 - type: mrr\_at\_3 value: 51.37500000000001 - type: mrr\_at\_5 value: 53.822 - type: ndcg\_at\_1 value: 38.478 - type: ndcg\_at\_10 value: 63.239999999999995 - type: ndcg\_at\_100 value: 63.239999999999995 - type: ndcg\_at\_1000 value: 63.239999999999995 - type: ndcg\_at\_3 value: 54.935 - type: ndcg\_at\_5 value: 59.379000000000005 - type: precision\_at\_1 value: 38.478 - type: precision\_at\_10 value: 8.933 - type: precision\_at\_100 value: 0.893 - type: precision\_at\_1000 value: 0.089 - type: precision\_at\_3 value: 22.214 - type: precision\_at\_5 value: 15.491 - type: recall\_at\_1 value: 38.478 - type: recall\_at\_10 value: 89.331 - type: recall\_at\_100 value: 89.331 - type: recall\_at\_1000 value: 89.331 - type: recall\_at\_3 value: 66.643 - type: recall\_at\_5 value: 77.45400000000001 + task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v\_measure value: 51.67144081472449 + task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v\_measure value: 48.11256154264126 + task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 + task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos\_sim\_pearson value: 88.1935203751726 - type: cos\_sim\_spearman value: 86.35497970498659 - type: euclidean\_pearson value: 85.46910708503744 - type: euclidean\_spearman value: 85.13928935405485 - type: manhattan\_pearson value: 85.68373836333303 - type: manhattan\_spearman value: 85.40013867117746 + task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v\_measure value: 40.86793640310432 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v\_measure value: 39.80291334130727 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.421 - type: map\_at\_10 value: 52.349000000000004 - type: map\_at\_100 value: 52.349000000000004 - type: map\_at\_1000 value: 52.349000000000004 - type: map\_at\_3 value: 48.17 - type: map\_at\_5 value: 50.432 - type: mrr\_at\_1 value: 47.353 - type: mrr\_at\_10 value: 58.387 - type: mrr\_at\_100 value: 58.387 - type: mrr\_at\_1000 value: 58.387 - type: mrr\_at\_3 value: 56.199 - type: mrr\_at\_5 value: 57.487 - type: ndcg\_at\_1 value: 47.353 - type: ndcg\_at\_10 value: 59.202 - type: ndcg\_at\_100 value: 58.848 - type: ndcg\_at\_1000 value: 58.831999999999994 - type: ndcg\_at\_3 value: 54.112 - type: ndcg\_at\_5 value: 56.312 - type: precision\_at\_1 value: 47.353 - type: precision\_at\_10 value: 11.459 - type: precision\_at\_100 value: 1.146 - type: precision\_at\_1000 value: 0.11499999999999999 - type: precision\_at\_3 value: 26.133 - type: precision\_at\_5 value: 18.627 - type: recall\_at\_1 value: 38.421 - type: recall\_at\_10 value: 71.89 - type: recall\_at\_100 value: 71.89 - type: recall\_at\_1000 value: 71.89 - type: recall\_at\_3 value: 56.58 - type: recall\_at\_5 value: 63.125 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.025999999999996 - type: map\_at\_10 value: 50.590999999999994 - type: map\_at\_100 value: 51.99700000000001 - type: map\_at\_1000 value: 52.11599999999999 - type: map\_at\_3 value: 47.435 - type: map\_at\_5 value: 49.236000000000004 - type: mrr\_at\_1 value: 48.28 - type: mrr\_at\_10 value: 56.814 - type: mrr\_at\_100 value: 57.446 - type: mrr\_at\_1000 value: 57.476000000000006 - type: mrr\_at\_3 value: 54.958 - type: mrr\_at\_5 value: 56.084999999999994 - type: ndcg\_at\_1 value: 48.28 - type: ndcg\_at\_10 value: 56.442 - type: ndcg\_at\_100 value: 60.651999999999994 - type: ndcg\_at\_1000 value: 62.187000000000005 - type: ndcg\_at\_3 value: 52.866 - type: ndcg\_at\_5 value: 54.515 - type: precision\_at\_1 value: 48.28 - type: precision\_at\_10 value: 10.586 - type: precision\_at\_100 value: 1.6310000000000002 - type: precision\_at\_1000 value: 0.20600000000000002 - type: precision\_at\_3 value: 25.945 - type: precision\_at\_5 value: 18.076 - type: recall\_at\_1 value: 38.025999999999996 - type: recall\_at\_10 value: 66.11399999999999 - type: recall\_at\_100 value: 83.339 - type: recall\_at\_1000 value: 92.413 - type: recall\_at\_3 value: 54.493 - type: recall\_at\_5 value: 59.64699999999999 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 47.905 - type: map\_at\_10 value: 61.58 - type: map\_at\_100 value: 62.605 - type: map\_at\_1000 value: 62.637 - type: map\_at\_3 value: 58.074000000000005 - type: map\_at\_5 value: 60.260000000000005 - type: mrr\_at\_1 value: 54.42 - type: mrr\_at\_10 value: 64.847 - type: mrr\_at\_100 value: 65.403 - type: mrr\_at\_1000 value: 65.41900000000001 - type: mrr\_at\_3 value: 62.675000000000004 - type: mrr\_at\_5 value: 64.101 - type: ndcg\_at\_1 value: 54.42 - type: ndcg\_at\_10 value: 67.394 - type: ndcg\_at\_100 value: 70.846 - type: ndcg\_at\_1000 value: 71.403 - type: ndcg\_at\_3 value: 62.025 - type: ndcg\_at\_5 value: 65.032 - type: precision\_at\_1 value: 54.42 - type: precision\_at\_10 value: 10.646 - type: precision\_at\_100 value: 1.325 - type: precision\_at\_1000 value: 0.13999999999999999 - type: precision\_at\_3 value: 27.398 - type: precision\_at\_5 value: 18.796 - type: recall\_at\_1 value: 47.905 - type: recall\_at\_10 value: 80.84599999999999 - type: recall\_at\_100 value: 95.078 - type: recall\_at\_1000 value: 98.878 - type: recall\_at\_3 value: 67.05600000000001 - type: recall\_at\_5 value: 74.261 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 30.745 - type: map\_at\_10 value: 41.021 - type: map\_at\_100 value: 41.021 - type: map\_at\_1000 value: 41.021 - type: map\_at\_3 value: 37.714999999999996 - type: map\_at\_5 value: 39.766 - type: mrr\_at\_1 value: 33.559 - type: mrr\_at\_10 value: 43.537 - type: mrr\_at\_100 value: 43.537 - type: mrr\_at\_1000 value: 43.537 - type: mrr\_at\_3 value: 40.546 - type: mrr\_at\_5 value: 42.439 - type: ndcg\_at\_1 value: 33.559 - type: ndcg\_at\_10 value: 46.781 - type: ndcg\_at\_100 value: 46.781 - type: ndcg\_at\_1000 value: 46.781 - type: ndcg\_at\_3 value: 40.516000000000005 - type: ndcg\_at\_5 value: 43.957 - type: precision\_at\_1 value: 33.559 - type: precision\_at\_10 value: 7.198 - type: precision\_at\_100 value: 0.72 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 17.1 - type: precision\_at\_5 value: 12.316 - type: recall\_at\_1 value: 30.745 - type: recall\_at\_10 value: 62.038000000000004 - type: recall\_at\_100 value: 62.038000000000004 - type: recall\_at\_1000 value: 62.038000000000004 - type: recall\_at\_3 value: 45.378 - type: recall\_at\_5 value: 53.580000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 19.637999999999998 - type: map\_at\_10 value: 31.05 - type: map\_at\_100 value: 31.05 - type: map\_at\_1000 value: 31.05 - type: map\_at\_3 value: 27.628000000000004 - type: map\_at\_5 value: 29.767 - type: mrr\_at\_1 value: 25.0 - type: mrr\_at\_10 value: 36.131 - type: mrr\_at\_100 value: 36.131 - type: mrr\_at\_1000 value: 36.131 - type: mrr\_at\_3 value: 33.333 - type: mrr\_at\_5 value: 35.143 - type: ndcg\_at\_1 value: 25.0 - type: ndcg\_at\_10 value: 37.478 - type: ndcg\_at\_100 value: 37.469 - type: ndcg\_at\_1000 value: 37.469 - type: ndcg\_at\_3 value: 31.757999999999996 - type: ndcg\_at\_5 value: 34.821999999999996 - type: precision\_at\_1 value: 25.0 - type: precision\_at\_10 value: 7.188999999999999 - type: precision\_at\_100 value: 0.719 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 15.837000000000002 - type: precision\_at\_5 value: 11.841 - type: recall\_at\_1 value: 19.637999999999998 - type: recall\_at\_10 value: 51.836000000000006 - type: recall\_at\_100 value: 51.836000000000006 - type: recall\_at\_1000 value: 51.836000000000006 - type: recall\_at\_3 value: 36.384 - type: recall\_at\_5 value: 43.964 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 34.884 - type: map\_at\_10 value: 47.88 - type: map\_at\_100 value: 47.88 - type: map\_at\_1000 value: 47.88 - type: map\_at\_3 value: 43.85 - type: map\_at\_5 value: 46.414 - type: mrr\_at\_1 value: 43.022 - type: mrr\_at\_10 value: 53.569 - type: mrr\_at\_100 value: 53.569 - type: mrr\_at\_1000 value: 53.569 - type: mrr\_at\_3 value: 51.075 - type: mrr\_at\_5 value: 52.725 - type: ndcg\_at\_1 value: 43.022 - type: ndcg\_at\_10 value: 54.461000000000006 - type: ndcg\_at\_100 value: 54.388000000000005 - type: ndcg\_at\_1000 value: 54.388000000000005 - type: ndcg\_at\_3 value: 48.864999999999995 - type: ndcg\_at\_5 value: 52.032000000000004 - type: precision\_at\_1 value: 43.022 - type: precision\_at\_10 value: 9.885 - type: precision\_at\_100 value: 0.988 - type: precision\_at\_1000 value: 0.099 - type: precision\_at\_3 value: 23.612 - type: precision\_at\_5 value: 16.997 - type: recall\_at\_1 value: 34.884 - type: recall\_at\_10 value: 68.12899999999999 - type: recall\_at\_100 value: 68.12899999999999 - type: recall\_at\_1000 value: 68.12899999999999 - type: recall\_at\_3 value: 52.428 - type: recall\_at\_5 value: 60.662000000000006 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.588 - type: map\_at\_10 value: 43.85 - type: map\_at\_100 value: 45.317 - type: map\_at\_1000 value: 45.408 - type: map\_at\_3 value: 39.73 - type: map\_at\_5 value: 42.122 - type: mrr\_at\_1 value: 38.927 - type: mrr\_at\_10 value: 49.582 - type: mrr\_at\_100 value: 50.39 - type: mrr\_at\_1000 value: 50.426 - type: mrr\_at\_3 value: 46.518 - type: mrr\_at\_5 value: 48.271 - type: ndcg\_at\_1 value: 38.927 - type: ndcg\_at\_10 value: 50.605999999999995 - type: ndcg\_at\_100 value: 56.22200000000001 - type: ndcg\_at\_1000 value: 57.724 - type: ndcg\_at\_3 value: 44.232 - type: ndcg\_at\_5 value: 47.233999999999995 - type: precision\_at\_1 value: 38.927 - type: precision\_at\_10 value: 9.429 - type: precision\_at\_100 value: 1.435 - type: precision\_at\_1000 value: 0.172 - type: precision\_at\_3 value: 21.271 - type: precision\_at\_5 value: 15.434000000000001 - type: recall\_at\_1 value: 31.588 - type: recall\_at\_10 value: 64.836 - type: recall\_at\_100 value: 88.066 - type: recall\_at\_1000 value: 97.748 - type: recall\_at\_3 value: 47.128 - type: recall\_at\_5 value: 54.954 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.956083333333336 - type: map\_at\_10 value: 43.33483333333333 - type: map\_at\_100 value: 44.64883333333333 - type: map\_at\_1000 value: 44.75 - type: map\_at\_3 value: 39.87741666666666 - type: map\_at\_5 value: 41.86766666666667 - type: mrr\_at\_1 value: 38.06341666666667 - type: mrr\_at\_10 value: 47.839666666666666 - type: mrr\_at\_100 value: 48.644000000000005 - type: mrr\_at\_1000 value: 48.68566666666667 - type: mrr\_at\_3 value: 45.26358333333334 - type: mrr\_at\_5 value: 46.790000000000006 - type: ndcg\_at\_1 value: 38.06341666666667 - type: ndcg\_at\_10 value: 49.419333333333334 - type: ndcg\_at\_100 value: 54.50166666666667 - type: ndcg\_at\_1000 value: 56.161166666666674 - type: ndcg\_at\_3 value: 43.982416666666666 - type: ndcg\_at\_5 value: 46.638083333333334 - type: precision\_at\_1 value: 38.06341666666667 - type: precision\_at\_10 value: 8.70858333333333 - type: precision\_at\_100 value: 1.327 - type: precision\_at\_1000 value: 0.165 - type: precision\_at\_3 value: 20.37816666666667 - type: precision\_at\_5 value: 14.516333333333334 - type: recall\_at\_1 value: 31.956083333333336 - type: recall\_at\_10 value: 62.69458333333334 - type: recall\_at\_100 value: 84.46433333333334 - type: recall\_at\_1000 value: 95.58449999999999 - type: recall\_at\_3 value: 47.52016666666666 - type: recall\_at\_5 value: 54.36066666666666 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 28.912 - type: map\_at\_10 value: 38.291 - type: map\_at\_100 value: 39.44 - type: map\_at\_1000 value: 39.528 - type: map\_at\_3 value: 35.638 - type: map\_at\_5 value: 37.218 - type: mrr\_at\_1 value: 32.822 - type: mrr\_at\_10 value: 41.661 - type: mrr\_at\_100 value: 42.546 - type: mrr\_at\_1000 value: 42.603 - type: mrr\_at\_3 value: 39.238 - type: mrr\_at\_5 value: 40.726 - type: ndcg\_at\_1 value: 32.822 - type: ndcg\_at\_10 value: 43.373 - type: ndcg\_at\_100 value: 48.638 - type: ndcg\_at\_1000 value: 50.654999999999994 - type: ndcg\_at\_3 value: 38.643 - type: ndcg\_at\_5 value: 41.126000000000005 - type: precision\_at\_1 value: 32.822 - type: precision\_at\_10 value: 6.8709999999999996 - type: precision\_at\_100 value: 1.032 - type: precision\_at\_1000 value: 0.128 - type: precision\_at\_3 value: 16.82 - type: precision\_at\_5 value: 11.718 - type: recall\_at\_1 value: 28.912 - type: recall\_at\_10 value: 55.376999999999995 - type: recall\_at\_100 value: 79.066 - type: recall\_at\_1000 value: 93.664 - type: recall\_at\_3 value: 42.569 - type: recall\_at\_5 value: 48.719 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 22.181 - type: map\_at\_10 value: 31.462 - type: map\_at\_100 value: 32.73 - type: map\_at\_1000 value: 32.848 - type: map\_at\_3 value: 28.57 - type: map\_at\_5 value: 30.182 - type: mrr\_at\_1 value: 27.185 - type: mrr\_at\_10 value: 35.846000000000004 - type: mrr\_at\_100 value: 36.811 - type: mrr\_at\_1000 value: 36.873 - type: mrr\_at\_3 value: 33.437 - type: mrr\_at\_5 value: 34.813 - type: ndcg\_at\_1 value: 27.185 - type: ndcg\_at\_10 value: 36.858000000000004 - type: ndcg\_at\_100 value: 42.501 - type: ndcg\_at\_1000 value: 44.945 - type: ndcg\_at\_3 value: 32.066 - type: ndcg\_at\_5 value: 34.29 - type: precision\_at\_1 value: 27.185 - type: precision\_at\_10 value: 6.752 - type: precision\_at\_100 value: 1.111 - type: precision\_at\_1000 value: 0.151 - type: precision\_at\_3 value: 15.290000000000001 - type: precision\_at\_5 value: 11.004999999999999 - type: recall\_at\_1 value: 22.181 - type: recall\_at\_10 value: 48.513 - type: recall\_at\_100 value: 73.418 - type: recall\_at\_1000 value: 90.306 - type: recall\_at\_3 value: 35.003 - type: recall\_at\_5 value: 40.876000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 33.934999999999995 - type: map\_at\_10 value: 44.727 - type: map\_at\_100 value: 44.727 - type: map\_at\_1000 value: 44.727 - type: map\_at\_3 value: 40.918 - type: map\_at\_5 value: 42.961 - type: mrr\_at\_1 value: 39.646 - type: mrr\_at\_10 value: 48.898 - type: mrr\_at\_100 value: 48.898 - type: mrr\_at\_1000 value: 48.898 - type: mrr\_at\_3 value: 45.896 - type: mrr\_at\_5 value: 47.514 - type: ndcg\_at\_1 value: 39.646 - type: ndcg\_at\_10 value: 50.817 - type: ndcg\_at\_100 value: 50.803 - type: ndcg\_at\_1000 value: 50.803 - type: ndcg\_at\_3 value: 44.507999999999996 - type: ndcg\_at\_5 value: 47.259 - type: precision\_at\_1 value: 39.646 - type: precision\_at\_10 value: 8.759 - type: precision\_at\_100 value: 0.876 - type: precision\_at\_1000 value: 0.08800000000000001 - type: precision\_at\_3 value: 20.274 - type: precision\_at\_5 value: 14.366000000000001 - type: recall\_at\_1 value: 33.934999999999995 - type: recall\_at\_10 value: 65.037 - type: recall\_at\_100 value: 65.037 - type: recall\_at\_1000 value: 65.037 - type: recall\_at\_3 value: 47.439 - type: recall\_at\_5 value: 54.567 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 32.058 - type: map\_at\_10 value: 43.137 - type: map\_at\_100 value: 43.137 - type: map\_at\_1000 value: 43.137 - type: map\_at\_3 value: 39.882 - type: map\_at\_5 value: 41.379 - type: mrr\_at\_1 value: 38.933 - type: mrr\_at\_10 value: 48.344 - type: mrr\_at\_100 value: 48.344 - type: mrr\_at\_1000 value: 48.344 - type: mrr\_at\_3 value: 45.652 - type: mrr\_at\_5 value: 46.877 - type: ndcg\_at\_1 value: 38.933 - type: ndcg\_at\_10 value: 49.964 - type: ndcg\_at\_100 value: 49.242000000000004 - type: ndcg\_at\_1000 value: 49.222 - type: ndcg\_at\_3 value: 44.605 - type: ndcg\_at\_5 value: 46.501999999999995 - type: precision\_at\_1 value: 38.933 - type: precision\_at\_10 value: 9.427000000000001 - type: precision\_at\_100 value: 0.943 - type: precision\_at\_1000 value: 0.094 - type: precision\_at\_3 value: 20.685000000000002 - type: precision\_at\_5 value: 14.585 - type: recall\_at\_1 value: 32.058 - type: recall\_at\_10 value: 63.074 - type: recall\_at\_100 value: 63.074 - type: recall\_at\_1000 value: 63.074 - type: recall\_at\_3 value: 47.509 - type: recall\_at\_5 value: 52.455 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 26.029000000000003 - type: map\_at\_10 value: 34.646 - type: map\_at\_100 value: 34.646 - type: map\_at\_1000 value: 34.646 - type: map\_at\_3 value: 31.456 - type: map\_at\_5 value: 33.138 - type: mrr\_at\_1 value: 28.281 - type: mrr\_at\_10 value: 36.905 - type: mrr\_at\_100 value: 36.905 - type: mrr\_at\_1000 value: 36.905 - type: mrr\_at\_3 value: 34.011 - type: mrr\_at\_5 value: 35.638 - type: ndcg\_at\_1 value: 28.281 - type: ndcg\_at\_10 value: 40.159 - type: ndcg\_at\_100 value: 40.159 - type: ndcg\_at\_1000 value: 40.159 - type: ndcg\_at\_3 value: 33.995 - type: ndcg\_at\_5 value: 36.836999999999996 - type: precision\_at\_1 value: 28.281 - type: precision\_at\_10 value: 6.358999999999999 - type: precision\_at\_100 value: 0.636 - type: precision\_at\_1000 value: 0.064 - type: precision\_at\_3 value: 14.233 - type: precision\_at\_5 value: 10.314 - type: recall\_at\_1 value: 26.029000000000003 - type: recall\_at\_10 value: 55.08 - type: recall\_at\_100 value: 55.08 - type: recall\_at\_1000 value: 55.08 - type: recall\_at\_3 value: 38.487 - type: recall\_at\_5 value: 45.308 + task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 12.842999999999998 - type: map\_at\_10 value: 22.101000000000003 - type: map\_at\_100 value: 24.319 - type: map\_at\_1000 value: 24.51 - type: map\_at\_3 value: 18.372 - type: map\_at\_5 value: 20.323 - type: mrr\_at\_1 value: 27.948 - type: mrr\_at\_10 value: 40.321 - type: mrr\_at\_100 value: 41.262 - type: mrr\_at\_1000 value: 41.297 - type: mrr\_at\_3 value: 36.558 - type: mrr\_at\_5 value: 38.824999999999996 - type: ndcg\_at\_1 value: 27.948 - type: ndcg\_at\_10 value: 30.906 - type: ndcg\_at\_100 value: 38.986 - type: ndcg\_at\_1000 value: 42.136 - type: ndcg\_at\_3 value: 24.911 - type: ndcg\_at\_5 value: 27.168999999999997 - type: precision\_at\_1 value: 27.948 - type: precision\_at\_10 value: 9.798 - type: precision\_at\_100 value: 1.8399999999999999 - type: precision\_at\_1000 value: 0.243 - type: precision\_at\_3 value: 18.328 - type: precision\_at\_5 value: 14.502 - type: recall\_at\_1 value: 12.842999999999998 - type: recall\_at\_10 value: 37.245 - type: recall\_at\_100 value: 64.769 - type: recall\_at\_1000 value: 82.055 - type: recall\_at\_3 value: 23.159 - type: recall\_at\_5 value: 29.113 + task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map\_at\_1 value: 8.934000000000001 - type: map\_at\_10 value: 21.915000000000003 - type: map\_at\_100 value: 21.915000000000003 - type: map\_at\_1000 value: 21.915000000000003 - type: map\_at\_3 value: 14.623 - type: map\_at\_5 value: 17.841 - type: mrr\_at\_1 value: 71.25 - type: mrr\_at\_10 value: 78.994 - type: mrr\_at\_100 value: 78.994 - type: mrr\_at\_1000 value: 78.994 - type: mrr\_at\_3 value: 77.208 - type: mrr\_at\_5 value: 78.55799999999999 - type: ndcg\_at\_1 value: 60.62499999999999 - type: ndcg\_at\_10 value: 46.604 - type: ndcg\_at\_100 value: 35.653 - type: ndcg\_at\_1000 value: 35.531 - type: ndcg\_at\_3 value: 50.605 - type: ndcg\_at\_5 value: 48.730000000000004 - type: precision\_at\_1 value: 71.25 - type: precision\_at\_10 value: 37.75 - type: precision\_at\_100 value: 3.775 - type: precision\_at\_1000 value: 0.377 - type: precision\_at\_3 value: 54.417 - type: precision\_at\_5 value: 48.15 - type: recall\_at\_1 value: 8.934000000000001 - type: recall\_at\_10 value: 28.471000000000004 - type: recall\_at\_100 value: 28.471000000000004 - type: recall\_at\_1000 value: 28.471000000000004 - type: recall\_at\_3 value: 16.019 - type: recall\_at\_5 value: 21.410999999999998 + task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 + task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 66.81899999999999 - type: map\_at\_10 value: 78.034 - type: map\_at\_100 value: 78.034 - type: map\_at\_1000 value: 78.034 - type: map\_at\_3 value: 76.43100000000001 - type: map\_at\_5 value: 77.515 - type: mrr\_at\_1 value: 71.542 - type: mrr\_at\_10 value: 81.638 - type: mrr\_at\_100 value: 81.638 - type: mrr\_at\_1000 value: 81.638 - type: mrr\_at\_3 value: 80.403 - type: mrr\_at\_5 value: 81.256 - type: ndcg\_at\_1 value: 71.542 - type: ndcg\_at\_10 value: 82.742 - type: ndcg\_at\_100 value: 82.741 - type: ndcg\_at\_1000 value: 82.741 - type: ndcg\_at\_3 value: 80.039 - type: ndcg\_at\_5 value: 81.695 - type: precision\_at\_1 value: 71.542 - type: precision\_at\_10 value: 10.387 - type: precision\_at\_100 value: 1.039 - type: precision\_at\_1000 value: 0.104 - type: precision\_at\_3 value: 31.447999999999997 - type: precision\_at\_5 value: 19.91 - type: recall\_at\_1 value: 66.81899999999999 - type: recall\_at\_10 value: 93.372 - type: recall\_at\_100 value: 93.372 - type: recall\_at\_1000 value: 93.372 - type: recall\_at\_3 value: 86.33 - type: recall\_at\_5 value: 90.347 + task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.158 - type: map\_at\_10 value: 52.017 - type: map\_at\_100 value: 54.259 - type: map\_at\_1000 value: 54.367 - type: map\_at\_3 value: 45.738 - type: map\_at\_5 value: 49.283 - type: mrr\_at\_1 value: 57.87 - type: mrr\_at\_10 value: 66.215 - type: mrr\_at\_100 value: 66.735 - type: mrr\_at\_1000 value: 66.75 - type: mrr\_at\_3 value: 64.043 - type: mrr\_at\_5 value: 65.116 - type: ndcg\_at\_1 value: 57.87 - type: ndcg\_at\_10 value: 59.946999999999996 - type: ndcg\_at\_100 value: 66.31099999999999 - type: ndcg\_at\_1000 value: 67.75999999999999 - type: ndcg\_at\_3 value: 55.483000000000004 - type: ndcg\_at\_5 value: 56.891000000000005 - type: precision\_at\_1 value: 57.87 - type: precision\_at\_10 value: 16.497 - type: precision\_at\_100 value: 2.321 - type: precision\_at\_1000 value: 0.258 - type: precision\_at\_3 value: 37.14 - type: precision\_at\_5 value: 27.067999999999998 - type: recall\_at\_1 value: 31.158 - type: recall\_at\_10 value: 67.381 - type: recall\_at\_100 value: 89.464 - type: recall\_at\_1000 value: 97.989 - type: recall\_at\_3 value: 50.553000000000004 - type: recall\_at\_5 value: 57.824 + task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map\_at\_1 value: 42.073 - type: map\_at\_10 value: 72.418 - type: map\_at\_100 value: 73.175 - type: map\_at\_1000 value: 73.215 - type: map\_at\_3 value: 68.791 - type: map\_at\_5 value: 71.19 - type: mrr\_at\_1 value: 84.146 - type: mrr\_at\_10 value: 88.994 - type: mrr\_at\_100 value: 89.116 - type: mrr\_at\_1000 value: 89.12 - type: mrr\_at\_3 value: 88.373 - type: mrr\_at\_5 value: 88.82 - type: ndcg\_at\_1 value: 84.146 - type: ndcg\_at\_10 value: 79.404 - type: ndcg\_at\_100 value: 81.83200000000001 - type: ndcg\_at\_1000 value: 82.524 - type: ndcg\_at\_3 value: 74.595 - type: ndcg\_at\_5 value: 77.474 - type: precision\_at\_1 value: 84.146 - type: precision\_at\_10 value: 16.753999999999998 - type: precision\_at\_100 value: 1.8599999999999999 - type: precision\_at\_1000 value: 0.19499999999999998 - type: precision\_at\_3 value: 48.854 - type: precision\_at\_5 value: 31.579 - type: recall\_at\_1 value: 42.073 - type: recall\_at\_10 value: 83.768 - type: recall\_at\_100 value: 93.018 - type: recall\_at\_1000 value: 97.481 - type: recall\_at\_3 value: 73.282 - type: recall\_at\_5 value: 78.947 + task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 + task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map\_at\_1 value: 21.698 - type: map\_at\_10 value: 34.585 - type: map\_at\_100 value: 35.782000000000004 - type: map\_at\_1000 value: 35.825 - type: map\_at\_3 value: 30.397999999999996 - type: map\_at\_5 value: 32.72 - type: mrr\_at\_1 value: 22.192 - type: mrr\_at\_10 value: 35.085 - type: mrr\_at\_100 value: 36.218 - type: mrr\_at\_1000 value: 36.256 - type: mrr\_at\_3 value: 30.986000000000004 - type: mrr\_at\_5 value: 33.268 - type: ndcg\_at\_1 value: 22.192 - type: ndcg\_at\_10 value: 41.957 - type: ndcg\_at\_100 value: 47.658 - type: ndcg\_at\_1000 value: 48.697 - type: ndcg\_at\_3 value: 33.433 - type: ndcg\_at\_5 value: 37.551 - type: precision\_at\_1 value: 22.192 - type: precision\_at\_10 value: 6.781 - type: precision\_at\_100 value: 0.963 - type: precision\_at\_1000 value: 0.105 - type: precision\_at\_3 value: 14.365 - type: precision\_at\_5 value: 10.713000000000001 - type: recall\_at\_1 value: 21.698 - type: recall\_at\_10 value: 64.79 - type: recall\_at\_100 value: 91.071 - type: recall\_at\_1000 value: 98.883 - type: recall\_at\_3 value: 41.611 - type: recall\_at\_5 value: 51.459999999999994 + task: type: Classification dataset: type: mteb/mtop\_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 + task: type: Classification dataset: type: mteb/mtop\_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 + task: type: Classification dataset: type: mteb/amazon\_massive\_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 + task: type: Classification dataset: type: mteb/amazon\_massive\_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v\_measure value: 36.52153488185864 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v\_measure value: 36.80090398444147 + task: type: Reranking dataset: type: mteb/mind\_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 + task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map\_at\_1 value: 6.920999999999999 - type: map\_at\_10 value: 16.049 - type: map\_at\_100 value: 16.049 - type: map\_at\_1000 value: 16.049 - type: map\_at\_3 value: 11.865 - type: map\_at\_5 value: 13.657 - type: mrr\_at\_1 value: 53.87 - type: mrr\_at\_10 value: 62.291 - type: mrr\_at\_100 value: 62.291 - type: mrr\_at\_1000 value: 62.291 - type: mrr\_at\_3 value: 60.681 - type: mrr\_at\_5 value: 61.61 - type: ndcg\_at\_1 value: 51.23799999999999 - type: ndcg\_at\_10 value: 40.892 - type: ndcg\_at\_100 value: 26.951999999999998 - type: ndcg\_at\_1000 value: 26.474999999999998 - type: ndcg\_at\_3 value: 46.821 - type: ndcg\_at\_5 value: 44.333 - type: precision\_at\_1 value: 53.251000000000005 - type: precision\_at\_10 value: 30.124000000000002 - type: precision\_at\_100 value: 3.012 - type: precision\_at\_1000 value: 0.301 - type: precision\_at\_3 value: 43.55 - type: precision\_at\_5 value: 38.266 - type: recall\_at\_1 value: 6.920999999999999 - type: recall\_at\_10 value: 20.852 - type: recall\_at\_100 value: 20.852 - type: recall\_at\_1000 value: 20.852 - type: recall\_at\_3 value: 13.628000000000002 - type: recall\_at\_5 value: 16.273 + task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map\_at\_1 value: 46.827999999999996 - type: map\_at\_10 value: 63.434000000000005 - type: map\_at\_100 value: 63.434000000000005 - type: map\_at\_1000 value: 63.434000000000005 - type: map\_at\_3 value: 59.794000000000004 - type: map\_at\_5 value: 62.08 - type: mrr\_at\_1 value: 52.288999999999994 - type: mrr\_at\_10 value: 65.95 - type: mrr\_at\_100 value: 65.95 - type: mrr\_at\_1000 value: 65.95 - type: mrr\_at\_3 value: 63.413 - type: mrr\_at\_5 value: 65.08 - type: ndcg\_at\_1 value: 52.288999999999994 - type: ndcg\_at\_10 value: 70.301 - type: ndcg\_at\_100 value: 70.301 - type: ndcg\_at\_1000 value: 70.301 - type: ndcg\_at\_3 value: 63.979 - type: ndcg\_at\_5 value: 67.582 - type: precision\_at\_1 value: 52.288999999999994 - type: precision\_at\_10 value: 10.576 - type: precision\_at\_100 value: 1.058 - type: precision\_at\_1000 value: 0.106 - type: precision\_at\_3 value: 28.177000000000003 - type: precision\_at\_5 value: 19.073 - type: recall\_at\_1 value: 46.827999999999996 - type: recall\_at\_10 value: 88.236 - type: recall\_at\_100 value: 88.236 - type: recall\_at\_1000 value: 88.236 - type: recall\_at\_3 value: 72.371 - type: recall\_at\_5 value: 80.56 + task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 71.652 - type: map\_at\_10 value: 85.953 - type: map\_at\_100 value: 85.953 - type: map\_at\_1000 value: 85.953 - type: map\_at\_3 value: 83.05399999999999 - type: map\_at\_5 value: 84.89 - type: mrr\_at\_1 value: 82.42 - type: mrr\_at\_10 value: 88.473 - type: mrr\_at\_100 value: 88.473 - type: mrr\_at\_1000 value: 88.473 - type: mrr\_at\_3 value: 87.592 - type: mrr\_at\_5 value: 88.211 - type: ndcg\_at\_1 value: 82.44 - type: ndcg\_at\_10 value: 89.467 - type: ndcg\_at\_100 value: 89.33 - type: ndcg\_at\_1000 value: 89.33 - type: ndcg\_at\_3 value: 86.822 - type: ndcg\_at\_5 value: 88.307 - type: precision\_at\_1 value: 82.44 - type: precision\_at\_10 value: 13.616 - type: precision\_at\_100 value: 1.362 - type: precision\_at\_1000 value: 0.136 - type: precision\_at\_3 value: 38.117000000000004 - type: precision\_at\_5 value: 25.05 - type: recall\_at\_1 value: 71.652 - type: recall\_at\_10 value: 96.224 - type: recall\_at\_100 value: 96.224 - type: recall\_at\_1000 value: 96.224 - type: recall\_at\_3 value: 88.571 - 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type: recall\_at\_3 value: 78.878 - type: recall\_at\_5 value: 85.172 + task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos\_sim\_accuracy value: 99.72079207920792 - type: cos\_sim\_ap value: 93.00265215525152 - type: cos\_sim\_f1 value: 85.06596306068602 - type: cos\_sim\_precision value: 90.05586592178771 - type: cos\_sim\_recall value: 80.60000000000001 - type: dot\_accuracy value: 99.66039603960397 - type: dot\_ap value: 91.22371407479089 - type: dot\_f1 value: 82.34693877551021 - type: dot\_precision value: 84.0625 - type: dot\_recall value: 80.7 - type: euclidean\_accuracy value: 99.71881188118812 - type: euclidean\_ap value: 92.88449963304728 - type: euclidean\_f1 value: 85.19480519480518 - type: euclidean\_precision value: 88.64864864864866 - type: euclidean\_recall value: 82.0 - type: manhattan\_accuracy value: 99.73267326732673 - 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type: accuracy value: 70.79960000000001 - type: ap value: 15.467565415565815 - type: f1 value: 55.28639823443618 + task: type: Classification dataset: type: mteb/tweet\_sentiment\_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.7792869269949 - type: f1 value: 65.08597154774318 + task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v\_measure value: 55.70352297774293 + task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos\_sim\_accuracy value: 88.27561542588067 - type: cos\_sim\_ap value: 81.08262141256193 - type: cos\_sim\_f1 value: 73.82341501361338 - type: cos\_sim\_precision value: 72.5720112159062 - type: cos\_sim\_recall value: 75.11873350923483 - type: dot\_accuracy value: 86.66030875603504 - type: dot\_ap value: 76.6052349228621 - type: dot\_f1 value: 70.13897280966768 - type: dot\_precision value: 64.70457079152732 - type: dot\_recall value: 76.56992084432717 - type: euclidean\_accuracy value: 88.37098408535495 - type: euclidean\_ap value: 81.12515230092113 - type: euclidean\_f1 value: 74.10338225909379 - type: euclidean\_precision value: 71.76761433868974 - type: euclidean\_recall value: 76.59630606860158 - type: manhattan\_accuracy value: 88.34118137926924 - type: manhattan\_ap value: 80.95751834536561 - type: manhattan\_f1 value: 73.9119496855346 - type: manhattan\_precision value: 70.625 - type: manhattan\_recall value: 77.5197889182058 - type: max\_accuracy value: 88.37098408535495 - type: max\_ap value: 81.12515230092113 - type: max\_f1 value: 74.10338225909379 + task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos\_sim\_accuracy value: 89.79896767182831 - type: cos\_sim\_ap value: 87.40071784061065 - type: cos\_sim\_f1 value: 79.87753144712087 - type: cos\_sim\_precision value: 76.67304015296367 - type: cos\_sim\_recall value: 83.3615645210964 - type: dot\_accuracy value: 88.95486474948578 - type: dot\_ap value: 86.00227979119943 - type: dot\_f1 value: 78.54601474525914 - type: dot\_precision value: 75.00525394045535 - type: dot\_recall value: 82.43763473975977 - type: euclidean\_accuracy value: 89.7892653393876 - type: euclidean\_ap value: 87.42174706480819 - type: euclidean\_f1 value: 80.07283321194465 - type: euclidean\_precision value: 75.96738529574351 - type: euclidean\_recall value: 84.6473668001232 - type: manhattan\_accuracy value: 89.8474793340319 - type: manhattan\_ap value: 87.47814292587448 - type: manhattan\_f1 value: 80.15461150280949 - type: manhattan\_precision value: 74.88798234468 - type: manhattan\_recall value: 86.21804742839544 - type: max\_accuracy value: 89.8474793340319 - type: max\_ap value: 87.47814292587448 - type: max\_f1 value: 80.15461150280949 --- Model Summary ============= > > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. > > > * Repository: ContextualAI/gritlm * Paper: URL * Logs: URL * Script: URL Use === The model usage is documented here.

peft

<!-- 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. --> # GUE_virus_covid-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.2499 - F1 Score: 0.5474 - Accuracy: 0.5351 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1844 | 0.35 | 200 | 2.1790 | 0.0846 | 0.1394 | | 2.1709 | 0.7 | 400 | 2.1527 | 0.1291 | 0.1645 | | 2.1059 | 1.05 | 600 | 2.0039 | 0.2082 | 0.2292 | | 1.9838 | 1.4 | 800 | 1.8829 | 0.2394 | 0.2690 | | 1.8962 | 1.75 | 1000 | 1.8143 | 0.2801 | 0.2901 | | 1.8521 | 2.09 | 1200 | 1.7949 | 0.2914 | 0.3109 | | 1.8118 | 2.44 | 1400 | 1.7158 | 0.3344 | 0.3469 | | 1.7736 | 2.79 | 1600 | 1.6691 | 0.3469 | 0.3676 | | 1.7313 | 3.14 | 1800 | 1.6238 | 0.3670 | 0.3842 | | 1.6996 | 3.49 | 2000 | 1.6052 | 0.3787 | 0.3851 | | 1.6784 | 3.84 | 2200 | 1.5802 | 0.3885 | 0.3936 | | 1.6454 | 4.19 | 2400 | 1.5568 | 0.3987 | 0.3977 | | 1.6235 | 4.54 | 2600 | 1.5292 | 0.4096 | 0.4159 | | 1.6131 | 4.89 | 2800 | 1.5245 | 0.4141 | 0.4209 | | 1.5953 | 5.24 | 3000 | 1.4982 | 0.4287 | 0.4345 | | 1.5705 | 5.58 | 3200 | 1.4806 | 0.4525 | 0.4462 | | 1.5505 | 5.93 | 3400 | 1.4619 | 0.4395 | 0.4443 | | 1.5402 | 6.28 | 3600 | 1.4492 | 0.4668 | 0.4506 | | 1.5208 | 6.63 | 3800 | 1.4306 | 0.4571 | 0.4609 | | 1.5061 | 6.98 | 4000 | 1.4279 | 0.4644 | 0.4623 | | 1.4925 | 7.33 | 4200 | 1.4147 | 0.4805 | 0.4701 | | 1.4772 | 7.68 | 4400 | 1.4055 | 0.4787 | 0.4696 | | 1.4782 | 8.03 | 4600 | 1.3983 | 0.4738 | 0.4700 | | 1.4524 | 8.38 | 4800 | 1.3893 | 0.4867 | 0.4829 | | 1.4546 | 8.73 | 5000 | 1.3800 | 0.4816 | 0.4738 | | 1.4394 | 9.08 | 5200 | 1.3782 | 0.4942 | 0.4775 | | 1.4326 | 9.42 | 5400 | 1.3631 | 0.4857 | 0.4853 | | 1.4264 | 9.77 | 5600 | 1.3457 | 0.4992 | 0.4932 | | 1.4145 | 10.12 | 5800 | 1.3439 | 0.5071 | 0.4976 | | 1.4115 | 10.47 | 6000 | 1.3366 | 0.5073 | 0.4972 | | 1.3942 | 10.82 | 6200 | 1.3286 | 0.5113 | 0.4964 | | 1.3797 | 11.17 | 6400 | 1.3205 | 0.5109 | 0.5029 | | 1.3778 | 11.52 | 6600 | 1.3173 | 0.5186 | 0.5041 | | 1.3805 | 11.87 | 6800 | 1.3090 | 0.5161 | 0.5040 | | 1.3645 | 12.22 | 7000 | 1.3017 | 0.5267 | 0.5171 | | 1.3628 | 12.57 | 7200 | 1.3015 | 0.5149 | 0.5061 | | 1.3597 | 12.91 | 7400 | 1.2982 | 0.5236 | 0.5075 | | 1.3554 | 13.26 | 7600 | 1.2894 | 0.5229 | 0.5130 | | 1.3392 | 13.61 | 7800 | 1.2850 | 0.5326 | 0.5183 | | 1.3441 | 13.96 | 8000 | 1.2806 | 0.5313 | 0.5182 | | 1.3317 | 14.31 | 8200 | 1.2782 | 0.5332 | 0.5193 | | 1.3369 | 14.66 | 8400 | 1.2731 | 0.5326 | 0.5220 | | 1.3337 | 15.01 | 8600 | 1.2732 | 0.5297 | 0.5226 | | 1.3336 | 15.36 | 8800 | 1.2696 | 0.5409 | 0.5279 | | 1.3161 | 15.71 | 9000 | 1.2714 | 0.5357 | 0.5248 | | 1.3329 | 16.06 | 9200 | 1.2696 | 0.5347 | 0.5242 | | 1.3261 | 16.4 | 9400 | 1.2665 | 0.5363 | 0.5287 | | 1.3228 | 16.75 | 9600 | 1.2668 | 0.5374 | 0.5252 | | 1.3258 | 17.1 | 9800 | 1.2662 | 0.5395 | 0.5280 | | 1.3289 | 17.45 | 10000 | 1.2655 | 0.5392 | 0.5277 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_virus_covid-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_65536_512_47M-L32_f

2024-05-03T17:10:57+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_virus\_covid-seqsight\_65536\_512\_47M-L32\_f ================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.2499 * F1 Score: 0.5474 * Accuracy: 0.5351 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

token-classification

spacy

| Feature | Description | | --- | --- | | **Name** | `en_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.7.4,<3.8.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (11 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `College Name`, `Companies worked at`, `Degree`, `Designation`, `Email Address`, `Graduation Year`, `Location`, `Name`, `Skills`, `UNKNOWN`, `Years of Experience` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 57.19 | | `ENTS_P` | 60.75 | | `ENTS_R` | 54.02 | | `TRANSFORMER_LOSS` | 480458.92 | | `NER_LOSS` | 1538225.13 |

{"language": ["en"], "tags": ["spacy", "token-classification"]}

prof144/en_pipeline

2024-05-03T17:10:59+00:00

TAGS #spacy #token-classification #en #model-index #region-us

### Label Scheme View label scheme (11 labels for 1 components) ### Accuracy

peft

<!-- 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. --> # GUE_virus_covid-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.4679 - F1 Score: 0.4607 - Accuracy: 0.4564 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1848 | 0.35 | 200 | 2.1827 | 0.0810 | 0.1389 | | 2.1771 | 0.7 | 400 | 2.1699 | 0.1086 | 0.1417 | | 2.1622 | 1.05 | 600 | 2.1455 | 0.1413 | 0.1689 | | 2.1255 | 1.4 | 800 | 2.0468 | 0.1962 | 0.2244 | | 2.0333 | 1.75 | 1000 | 1.9384 | 0.2227 | 0.2599 | | 1.9701 | 2.09 | 1200 | 1.8946 | 0.2381 | 0.2709 | | 1.9252 | 2.44 | 1400 | 1.8326 | 0.2954 | 0.3059 | | 1.8922 | 2.79 | 1600 | 1.7987 | 0.2895 | 0.3096 | | 1.8635 | 3.14 | 1800 | 1.7673 | 0.2891 | 0.3158 | | 1.8315 | 3.49 | 2000 | 1.7372 | 0.3193 | 0.3337 | | 1.8216 | 3.84 | 2200 | 1.7192 | 0.3306 | 0.3421 | | 1.7967 | 4.19 | 2400 | 1.6913 | 0.3757 | 0.3702 | | 1.7827 | 4.54 | 2600 | 1.6755 | 0.3676 | 0.3740 | | 1.7731 | 4.89 | 2800 | 1.6627 | 0.3750 | 0.3800 | | 1.7636 | 5.24 | 3000 | 1.6477 | 0.3810 | 0.3869 | | 1.7467 | 5.58 | 3200 | 1.6318 | 0.3942 | 0.3981 | | 1.7298 | 5.93 | 3400 | 1.6335 | 0.3720 | 0.3806 | | 1.7237 | 6.28 | 3600 | 1.6197 | 0.3891 | 0.3901 | | 1.7099 | 6.63 | 3800 | 1.5950 | 0.4002 | 0.4086 | | 1.6962 | 6.98 | 4000 | 1.5889 | 0.4084 | 0.4094 | | 1.6871 | 7.33 | 4200 | 1.5824 | 0.4067 | 0.4116 | | 1.6794 | 7.68 | 4400 | 1.5680 | 0.4223 | 0.4211 | | 1.6816 | 8.03 | 4600 | 1.5706 | 0.4142 | 0.4126 | | 1.6575 | 8.38 | 4800 | 1.5548 | 0.4110 | 0.4181 | | 1.6688 | 8.73 | 5000 | 1.5507 | 0.4238 | 0.4271 | | 1.6537 | 9.08 | 5200 | 1.5434 | 0.4284 | 0.4202 | | 1.6549 | 9.42 | 5400 | 1.5424 | 0.4228 | 0.4244 | | 1.6383 | 9.77 | 5600 | 1.5232 | 0.4264 | 0.4319 | | 1.6347 | 10.12 | 5800 | 1.5260 | 0.4333 | 0.4294 | | 1.6299 | 10.47 | 6000 | 1.5217 | 0.4366 | 0.4297 | | 1.6276 | 10.82 | 6200 | 1.5146 | 0.4402 | 0.4307 | | 1.6149 | 11.17 | 6400 | 1.5198 | 0.4366 | 0.4309 | | 1.6118 | 11.52 | 6600 | 1.5046 | 0.4404 | 0.4319 | | 1.6157 | 11.87 | 6800 | 1.5022 | 0.4437 | 0.4384 | | 1.6018 | 12.22 | 7000 | 1.4951 | 0.4450 | 0.4370 | | 1.5977 | 12.57 | 7200 | 1.4887 | 0.4440 | 0.4403 | | 1.5986 | 12.91 | 7400 | 1.4909 | 0.4491 | 0.4399 | | 1.5961 | 13.26 | 7600 | 1.4830 | 0.4442 | 0.4374 | | 1.5912 | 13.61 | 7800 | 1.4843 | 0.4468 | 0.4355 | | 1.585 | 13.96 | 8000 | 1.4802 | 0.4520 | 0.4471 | | 1.5771 | 14.31 | 8200 | 1.4751 | 0.4550 | 0.4488 | | 1.584 | 14.66 | 8400 | 1.4684 | 0.4564 | 0.4475 | | 1.5823 | 15.01 | 8600 | 1.4734 | 0.4526 | 0.4475 | | 1.593 | 15.36 | 8800 | 1.4694 | 0.4581 | 0.4493 | | 1.5742 | 15.71 | 9000 | 1.4690 | 0.4541 | 0.4465 | | 1.5807 | 16.06 | 9200 | 1.4676 | 0.4575 | 0.4491 | | 1.5771 | 16.4 | 9400 | 1.4680 | 0.4541 | 0.4472 | | 1.5728 | 16.75 | 9600 | 1.4663 | 0.4590 | 0.4504 | | 1.5805 | 17.1 | 9800 | 1.4656 | 0.4607 | 0.4529 | | 1.5809 | 17.45 | 10000 | 1.4646 | 0.4602 | 0.4528 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_virus_covid-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_65536_512_47M-L8_f

2024-05-03T17:11:02+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_65536_512_47M #region-us

GUE\_virus\_covid-seqsight\_65536\_512\_47M-L8\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_65536\_512\_47M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.4679 * F1 Score: 0.4607 * Accuracy: 0.4564 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_300_tata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4633 - F1 Score: 0.8019 - Accuracy: 0.8026 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6143 | 5.13 | 200 | 0.5429 | 0.7210 | 0.7308 | | 0.4939 | 10.26 | 400 | 0.4784 | 0.7652 | 0.7651 | | 0.4597 | 15.38 | 600 | 0.4630 | 0.7815 | 0.7814 | | 0.4424 | 20.51 | 800 | 0.4467 | 0.7942 | 0.7945 | | 0.429 | 25.64 | 1000 | 0.4458 | 0.7912 | 0.7912 | | 0.4193 | 30.77 | 1200 | 0.4435 | 0.8027 | 0.8026 | | 0.4116 | 35.9 | 1400 | 0.4424 | 0.7992 | 0.7993 | | 0.4052 | 41.03 | 1600 | 0.4467 | 0.7970 | 0.7977 | | 0.4001 | 46.15 | 1800 | 0.4446 | 0.7945 | 0.7945 | | 0.3919 | 51.28 | 2000 | 0.4406 | 0.8009 | 0.8010 | | 0.3874 | 56.41 | 2200 | 0.4495 | 0.8066 | 0.8075 | | 0.3804 | 61.54 | 2400 | 0.4465 | 0.8024 | 0.8026 | | 0.3733 | 66.67 | 2600 | 0.4572 | 0.8039 | 0.8042 | | 0.3732 | 71.79 | 2800 | 0.4552 | 0.8054 | 0.8059 | | 0.3721 | 76.92 | 3000 | 0.4549 | 0.7799 | 0.7798 | | 0.3669 | 82.05 | 3200 | 0.4633 | 0.7893 | 0.7896 | | 0.3633 | 87.18 | 3400 | 0.4594 | 0.7881 | 0.7879 | | 0.3587 | 92.31 | 3600 | 0.4601 | 0.7993 | 0.7993 | | 0.3569 | 97.44 | 3800 | 0.4608 | 0.7961 | 0.7961 | | 0.3474 | 102.56 | 4000 | 0.4729 | 0.7912 | 0.7912 | | 0.3523 | 107.69 | 4200 | 0.4651 | 0.7929 | 0.7928 | | 0.3502 | 112.82 | 4400 | 0.4641 | 0.7896 | 0.7896 | | 0.3427 | 117.95 | 4600 | 0.4727 | 0.7896 | 0.7896 | | 0.3428 | 123.08 | 4800 | 0.4731 | 0.7946 | 0.7945 | | 0.3407 | 128.21 | 5000 | 0.4764 | 0.7927 | 0.7928 | | 0.3418 | 133.33 | 5200 | 0.4797 | 0.7893 | 0.7896 | | 0.3346 | 138.46 | 5400 | 0.4938 | 0.7925 | 0.7928 | | 0.3348 | 143.59 | 5600 | 0.4862 | 0.7957 | 0.7961 | | 0.3364 | 148.72 | 5800 | 0.4881 | 0.7908 | 0.7912 | | 0.3329 | 153.85 | 6000 | 0.4877 | 0.7860 | 0.7863 | | 0.3306 | 158.97 | 6200 | 0.4849 | 0.7878 | 0.7879 | | 0.3292 | 164.1 | 6400 | 0.4915 | 0.7939 | 0.7945 | | 0.3262 | 169.23 | 6600 | 0.4810 | 0.7863 | 0.7863 | | 0.3294 | 174.36 | 6800 | 0.4848 | 0.7911 | 0.7912 | | 0.3258 | 179.49 | 7000 | 0.4976 | 0.7908 | 0.7912 | | 0.3258 | 184.62 | 7200 | 0.5007 | 0.7986 | 0.7993 | | 0.3236 | 189.74 | 7400 | 0.4985 | 0.7878 | 0.7879 | | 0.3199 | 194.87 | 7600 | 0.5001 | 0.7878 | 0.7879 | | 0.3197 | 200.0 | 7800 | 0.5024 | 0.7876 | 0.7879 | | 0.3227 | 205.13 | 8000 | 0.4944 | 0.7877 | 0.7879 | | 0.3174 | 210.26 | 8200 | 0.4960 | 0.7863 | 0.7863 | | 0.3199 | 215.38 | 8400 | 0.4989 | 0.7862 | 0.7863 | | 0.3156 | 220.51 | 8600 | 0.5035 | 0.7893 | 0.7896 | | 0.3171 | 225.64 | 8800 | 0.5018 | 0.7879 | 0.7879 | | 0.3179 | 230.77 | 9000 | 0.5001 | 0.7895 | 0.7896 | | 0.3152 | 235.9 | 9200 | 0.4989 | 0.7895 | 0.7896 | | 0.3189 | 241.03 | 9400 | 0.5018 | 0.7911 | 0.7912 | | 0.3144 | 246.15 | 9600 | 0.5024 | 0.7895 | 0.7896 | | 0.3203 | 251.28 | 9800 | 0.5003 | 0.7895 | 0.7896 | | 0.3167 | 256.41 | 10000 | 0.5005 | 0.7895 | 0.7896 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_4096_512_15M-L1_f

2024-05-03T17:11:27+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_tata-seqsight\_4096\_512\_15M-L1\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4633 * F1 Score: 0.8019 * Accuracy: 0.8026 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_300_tata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4525 - F1 Score: 0.8104 - Accuracy: 0.8108 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5575 | 5.13 | 200 | 0.4922 | 0.7656 | 0.7700 | | 0.4488 | 10.26 | 400 | 0.4478 | 0.7979 | 0.7977 | | 0.4178 | 15.38 | 600 | 0.4400 | 0.8073 | 0.8075 | | 0.399 | 20.51 | 800 | 0.4380 | 0.7994 | 0.7993 | | 0.3815 | 25.64 | 1000 | 0.4497 | 0.8044 | 0.8042 | | 0.3664 | 30.77 | 1200 | 0.4466 | 0.8008 | 0.8010 | | 0.3513 | 35.9 | 1400 | 0.4605 | 0.8009 | 0.8010 | | 0.3398 | 41.03 | 1600 | 0.4883 | 0.8029 | 0.8042 | | 0.3316 | 46.15 | 1800 | 0.4697 | 0.7992 | 0.7993 | | 0.3172 | 51.28 | 2000 | 0.4807 | 0.7990 | 0.7993 | | 0.3078 | 56.41 | 2200 | 0.4928 | 0.8010 | 0.8010 | | 0.2977 | 61.54 | 2400 | 0.4936 | 0.8027 | 0.8026 | | 0.2837 | 66.67 | 2600 | 0.5377 | 0.7967 | 0.7977 | | 0.28 | 71.79 | 2800 | 0.5258 | 0.7924 | 0.7928 | | 0.2724 | 76.92 | 3000 | 0.5418 | 0.7943 | 0.7945 | | 0.2668 | 82.05 | 3200 | 0.5509 | 0.7865 | 0.7879 | | 0.256 | 87.18 | 3400 | 0.5541 | 0.8010 | 0.8010 | | 0.2487 | 92.31 | 3600 | 0.5716 | 0.7987 | 0.7993 | | 0.2461 | 97.44 | 3800 | 0.5703 | 0.7832 | 0.7847 | | 0.2357 | 102.56 | 4000 | 0.5745 | 0.7926 | 0.7928 | | 0.2345 | 107.69 | 4200 | 0.5881 | 0.7893 | 0.7896 | | 0.2332 | 112.82 | 4400 | 0.5964 | 0.7787 | 0.7798 | | 0.2222 | 117.95 | 4600 | 0.6121 | 0.7961 | 0.7961 | | 0.2141 | 123.08 | 4800 | 0.6155 | 0.7897 | 0.7896 | | 0.2133 | 128.21 | 5000 | 0.6218 | 0.7945 | 0.7945 | | 0.2121 | 133.33 | 5200 | 0.6485 | 0.7872 | 0.7879 | | 0.2051 | 138.46 | 5400 | 0.6307 | 0.7910 | 0.7912 | | 0.1996 | 143.59 | 5600 | 0.6425 | 0.7929 | 0.7928 | | 0.1976 | 148.72 | 5800 | 0.6696 | 0.7994 | 0.7993 | | 0.1967 | 153.85 | 6000 | 0.6575 | 0.7873 | 0.7879 | | 0.1901 | 158.97 | 6200 | 0.6697 | 0.7816 | 0.7814 | | 0.1896 | 164.1 | 6400 | 0.6617 | 0.7943 | 0.7945 | | 0.1824 | 169.23 | 6600 | 0.6753 | 0.7977 | 0.7977 | | 0.1858 | 174.36 | 6800 | 0.6642 | 0.7959 | 0.7961 | | 0.1762 | 179.49 | 7000 | 0.6973 | 0.7942 | 0.7945 | | 0.1769 | 184.62 | 7200 | 0.7137 | 0.7921 | 0.7928 | | 0.1769 | 189.74 | 7400 | 0.7157 | 0.7911 | 0.7912 | | 0.1709 | 194.87 | 7600 | 0.7214 | 0.7878 | 0.7879 | | 0.1749 | 200.0 | 7800 | 0.7159 | 0.7894 | 0.7896 | | 0.1717 | 205.13 | 8000 | 0.7236 | 0.7863 | 0.7863 | | 0.1698 | 210.26 | 8200 | 0.7168 | 0.7911 | 0.7912 | | 0.1669 | 215.38 | 8400 | 0.7280 | 0.7862 | 0.7863 | | 0.1685 | 220.51 | 8600 | 0.7279 | 0.7843 | 0.7847 | | 0.1626 | 225.64 | 8800 | 0.7365 | 0.7895 | 0.7896 | | 0.1678 | 230.77 | 9000 | 0.7328 | 0.7895 | 0.7896 | | 0.1628 | 235.9 | 9200 | 0.7431 | 0.7912 | 0.7912 | | 0.1676 | 241.03 | 9400 | 0.7286 | 0.7877 | 0.7879 | | 0.1602 | 246.15 | 9600 | 0.7438 | 0.7844 | 0.7847 | | 0.1668 | 251.28 | 9800 | 0.7388 | 0.7894 | 0.7896 | | 0.1609 | 256.41 | 10000 | 0.7400 | 0.7894 | 0.7896 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_4096_512_15M-L8_f

2024-05-03T17:11:52+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_tata-seqsight\_4096\_512\_15M-L8\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4525 * F1 Score: 0.8104 * Accuracy: 0.8108 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_300_tata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4529 - F1 Score: 0.8189 - Accuracy: 0.8189 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.525 | 5.13 | 200 | 0.4598 | 0.7888 | 0.7912 | | 0.4258 | 10.26 | 400 | 0.4665 | 0.7872 | 0.7879 | | 0.3858 | 15.38 | 600 | 0.4548 | 0.7944 | 0.7945 | | 0.3567 | 20.51 | 800 | 0.4949 | 0.7881 | 0.7896 | | 0.3289 | 25.64 | 1000 | 0.4710 | 0.7994 | 0.7993 | | 0.3034 | 30.77 | 1200 | 0.4920 | 0.7770 | 0.7781 | | 0.2771 | 35.9 | 1400 | 0.5520 | 0.7913 | 0.7912 | | 0.2585 | 41.03 | 1600 | 0.5391 | 0.7790 | 0.7798 | | 0.2376 | 46.15 | 1800 | 0.5667 | 0.7839 | 0.7847 | | 0.2163 | 51.28 | 2000 | 0.6376 | 0.7881 | 0.7879 | | 0.2005 | 56.41 | 2200 | 0.6994 | 0.7927 | 0.7928 | | 0.1804 | 61.54 | 2400 | 0.7399 | 0.7848 | 0.7847 | | 0.1633 | 66.67 | 2600 | 0.8005 | 0.7694 | 0.7700 | | 0.1578 | 71.79 | 2800 | 0.8019 | 0.7723 | 0.7732 | | 0.1423 | 76.92 | 3000 | 0.8350 | 0.7480 | 0.7488 | | 0.1326 | 82.05 | 3200 | 0.7942 | 0.7535 | 0.7537 | | 0.1223 | 87.18 | 3400 | 0.9037 | 0.7633 | 0.7635 | | 0.1147 | 92.31 | 3600 | 0.9318 | 0.7583 | 0.7586 | | 0.1092 | 97.44 | 3800 | 0.9013 | 0.7675 | 0.7684 | | 0.1027 | 102.56 | 4000 | 0.9575 | 0.7649 | 0.7651 | | 0.0978 | 107.69 | 4200 | 0.9630 | 0.7778 | 0.7781 | | 0.0934 | 112.82 | 4400 | 0.9373 | 0.7747 | 0.7749 | | 0.0825 | 117.95 | 4600 | 1.0492 | 0.7668 | 0.7667 | | 0.083 | 123.08 | 4800 | 1.1142 | 0.7633 | 0.7635 | | 0.0819 | 128.21 | 5000 | 1.0054 | 0.7750 | 0.7749 | | 0.0807 | 133.33 | 5200 | 1.0625 | 0.7741 | 0.7749 | | 0.0732 | 138.46 | 5400 | 1.0712 | 0.7658 | 0.7667 | | 0.0694 | 143.59 | 5600 | 1.0530 | 0.7679 | 0.7684 | | 0.0686 | 148.72 | 5800 | 1.0695 | 0.7726 | 0.7732 | | 0.0683 | 153.85 | 6000 | 1.0801 | 0.7783 | 0.7781 | | 0.0603 | 158.97 | 6200 | 1.1614 | 0.7749 | 0.7749 | | 0.0629 | 164.1 | 6400 | 1.0910 | 0.7748 | 0.7749 | | 0.0597 | 169.23 | 6600 | 1.0800 | 0.7764 | 0.7765 | | 0.0612 | 174.36 | 6800 | 1.1113 | 0.7620 | 0.7618 | | 0.0553 | 179.49 | 7000 | 1.1382 | 0.7781 | 0.7781 | | 0.0561 | 184.62 | 7200 | 1.1173 | 0.7763 | 0.7765 | | 0.0553 | 189.74 | 7400 | 1.1237 | 0.7701 | 0.7700 | | 0.0503 | 194.87 | 7600 | 1.1780 | 0.7799 | 0.7798 | | 0.05 | 200.0 | 7800 | 1.2119 | 0.7668 | 0.7667 | | 0.0483 | 205.13 | 8000 | 1.2256 | 0.7733 | 0.7732 | | 0.0485 | 210.26 | 8200 | 1.2152 | 0.7797 | 0.7798 | | 0.0508 | 215.38 | 8400 | 1.1864 | 0.7779 | 0.7781 | | 0.0476 | 220.51 | 8600 | 1.2031 | 0.7857 | 0.7863 | | 0.0453 | 225.64 | 8800 | 1.2366 | 0.7830 | 0.7830 | | 0.0451 | 230.77 | 9000 | 1.2441 | 0.7782 | 0.7781 | | 0.0443 | 235.9 | 9200 | 1.2473 | 0.7812 | 0.7814 | | 0.0473 | 241.03 | 9400 | 1.2117 | 0.7764 | 0.7765 | | 0.0442 | 246.15 | 9600 | 1.2430 | 0.7846 | 0.7847 | | 0.0428 | 251.28 | 9800 | 1.2550 | 0.7829 | 0.7830 | | 0.0434 | 256.41 | 10000 | 1.2525 | 0.7829 | 0.7830 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_4096_512_15M-L32_f

2024-05-03T17:12:26+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_tata-seqsight\_4096\_512\_15M-L32\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4529 * F1 Score: 0.8189 * Accuracy: 0.8189 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

# Uploaded model - **Developed by:** jirawan-chro - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

jirawan-chro/lora_model

2024-05-03T17:14:19+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: jirawan-chro - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

peft

<!-- 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. --> # GUE_prom_prom_300_notata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1268 - F1 Score: 0.9512 - Accuracy: 0.9512 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3546 | 0.6 | 200 | 0.1798 | 0.9257 | 0.9258 | | 0.1903 | 1.2 | 400 | 0.1564 | 0.9370 | 0.9371 | | 0.1734 | 1.81 | 600 | 0.1403 | 0.9463 | 0.9463 | | 0.1569 | 2.41 | 800 | 0.1374 | 0.9468 | 0.9469 | | 0.1515 | 3.01 | 1000 | 0.1296 | 0.9510 | 0.9510 | | 0.1465 | 3.61 | 1200 | 0.1263 | 0.9521 | 0.9521 | | 0.1441 | 4.22 | 1400 | 0.1236 | 0.9529 | 0.9529 | | 0.1372 | 4.82 | 1600 | 0.1209 | 0.9538 | 0.9538 | | 0.1365 | 5.42 | 1800 | 0.1239 | 0.9504 | 0.9504 | | 0.1312 | 6.02 | 2000 | 0.1234 | 0.9520 | 0.9520 | | 0.1302 | 6.63 | 2200 | 0.1171 | 0.9550 | 0.9550 | | 0.1309 | 7.23 | 2400 | 0.1162 | 0.9546 | 0.9546 | | 0.1263 | 7.83 | 2600 | 0.1171 | 0.9533 | 0.9533 | | 0.1285 | 8.43 | 2800 | 0.1189 | 0.9536 | 0.9536 | | 0.1298 | 9.04 | 3000 | 0.1164 | 0.9563 | 0.9563 | | 0.126 | 9.64 | 3200 | 0.1199 | 0.9533 | 0.9533 | | 0.1268 | 10.24 | 3400 | 0.1154 | 0.9585 | 0.9585 | | 0.1243 | 10.84 | 3600 | 0.1142 | 0.9566 | 0.9567 | | 0.1214 | 11.45 | 3800 | 0.1139 | 0.9555 | 0.9555 | | 0.1228 | 12.05 | 4000 | 0.1134 | 0.9576 | 0.9576 | | 0.1223 | 12.65 | 4200 | 0.1137 | 0.9557 | 0.9557 | | 0.1237 | 13.25 | 4400 | 0.1122 | 0.9557 | 0.9557 | | 0.1207 | 13.86 | 4600 | 0.1118 | 0.9563 | 0.9563 | | 0.1225 | 14.46 | 4800 | 0.1122 | 0.9572 | 0.9572 | | 0.1182 | 15.06 | 5000 | 0.1109 | 0.9580 | 0.9580 | | 0.1191 | 15.66 | 5200 | 0.1114 | 0.9565 | 0.9565 | | 0.1218 | 16.27 | 5400 | 0.1102 | 0.9580 | 0.9580 | | 0.1179 | 16.87 | 5600 | 0.1105 | 0.9561 | 0.9561 | | 0.1165 | 17.47 | 5800 | 0.1104 | 0.9563 | 0.9563 | | 0.1219 | 18.07 | 6000 | 0.1094 | 0.9580 | 0.9580 | | 0.1189 | 18.67 | 6200 | 0.1086 | 0.9583 | 0.9584 | | 0.1187 | 19.28 | 6400 | 0.1089 | 0.9576 | 0.9576 | | 0.1128 | 19.88 | 6600 | 0.1102 | 0.9583 | 0.9584 | | 0.1209 | 20.48 | 6800 | 0.1097 | 0.9580 | 0.9580 | | 0.115 | 21.08 | 7000 | 0.1088 | 0.9576 | 0.9576 | | 0.1127 | 21.69 | 7200 | 0.1103 | 0.9565 | 0.9565 | | 0.1147 | 22.29 | 7400 | 0.1116 | 0.9567 | 0.9567 | | 0.1183 | 22.89 | 7600 | 0.1086 | 0.9567 | 0.9567 | | 0.1137 | 23.49 | 7800 | 0.1083 | 0.9576 | 0.9576 | | 0.1158 | 24.1 | 8000 | 0.1084 | 0.9593 | 0.9593 | | 0.1133 | 24.7 | 8200 | 0.1080 | 0.9584 | 0.9584 | | 0.1132 | 25.3 | 8400 | 0.1082 | 0.9580 | 0.9580 | | 0.1129 | 25.9 | 8600 | 0.1081 | 0.9574 | 0.9574 | | 0.1149 | 26.51 | 8800 | 0.1079 | 0.9572 | 0.9572 | | 0.1137 | 27.11 | 9000 | 0.1075 | 0.9578 | 0.9578 | | 0.1135 | 27.71 | 9200 | 0.1078 | 0.9593 | 0.9593 | | 0.1092 | 28.31 | 9400 | 0.1081 | 0.9589 | 0.9589 | | 0.1183 | 28.92 | 9600 | 0.1074 | 0.9576 | 0.9576 | | 0.11 | 29.52 | 9800 | 0.1076 | 0.9578 | 0.9578 | | 0.1162 | 30.12 | 10000 | 0.1075 | 0.9582 | 0.9582 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_15M-L1_f

2024-05-03T17:15:04+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_notata-seqsight\_4096\_512\_15M-L1\_f =========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.1268 * F1 Score: 0.9512 * Accuracy: 0.9512 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_core_all-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4321 - F1 Score: 0.7993 - Accuracy: 0.7993 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6099 | 0.54 | 200 | 0.5123 | 0.7516 | 0.7517 | | 0.5027 | 1.08 | 400 | 0.4814 | 0.7731 | 0.7735 | | 0.4759 | 1.62 | 600 | 0.4652 | 0.7825 | 0.7826 | | 0.466 | 2.16 | 800 | 0.4640 | 0.7823 | 0.7823 | | 0.4622 | 2.7 | 1000 | 0.4578 | 0.7863 | 0.7863 | | 0.459 | 3.24 | 1200 | 0.4556 | 0.7879 | 0.7880 | | 0.4549 | 3.78 | 1400 | 0.4535 | 0.7881 | 0.7882 | | 0.452 | 4.32 | 1600 | 0.4580 | 0.7896 | 0.7897 | | 0.4519 | 4.86 | 1800 | 0.4566 | 0.7884 | 0.7887 | | 0.4485 | 5.41 | 2000 | 0.4530 | 0.7923 | 0.7924 | | 0.444 | 5.95 | 2200 | 0.4512 | 0.7893 | 0.7894 | | 0.4465 | 6.49 | 2400 | 0.4478 | 0.7910 | 0.7910 | | 0.4425 | 7.03 | 2600 | 0.4493 | 0.7905 | 0.7909 | | 0.4443 | 7.57 | 2800 | 0.4481 | 0.7972 | 0.7973 | | 0.4371 | 8.11 | 3000 | 0.4472 | 0.7958 | 0.7959 | | 0.4398 | 8.65 | 3200 | 0.4437 | 0.7951 | 0.7951 | | 0.4411 | 9.19 | 3400 | 0.4442 | 0.7939 | 0.7939 | | 0.4368 | 9.73 | 3600 | 0.4501 | 0.7915 | 0.7919 | | 0.4404 | 10.27 | 3800 | 0.4432 | 0.7947 | 0.7948 | | 0.4346 | 10.81 | 4000 | 0.4449 | 0.7969 | 0.7970 | | 0.436 | 11.35 | 4200 | 0.4438 | 0.7951 | 0.7953 | | 0.435 | 11.89 | 4400 | 0.4437 | 0.7951 | 0.7953 | | 0.4315 | 12.43 | 4600 | 0.4426 | 0.7954 | 0.7954 | | 0.4327 | 12.97 | 4800 | 0.4431 | 0.7972 | 0.7973 | | 0.4332 | 13.51 | 5000 | 0.4484 | 0.7893 | 0.7900 | | 0.4322 | 14.05 | 5200 | 0.4408 | 0.7967 | 0.7968 | | 0.4306 | 14.59 | 5400 | 0.4414 | 0.7986 | 0.7986 | | 0.4301 | 15.14 | 5600 | 0.4410 | 0.7986 | 0.7986 | | 0.4322 | 15.68 | 5800 | 0.4412 | 0.7955 | 0.7956 | | 0.4238 | 16.22 | 6000 | 0.4422 | 0.7962 | 0.7963 | | 0.4305 | 16.76 | 6200 | 0.4392 | 0.7962 | 0.7963 | | 0.4333 | 17.3 | 6400 | 0.4398 | 0.7960 | 0.7961 | | 0.4277 | 17.84 | 6600 | 0.4423 | 0.7937 | 0.7939 | | 0.4271 | 18.38 | 6800 | 0.4429 | 0.7940 | 0.7943 | | 0.4266 | 18.92 | 7000 | 0.4394 | 0.7950 | 0.7951 | | 0.4217 | 19.46 | 7200 | 0.4408 | 0.7952 | 0.7953 | | 0.4336 | 20.0 | 7400 | 0.4388 | 0.7985 | 0.7985 | | 0.4299 | 20.54 | 7600 | 0.4405 | 0.7940 | 0.7941 | | 0.4257 | 21.08 | 7800 | 0.4399 | 0.7946 | 0.7948 | | 0.4269 | 21.62 | 8000 | 0.4372 | 0.7964 | 0.7965 | | 0.4254 | 22.16 | 8200 | 0.4375 | 0.7976 | 0.7976 | | 0.4316 | 22.7 | 8400 | 0.4386 | 0.7939 | 0.7941 | | 0.4249 | 23.24 | 8600 | 0.4363 | 0.7980 | 0.7980 | | 0.4243 | 23.78 | 8800 | 0.4377 | 0.7971 | 0.7971 | | 0.42 | 24.32 | 9000 | 0.4383 | 0.7985 | 0.7985 | | 0.426 | 24.86 | 9200 | 0.4372 | 0.7973 | 0.7973 | | 0.4327 | 25.41 | 9400 | 0.4373 | 0.7966 | 0.7966 | | 0.4198 | 25.95 | 9600 | 0.4382 | 0.7973 | 0.7973 | | 0.4274 | 26.49 | 9800 | 0.4382 | 0.7957 | 0.7958 | | 0.4227 | 27.03 | 10000 | 0.4381 | 0.7966 | 0.7966 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_15M-L1_f

2024-05-03T17:15:44+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_15M-L1\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.4321 * F1 Score: 0.7993 * Accuracy: 0.7993 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_core_all-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4073 - F1 Score: 0.8143 - Accuracy: 0.8144 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.532 | 0.54 | 200 | 0.4612 | 0.7853 | 0.7853 | | 0.458 | 1.08 | 400 | 0.4718 | 0.7885 | 0.7894 | | 0.4427 | 1.62 | 600 | 0.4458 | 0.7929 | 0.7929 | | 0.4349 | 2.16 | 800 | 0.4436 | 0.7970 | 0.7971 | | 0.4323 | 2.7 | 1000 | 0.4393 | 0.7956 | 0.7958 | | 0.4271 | 3.24 | 1200 | 0.4347 | 0.7961 | 0.7965 | | 0.4219 | 3.78 | 1400 | 0.4377 | 0.7934 | 0.7939 | | 0.4204 | 4.32 | 1600 | 0.4353 | 0.8010 | 0.8010 | | 0.4198 | 4.86 | 1800 | 0.4322 | 0.7973 | 0.7976 | | 0.4127 | 5.41 | 2000 | 0.4312 | 0.8001 | 0.8003 | | 0.4133 | 5.95 | 2200 | 0.4320 | 0.8046 | 0.8046 | | 0.4152 | 6.49 | 2400 | 0.4262 | 0.8016 | 0.8017 | | 0.4079 | 7.03 | 2600 | 0.4236 | 0.8015 | 0.8015 | | 0.4079 | 7.57 | 2800 | 0.4268 | 0.8023 | 0.8024 | | 0.404 | 8.11 | 3000 | 0.4295 | 0.8001 | 0.8003 | | 0.404 | 8.65 | 3200 | 0.4209 | 0.8054 | 0.8056 | | 0.4043 | 9.19 | 3400 | 0.4243 | 0.8071 | 0.8071 | | 0.4024 | 9.73 | 3600 | 0.4302 | 0.8033 | 0.8037 | | 0.4022 | 10.27 | 3800 | 0.4269 | 0.8034 | 0.8037 | | 0.4006 | 10.81 | 4000 | 0.4304 | 0.8042 | 0.8042 | | 0.3963 | 11.35 | 4200 | 0.4246 | 0.8036 | 0.8039 | | 0.3959 | 11.89 | 4400 | 0.4254 | 0.8037 | 0.8041 | | 0.3943 | 12.43 | 4600 | 0.4254 | 0.8029 | 0.8029 | | 0.3912 | 12.97 | 4800 | 0.4262 | 0.8036 | 0.8037 | | 0.3924 | 13.51 | 5000 | 0.4351 | 0.7990 | 0.8 | | 0.3908 | 14.05 | 5200 | 0.4232 | 0.8079 | 0.8079 | | 0.3875 | 14.59 | 5400 | 0.4218 | 0.8084 | 0.8084 | | 0.3879 | 15.14 | 5600 | 0.4291 | 0.8074 | 0.8074 | | 0.3881 | 15.68 | 5800 | 0.4278 | 0.8037 | 0.8041 | | 0.3809 | 16.22 | 6000 | 0.4286 | 0.8042 | 0.8044 | | 0.3888 | 16.76 | 6200 | 0.4171 | 0.8088 | 0.8090 | | 0.3879 | 17.3 | 6400 | 0.4229 | 0.8070 | 0.8073 | | 0.3836 | 17.84 | 6600 | 0.4255 | 0.8047 | 0.8049 | | 0.3787 | 18.38 | 6800 | 0.4352 | 0.7976 | 0.7986 | | 0.3789 | 18.92 | 7000 | 0.4214 | 0.8086 | 0.8088 | | 0.376 | 19.46 | 7200 | 0.4231 | 0.8084 | 0.8084 | | 0.3864 | 20.0 | 7400 | 0.4186 | 0.8082 | 0.8083 | | 0.3789 | 20.54 | 7600 | 0.4243 | 0.8051 | 0.8054 | | 0.3781 | 21.08 | 7800 | 0.4221 | 0.8074 | 0.8076 | | 0.3781 | 21.62 | 8000 | 0.4171 | 0.8080 | 0.8081 | | 0.3727 | 22.16 | 8200 | 0.4221 | 0.8067 | 0.8069 | | 0.3811 | 22.7 | 8400 | 0.4233 | 0.8069 | 0.8073 | | 0.3725 | 23.24 | 8600 | 0.4180 | 0.8096 | 0.8096 | | 0.3732 | 23.78 | 8800 | 0.4205 | 0.8070 | 0.8071 | | 0.3704 | 24.32 | 9000 | 0.4216 | 0.8077 | 0.8078 | | 0.3744 | 24.86 | 9200 | 0.4196 | 0.8060 | 0.8061 | | 0.3814 | 25.41 | 9400 | 0.4205 | 0.8075 | 0.8076 | | 0.367 | 25.95 | 9600 | 0.4235 | 0.8074 | 0.8076 | | 0.372 | 26.49 | 9800 | 0.4239 | 0.8061 | 0.8063 | | 0.372 | 27.03 | 10000 | 0.4234 | 0.8076 | 0.8078 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_15M-L32_f

2024-05-03T17:15:44+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_15M-L32\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.4073 * F1 Score: 0.8143 * Accuracy: 0.8144 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

golf2248/u4t42vm

2024-05-03T17:15:50+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # Llama3_on_scigen_fixedprompt_server This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset. ## 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: 1e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Llama3_on_scigen_fixedprompt_server", "results": []}]}

moetezsa/Llama3_on_scigen_fixedprompt_server

2024-05-03T17:16:06+00:00

TAGS #peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us

# Llama3_on_scigen_fixedprompt_server This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the None dataset. ## 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: 1e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

feature-extraction

transformers

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

stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep46

2024-05-03T17:16:13+00:00

TAGS #transformers #safetensors #xlm-roberta #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GritLM-7B - GGUF - Model creator: https://huggingface.co/GritLM/ - Original model: https://huggingface.co/GritLM/GritLM-7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [GritLM-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q2_K.gguf) | Q2_K | 2.53GB | | [GritLM-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [GritLM-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_S.gguf) | IQ3_S | 2.96GB | | [GritLM-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [GritLM-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_M.gguf) | IQ3_M | 3.06GB | | [GritLM-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K.gguf) | Q3_K | 3.28GB | | [GritLM-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [GritLM-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [GritLM-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [GritLM-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_0.gguf) | Q4_0 | 3.83GB | | [GritLM-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [GritLM-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [GritLM-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K.gguf) | Q4_K | 4.07GB | | [GritLM-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [GritLM-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_1.gguf) | Q4_1 | 4.24GB | | [GritLM-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_0.gguf) | Q5_0 | 4.65GB | | [GritLM-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [GritLM-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K.gguf) | Q5_K | 4.78GB | | [GritLM-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [GritLM-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_1.gguf) | Q5_1 | 5.07GB | | [GritLM-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- pipeline_tag: text-generation inference: true license: apache-2.0 datasets: - GritLM/tulu2 tags: - mteb model-index: - name: GritLM-7B results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.478 - type: map_at_10 value: 54.955 - type: map_at_100 value: 54.955 - type: map_at_1000 value: 54.955 - type: map_at_3 value: 50.888999999999996 - type: map_at_5 value: 53.349999999999994 - type: mrr_at_1 value: 39.757999999999996 - type: mrr_at_10 value: 55.449000000000005 - type: mrr_at_100 value: 55.449000000000005 - type: mrr_at_1000 value: 55.449000000000005 - type: mrr_at_3 value: 51.37500000000001 - type: mrr_at_5 value: 53.822 - type: ndcg_at_1 value: 38.478 - type: ndcg_at_10 value: 63.239999999999995 - type: ndcg_at_100 value: 63.239999999999995 - type: ndcg_at_1000 value: 63.239999999999995 - type: ndcg_at_3 value: 54.935 - type: ndcg_at_5 value: 59.379000000000005 - type: precision_at_1 value: 38.478 - type: precision_at_10 value: 8.933 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 22.214 - type: precision_at_5 value: 15.491 - type: recall_at_1 value: 38.478 - type: recall_at_10 value: 89.331 - type: recall_at_100 value: 89.331 - type: recall_at_1000 value: 89.331 - type: recall_at_3 value: 66.643 - type: recall_at_5 value: 77.45400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 51.67144081472449 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 48.11256154264126 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.1935203751726 - type: cos_sim_spearman value: 86.35497970498659 - type: euclidean_pearson value: 85.46910708503744 - type: euclidean_spearman value: 85.13928935405485 - type: manhattan_pearson value: 85.68373836333303 - type: manhattan_spearman value: 85.40013867117746 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.86793640310432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 39.80291334130727 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.421 - type: map_at_10 value: 52.349000000000004 - type: map_at_100 value: 52.349000000000004 - type: map_at_1000 value: 52.349000000000004 - type: map_at_3 value: 48.17 - type: map_at_5 value: 50.432 - type: mrr_at_1 value: 47.353 - type: mrr_at_10 value: 58.387 - type: mrr_at_100 value: 58.387 - type: mrr_at_1000 value: 58.387 - type: mrr_at_3 value: 56.199 - type: mrr_at_5 value: 57.487 - type: ndcg_at_1 value: 47.353 - type: ndcg_at_10 value: 59.202 - type: ndcg_at_100 value: 58.848 - type: ndcg_at_1000 value: 58.831999999999994 - type: ndcg_at_3 value: 54.112 - type: ndcg_at_5 value: 56.312 - type: precision_at_1 value: 47.353 - type: precision_at_10 value: 11.459 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 26.133 - type: precision_at_5 value: 18.627 - type: recall_at_1 value: 38.421 - type: recall_at_10 value: 71.89 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 71.89 - type: recall_at_3 value: 56.58 - type: recall_at_5 value: 63.125 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.025999999999996 - type: map_at_10 value: 50.590999999999994 - type: map_at_100 value: 51.99700000000001 - type: map_at_1000 value: 52.11599999999999 - type: map_at_3 value: 47.435 - type: map_at_5 value: 49.236000000000004 - type: mrr_at_1 value: 48.28 - type: mrr_at_10 value: 56.814 - type: mrr_at_100 value: 57.446 - type: mrr_at_1000 value: 57.476000000000006 - type: mrr_at_3 value: 54.958 - type: mrr_at_5 value: 56.084999999999994 - type: ndcg_at_1 value: 48.28 - type: ndcg_at_10 value: 56.442 - type: ndcg_at_100 value: 60.651999999999994 - type: ndcg_at_1000 value: 62.187000000000005 - type: ndcg_at_3 value: 52.866 - type: ndcg_at_5 value: 54.515 - type: precision_at_1 value: 48.28 - type: precision_at_10 value: 10.586 - type: precision_at_100 value: 1.6310000000000002 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 25.945 - type: precision_at_5 value: 18.076 - type: recall_at_1 value: 38.025999999999996 - type: recall_at_10 value: 66.11399999999999 - type: recall_at_100 value: 83.339 - type: recall_at_1000 value: 92.413 - type: recall_at_3 value: 54.493 - type: recall_at_5 value: 59.64699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 47.905 - type: map_at_10 value: 61.58 - type: map_at_100 value: 62.605 - type: map_at_1000 value: 62.637 - type: map_at_3 value: 58.074000000000005 - type: map_at_5 value: 60.260000000000005 - type: mrr_at_1 value: 54.42 - type: mrr_at_10 value: 64.847 - type: mrr_at_100 value: 65.403 - type: mrr_at_1000 value: 65.41900000000001 - type: mrr_at_3 value: 62.675000000000004 - type: mrr_at_5 value: 64.101 - type: ndcg_at_1 value: 54.42 - type: ndcg_at_10 value: 67.394 - type: ndcg_at_100 value: 70.846 - type: ndcg_at_1000 value: 71.403 - type: ndcg_at_3 value: 62.025 - type: ndcg_at_5 value: 65.032 - type: precision_at_1 value: 54.42 - type: precision_at_10 value: 10.646 - type: precision_at_100 value: 1.325 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 27.398 - type: precision_at_5 value: 18.796 - type: recall_at_1 value: 47.905 - type: recall_at_10 value: 80.84599999999999 - type: recall_at_100 value: 95.078 - type: recall_at_1000 value: 98.878 - type: recall_at_3 value: 67.05600000000001 - type: recall_at_5 value: 74.261 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.745 - type: map_at_10 value: 41.021 - type: map_at_100 value: 41.021 - type: map_at_1000 value: 41.021 - type: map_at_3 value: 37.714999999999996 - type: map_at_5 value: 39.766 - type: mrr_at_1 value: 33.559 - type: mrr_at_10 value: 43.537 - type: mrr_at_100 value: 43.537 - type: mrr_at_1000 value: 43.537 - type: mrr_at_3 value: 40.546 - type: mrr_at_5 value: 42.439 - type: ndcg_at_1 value: 33.559 - type: ndcg_at_10 value: 46.781 - type: ndcg_at_100 value: 46.781 - type: ndcg_at_1000 value: 46.781 - type: ndcg_at_3 value: 40.516000000000005 - type: ndcg_at_5 value: 43.957 - type: precision_at_1 value: 33.559 - type: precision_at_10 value: 7.198 - type: precision_at_100 value: 0.72 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 17.1 - type: precision_at_5 value: 12.316 - type: recall_at_1 value: 30.745 - type: recall_at_10 value: 62.038000000000004 - type: recall_at_100 value: 62.038000000000004 - type: recall_at_1000 value: 62.038000000000004 - type: recall_at_3 value: 45.378 - type: recall_at_5 value: 53.580000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.637999999999998 - type: map_at_10 value: 31.05 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.05 - type: map_at_3 value: 27.628000000000004 - type: map_at_5 value: 29.767 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 36.131 - type: mrr_at_100 value: 36.131 - type: mrr_at_1000 value: 36.131 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 35.143 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 37.478 - type: ndcg_at_100 value: 37.469 - type: ndcg_at_1000 value: 37.469 - type: ndcg_at_3 value: 31.757999999999996 - type: ndcg_at_5 value: 34.821999999999996 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.188999999999999 - type: precision_at_100 value: 0.719 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 15.837000000000002 - type: precision_at_5 value: 11.841 - type: recall_at_1 value: 19.637999999999998 - type: recall_at_10 value: 51.836000000000006 - type: recall_at_100 value: 51.836000000000006 - type: recall_at_1000 value: 51.836000000000006 - type: recall_at_3 value: 36.384 - type: recall_at_5 value: 43.964 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 34.884 - type: map_at_10 value: 47.88 - type: map_at_100 value: 47.88 - type: map_at_1000 value: 47.88 - type: map_at_3 value: 43.85 - type: map_at_5 value: 46.414 - type: mrr_at_1 value: 43.022 - type: mrr_at_10 value: 53.569 - type: mrr_at_100 value: 53.569 - type: mrr_at_1000 value: 53.569 - type: mrr_at_3 value: 51.075 - type: mrr_at_5 value: 52.725 - type: ndcg_at_1 value: 43.022 - type: ndcg_at_10 value: 54.461000000000006 - type: ndcg_at_100 value: 54.388000000000005 - type: ndcg_at_1000 value: 54.388000000000005 - type: ndcg_at_3 value: 48.864999999999995 - type: ndcg_at_5 value: 52.032000000000004 - type: precision_at_1 value: 43.022 - type: precision_at_10 value: 9.885 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 23.612 - type: precision_at_5 value: 16.997 - type: recall_at_1 value: 34.884 - type: recall_at_10 value: 68.12899999999999 - type: recall_at_100 value: 68.12899999999999 - type: recall_at_1000 value: 68.12899999999999 - type: recall_at_3 value: 52.428 - type: recall_at_5 value: 60.662000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.588 - type: map_at_10 value: 43.85 - type: map_at_100 value: 45.317 - type: map_at_1000 value: 45.408 - type: map_at_3 value: 39.73 - type: map_at_5 value: 42.122 - type: mrr_at_1 value: 38.927 - type: mrr_at_10 value: 49.582 - type: mrr_at_100 value: 50.39 - type: mrr_at_1000 value: 50.426 - type: mrr_at_3 value: 46.518 - type: mrr_at_5 value: 48.271 - type: ndcg_at_1 value: 38.927 - type: ndcg_at_10 value: 50.605999999999995 - type: ndcg_at_100 value: 56.22200000000001 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 44.232 - type: ndcg_at_5 value: 47.233999999999995 - type: precision_at_1 value: 38.927 - type: precision_at_10 value: 9.429 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 21.271 - type: precision_at_5 value: 15.434000000000001 - type: recall_at_1 value: 31.588 - type: recall_at_10 value: 64.836 - type: recall_at_100 value: 88.066 - type: recall_at_1000 value: 97.748 - type: recall_at_3 value: 47.128 - type: recall_at_5 value: 54.954 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.956083333333336 - type: map_at_10 value: 43.33483333333333 - type: map_at_100 value: 44.64883333333333 - type: map_at_1000 value: 44.75 - type: map_at_3 value: 39.87741666666666 - type: map_at_5 value: 41.86766666666667 - type: mrr_at_1 value: 38.06341666666667 - type: mrr_at_10 value: 47.839666666666666 - type: mrr_at_100 value: 48.644000000000005 - type: mrr_at_1000 value: 48.68566666666667 - type: mrr_at_3 value: 45.26358333333334 - type: mrr_at_5 value: 46.790000000000006 - type: ndcg_at_1 value: 38.06341666666667 - type: ndcg_at_10 value: 49.419333333333334 - type: ndcg_at_100 value: 54.50166666666667 - type: ndcg_at_1000 value: 56.161166666666674 - type: ndcg_at_3 value: 43.982416666666666 - type: ndcg_at_5 value: 46.638083333333334 - type: precision_at_1 value: 38.06341666666667 - type: precision_at_10 value: 8.70858333333333 - type: precision_at_100 value: 1.327 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.37816666666667 - type: precision_at_5 value: 14.516333333333334 - type: recall_at_1 value: 31.956083333333336 - type: recall_at_10 value: 62.69458333333334 - type: recall_at_100 value: 84.46433333333334 - type: recall_at_1000 value: 95.58449999999999 - type: recall_at_3 value: 47.52016666666666 - type: recall_at_5 value: 54.36066666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.912 - type: map_at_10 value: 38.291 - type: map_at_100 value: 39.44 - type: map_at_1000 value: 39.528 - type: map_at_3 value: 35.638 - type: map_at_5 value: 37.218 - type: mrr_at_1 value: 32.822 - type: mrr_at_10 value: 41.661 - type: mrr_at_100 value: 42.546 - type: mrr_at_1000 value: 42.603 - type: mrr_at_3 value: 39.238 - type: mrr_at_5 value: 40.726 - type: ndcg_at_1 value: 32.822 - type: ndcg_at_10 value: 43.373 - type: ndcg_at_100 value: 48.638 - type: ndcg_at_1000 value: 50.654999999999994 - type: ndcg_at_3 value: 38.643 - type: ndcg_at_5 value: 41.126000000000005 - type: precision_at_1 value: 32.822 - type: precision_at_10 value: 6.8709999999999996 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.82 - type: precision_at_5 value: 11.718 - type: recall_at_1 value: 28.912 - type: recall_at_10 value: 55.376999999999995 - type: recall_at_100 value: 79.066 - type: recall_at_1000 value: 93.664 - type: recall_at_3 value: 42.569 - type: recall_at_5 value: 48.719 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.181 - type: map_at_10 value: 31.462 - type: map_at_100 value: 32.73 - type: map_at_1000 value: 32.848 - type: map_at_3 value: 28.57 - type: map_at_5 value: 30.182 - type: mrr_at_1 value: 27.185 - type: mrr_at_10 value: 35.846000000000004 - type: mrr_at_100 value: 36.811 - type: mrr_at_1000 value: 36.873 - type: mrr_at_3 value: 33.437 - type: mrr_at_5 value: 34.813 - type: ndcg_at_1 value: 27.185 - type: ndcg_at_10 value: 36.858000000000004 - type: ndcg_at_100 value: 42.501 - type: ndcg_at_1000 value: 44.945 - type: ndcg_at_3 value: 32.066 - type: ndcg_at_5 value: 34.29 - type: precision_at_1 value: 27.185 - type: precision_at_10 value: 6.752 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 15.290000000000001 - type: precision_at_5 value: 11.004999999999999 - type: recall_at_1 value: 22.181 - type: recall_at_10 value: 48.513 - type: recall_at_100 value: 73.418 - type: recall_at_1000 value: 90.306 - type: recall_at_3 value: 35.003 - type: recall_at_5 value: 40.876000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.934999999999995 - type: map_at_10 value: 44.727 - type: map_at_100 value: 44.727 - type: map_at_1000 value: 44.727 - type: map_at_3 value: 40.918 - type: map_at_5 value: 42.961 - type: mrr_at_1 value: 39.646 - type: mrr_at_10 value: 48.898 - type: mrr_at_100 value: 48.898 - type: mrr_at_1000 value: 48.898 - type: mrr_at_3 value: 45.896 - type: mrr_at_5 value: 47.514 - type: ndcg_at_1 value: 39.646 - type: ndcg_at_10 value: 50.817 - type: ndcg_at_100 value: 50.803 - type: ndcg_at_1000 value: 50.803 - type: ndcg_at_3 value: 44.507999999999996 - type: ndcg_at_5 value: 47.259 - type: precision_at_1 value: 39.646 - type: precision_at_10 value: 8.759 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_3 value: 20.274 - type: precision_at_5 value: 14.366000000000001 - type: recall_at_1 value: 33.934999999999995 - type: recall_at_10 value: 65.037 - type: recall_at_100 value: 65.037 - type: recall_at_1000 value: 65.037 - type: recall_at_3 value: 47.439 - type: recall_at_5 value: 54.567 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.058 - type: map_at_10 value: 43.137 - type: map_at_100 value: 43.137 - type: map_at_1000 value: 43.137 - type: map_at_3 value: 39.882 - type: map_at_5 value: 41.379 - type: mrr_at_1 value: 38.933 - type: mrr_at_10 value: 48.344 - type: mrr_at_100 value: 48.344 - type: mrr_at_1000 value: 48.344 - type: mrr_at_3 value: 45.652 - type: mrr_at_5 value: 46.877 - type: ndcg_at_1 value: 38.933 - type: ndcg_at_10 value: 49.964 - type: ndcg_at_100 value: 49.242000000000004 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 44.605 - type: ndcg_at_5 value: 46.501999999999995 - type: precision_at_1 value: 38.933 - type: precision_at_10 value: 9.427000000000001 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 20.685000000000002 - type: precision_at_5 value: 14.585 - type: recall_at_1 value: 32.058 - type: recall_at_10 value: 63.074 - type: recall_at_100 value: 63.074 - type: recall_at_1000 value: 63.074 - type: recall_at_3 value: 47.509 - type: recall_at_5 value: 52.455 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.029000000000003 - type: map_at_10 value: 34.646 - type: map_at_100 value: 34.646 - type: map_at_1000 value: 34.646 - type: map_at_3 value: 31.456 - type: map_at_5 value: 33.138 - type: mrr_at_1 value: 28.281 - type: mrr_at_10 value: 36.905 - type: mrr_at_100 value: 36.905 - type: mrr_at_1000 value: 36.905 - type: mrr_at_3 value: 34.011 - type: mrr_at_5 value: 35.638 - type: ndcg_at_1 value: 28.281 - type: ndcg_at_10 value: 40.159 - type: ndcg_at_100 value: 40.159 - type: ndcg_at_1000 value: 40.159 - type: ndcg_at_3 value: 33.995 - type: ndcg_at_5 value: 36.836999999999996 - type: precision_at_1 value: 28.281 - type: precision_at_10 value: 6.358999999999999 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.064 - type: precision_at_3 value: 14.233 - type: precision_at_5 value: 10.314 - type: recall_at_1 value: 26.029000000000003 - type: recall_at_10 value: 55.08 - type: recall_at_100 value: 55.08 - type: recall_at_1000 value: 55.08 - type: recall_at_3 value: 38.487 - type: recall_at_5 value: 45.308 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.842999999999998 - type: map_at_10 value: 22.101000000000003 - type: map_at_100 value: 24.319 - type: map_at_1000 value: 24.51 - type: map_at_3 value: 18.372 - type: map_at_5 value: 20.323 - type: mrr_at_1 value: 27.948 - type: mrr_at_10 value: 40.321 - type: mrr_at_100 value: 41.262 - type: mrr_at_1000 value: 41.297 - type: mrr_at_3 value: 36.558 - type: mrr_at_5 value: 38.824999999999996 - type: ndcg_at_1 value: 27.948 - type: ndcg_at_10 value: 30.906 - type: ndcg_at_100 value: 38.986 - type: ndcg_at_1000 value: 42.136 - type: ndcg_at_3 value: 24.911 - type: ndcg_at_5 value: 27.168999999999997 - type: precision_at_1 value: 27.948 - type: precision_at_10 value: 9.798 - type: precision_at_100 value: 1.8399999999999999 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 18.328 - type: precision_at_5 value: 14.502 - type: recall_at_1 value: 12.842999999999998 - type: recall_at_10 value: 37.245 - type: recall_at_100 value: 64.769 - type: recall_at_1000 value: 82.055 - type: recall_at_3 value: 23.159 - type: recall_at_5 value: 29.113 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.934000000000001 - type: map_at_10 value: 21.915000000000003 - type: map_at_100 value: 21.915000000000003 - type: map_at_1000 value: 21.915000000000003 - type: map_at_3 value: 14.623 - type: map_at_5 value: 17.841 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 78.994 - type: mrr_at_100 value: 78.994 - type: mrr_at_1000 value: 78.994 - type: mrr_at_3 value: 77.208 - type: mrr_at_5 value: 78.55799999999999 - type: ndcg_at_1 value: 60.62499999999999 - type: ndcg_at_10 value: 46.604 - type: ndcg_at_100 value: 35.653 - type: ndcg_at_1000 value: 35.531 - type: ndcg_at_3 value: 50.605 - type: ndcg_at_5 value: 48.730000000000004 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 37.75 - type: precision_at_100 value: 3.775 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 54.417 - type: precision_at_5 value: 48.15 - type: recall_at_1 value: 8.934000000000001 - type: recall_at_10 value: 28.471000000000004 - type: recall_at_100 value: 28.471000000000004 - type: recall_at_1000 value: 28.471000000000004 - type: recall_at_3 value: 16.019 - type: recall_at_5 value: 21.410999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.81899999999999 - type: map_at_10 value: 78.034 - type: map_at_100 value: 78.034 - type: map_at_1000 value: 78.034 - type: map_at_3 value: 76.43100000000001 - type: map_at_5 value: 77.515 - type: mrr_at_1 value: 71.542 - type: mrr_at_10 value: 81.638 - type: mrr_at_100 value: 81.638 - type: mrr_at_1000 value: 81.638 - type: mrr_at_3 value: 80.403 - type: mrr_at_5 value: 81.256 - type: ndcg_at_1 value: 71.542 - type: ndcg_at_10 value: 82.742 - type: ndcg_at_100 value: 82.741 - type: ndcg_at_1000 value: 82.741 - type: ndcg_at_3 value: 80.039 - type: ndcg_at_5 value: 81.695 - type: precision_at_1 value: 71.542 - type: precision_at_10 value: 10.387 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 31.447999999999997 - type: precision_at_5 value: 19.91 - type: recall_at_1 value: 66.81899999999999 - type: recall_at_10 value: 93.372 - type: recall_at_100 value: 93.372 - type: recall_at_1000 value: 93.372 - type: recall_at_3 value: 86.33 - type: recall_at_5 value: 90.347 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 31.158 - type: map_at_10 value: 52.017 - type: map_at_100 value: 54.259 - type: map_at_1000 value: 54.367 - type: map_at_3 value: 45.738 - type: map_at_5 value: 49.283 - type: mrr_at_1 value: 57.87 - type: mrr_at_10 value: 66.215 - type: mrr_at_100 value: 66.735 - type: mrr_at_1000 value: 66.75 - type: mrr_at_3 value: 64.043 - type: mrr_at_5 value: 65.116 - type: ndcg_at_1 value: 57.87 - type: ndcg_at_10 value: 59.946999999999996 - type: ndcg_at_100 value: 66.31099999999999 - type: ndcg_at_1000 value: 67.75999999999999 - type: ndcg_at_3 value: 55.483000000000004 - type: ndcg_at_5 value: 56.891000000000005 - type: precision_at_1 value: 57.87 - type: precision_at_10 value: 16.497 - type: precision_at_100 value: 2.321 - type: precision_at_1000 value: 0.258 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.067999999999998 - type: recall_at_1 value: 31.158 - type: recall_at_10 value: 67.381 - type: recall_at_100 value: 89.464 - type: recall_at_1000 value: 97.989 - type: recall_at_3 value: 50.553000000000004 - type: recall_at_5 value: 57.824 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 42.073 - type: map_at_10 value: 72.418 - type: map_at_100 value: 73.175 - type: map_at_1000 value: 73.215 - type: map_at_3 value: 68.791 - type: map_at_5 value: 71.19 - type: mrr_at_1 value: 84.146 - type: mrr_at_10 value: 88.994 - type: mrr_at_100 value: 89.116 - type: mrr_at_1000 value: 89.12 - type: mrr_at_3 value: 88.373 - type: mrr_at_5 value: 88.82 - type: ndcg_at_1 value: 84.146 - type: ndcg_at_10 value: 79.404 - type: ndcg_at_100 value: 81.83200000000001 - type: ndcg_at_1000 value: 82.524 - type: ndcg_at_3 value: 74.595 - type: ndcg_at_5 value: 77.474 - type: precision_at_1 value: 84.146 - type: precision_at_10 value: 16.753999999999998 - type: precision_at_100 value: 1.8599999999999999 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 48.854 - type: precision_at_5 value: 31.579 - type: recall_at_1 value: 42.073 - type: recall_at_10 value: 83.768 - type: recall_at_100 value: 93.018 - type: recall_at_1000 value: 97.481 - type: recall_at_3 value: 73.282 - type: recall_at_5 value: 78.947 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.698 - type: map_at_10 value: 34.585 - type: map_at_100 value: 35.782000000000004 - type: map_at_1000 value: 35.825 - type: map_at_3 value: 30.397999999999996 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 22.192 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 36.218 - type: mrr_at_1000 value: 36.256 - type: mrr_at_3 value: 30.986000000000004 - type: mrr_at_5 value: 33.268 - type: ndcg_at_1 value: 22.192 - type: ndcg_at_10 value: 41.957 - type: ndcg_at_100 value: 47.658 - type: ndcg_at_1000 value: 48.697 - type: ndcg_at_3 value: 33.433 - type: ndcg_at_5 value: 37.551 - type: precision_at_1 value: 22.192 - type: precision_at_10 value: 6.781 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.365 - type: precision_at_5 value: 10.713000000000001 - type: recall_at_1 value: 21.698 - type: recall_at_10 value: 64.79 - type: recall_at_100 value: 91.071 - type: recall_at_1000 value: 98.883 - type: recall_at_3 value: 41.611 - type: recall_at_5 value: 51.459999999999994 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.52153488185864 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 36.80090398444147 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.920999999999999 - type: map_at_10 value: 16.049 - type: map_at_100 value: 16.049 - type: map_at_1000 value: 16.049 - type: map_at_3 value: 11.865 - type: map_at_5 value: 13.657 - type: mrr_at_1 value: 53.87 - type: mrr_at_10 value: 62.291 - type: mrr_at_100 value: 62.291 - type: mrr_at_1000 value: 62.291 - type: mrr_at_3 value: 60.681 - type: mrr_at_5 value: 61.61 - type: ndcg_at_1 value: 51.23799999999999 - type: ndcg_at_10 value: 40.892 - type: ndcg_at_100 value: 26.951999999999998 - type: ndcg_at_1000 value: 26.474999999999998 - type: ndcg_at_3 value: 46.821 - type: ndcg_at_5 value: 44.333 - type: precision_at_1 value: 53.251000000000005 - type: precision_at_10 value: 30.124000000000002 - type: precision_at_100 value: 3.012 - type: precision_at_1000 value: 0.301 - type: precision_at_3 value: 43.55 - type: precision_at_5 value: 38.266 - type: recall_at_1 value: 6.920999999999999 - type: recall_at_10 value: 20.852 - type: recall_at_100 value: 20.852 - type: recall_at_1000 value: 20.852 - type: recall_at_3 value: 13.628000000000002 - type: recall_at_5 value: 16.273 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 46.827999999999996 - type: map_at_10 value: 63.434000000000005 - type: map_at_100 value: 63.434000000000005 - type: map_at_1000 value: 63.434000000000005 - type: map_at_3 value: 59.794000000000004 - type: map_at_5 value: 62.08 - type: mrr_at_1 value: 52.288999999999994 - type: mrr_at_10 value: 65.95 - type: mrr_at_100 value: 65.95 - type: mrr_at_1000 value: 65.95 - type: mrr_at_3 value: 63.413 - type: mrr_at_5 value: 65.08 - type: ndcg_at_1 value: 52.288999999999994 - type: ndcg_at_10 value: 70.301 - type: ndcg_at_100 value: 70.301 - type: ndcg_at_1000 value: 70.301 - type: ndcg_at_3 value: 63.979 - type: ndcg_at_5 value: 67.582 - type: precision_at_1 value: 52.288999999999994 - type: precision_at_10 value: 10.576 - type: precision_at_100 value: 1.058 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 28.177000000000003 - type: precision_at_5 value: 19.073 - type: recall_at_1 value: 46.827999999999996 - type: recall_at_10 value: 88.236 - type: recall_at_100 value: 88.236 - type: recall_at_1000 value: 88.236 - type: recall_at_3 value: 72.371 - type: recall_at_5 value: 80.56 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.652 - type: map_at_10 value: 85.953 - type: map_at_100 value: 85.953 - type: map_at_1000 value: 85.953 - type: map_at_3 value: 83.05399999999999 - type: map_at_5 value: 84.89 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.473 - type: mrr_at_100 value: 88.473 - type: mrr_at_1000 value: 88.473 - type: mrr_at_3 value: 87.592 - type: mrr_at_5 value: 88.211 - type: ndcg_at_1 value: 82.44 - type: ndcg_at_10 value: 89.467 - type: ndcg_at_100 value: 89.33 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 86.822 - type: ndcg_at_5 value: 88.307 - type: precision_at_1 value: 82.44 - type: precision_at_10 value: 13.616 - type: precision_at_100 value: 1.362 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 38.117000000000004 - type: precision_at_5 value: 25.05 - type: recall_at_1 value: 71.652 - type: recall_at_10 value: 96.224 - type: recall_at_100 value: 96.224 - type: recall_at_1000 value: 96.224 - type: recall_at_3 value: 88.571 - type: recall_at_5 value: 92.812 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 61.295010338050474 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 67.26380819328142 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.683 - type: map_at_10 value: 14.924999999999999 - type: map_at_100 value: 17.532 - type: map_at_1000 value: 17.875 - type: map_at_3 value: 10.392 - type: map_at_5 value: 12.592 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 39.951 - type: mrr_at_100 value: 41.025 - type: mrr_at_1000 value: 41.056 - type: mrr_at_3 value: 36.317 - type: mrr_at_5 value: 38.412 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.410999999999998 - type: ndcg_at_100 value: 33.79 - type: ndcg_at_1000 value: 39.035 - type: ndcg_at_3 value: 22.845 - type: ndcg_at_5 value: 20.080000000000002 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 12.790000000000001 - type: precision_at_100 value: 2.633 - type: precision_at_1000 value: 0.388 - type: precision_at_3 value: 21.367 - type: precision_at_5 value: 17.7 - type: recall_at_1 value: 5.683 - type: recall_at_10 value: 25.91 - type: recall_at_100 value: 53.443 - type: recall_at_1000 value: 78.73 - type: recall_at_3 value: 13.003 - type: recall_at_5 value: 17.932000000000002 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.677978681023 - type: cos_sim_spearman value: 83.13093441058189 - type: euclidean_pearson value: 83.35535759341572 - type: euclidean_spearman value: 83.42583744219611 - type: manhattan_pearson value: 83.2243124045889 - type: manhattan_spearman value: 83.39801618652632 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.68960206569666 - type: cos_sim_spearman value: 77.3368966488535 - type: euclidean_pearson value: 77.62828980560303 - type: euclidean_spearman value: 76.77951481444651 - type: manhattan_pearson value: 77.88637240839041 - type: manhattan_spearman value: 77.22157841466188 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.18745821650724 - type: cos_sim_spearman value: 85.04423285574542 - type: euclidean_pearson value: 85.46604816931023 - type: euclidean_spearman value: 85.5230593932974 - type: manhattan_pearson value: 85.57912805986261 - type: manhattan_spearman value: 85.65955905111873 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.6715333300355 - type: cos_sim_spearman value: 82.9058522514908 - type: euclidean_pearson value: 83.9640357424214 - type: euclidean_spearman value: 83.60415457472637 - type: manhattan_pearson value: 84.05621005853469 - type: manhattan_spearman value: 83.87077724707746 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.82422928098886 - type: cos_sim_spearman value: 88.12660311894628 - type: euclidean_pearson value: 87.50974805056555 - type: euclidean_spearman value: 87.91957275596677 - type: manhattan_pearson value: 87.74119404878883 - type: manhattan_spearman value: 88.2808922165719 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.80605838552093 - type: cos_sim_spearman value: 86.24123388765678 - type: euclidean_pearson value: 85.32648347339814 - type: euclidean_spearman value: 85.60046671950158 - type: manhattan_pearson value: 85.53800168487811 - type: manhattan_spearman value: 85.89542420480763 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.87540978988132 - type: cos_sim_spearman value: 90.12715295099461 - type: euclidean_pearson value: 91.61085993525275 - type: euclidean_spearman value: 91.31835942311758 - type: manhattan_pearson value: 91.57500202032934 - type: manhattan_spearman value: 91.1790925526635 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 69.87136205329556 - type: cos_sim_spearman value: 68.6253154635078 - type: euclidean_pearson value: 68.91536015034222 - type: euclidean_spearman value: 67.63744649352542 - type: manhattan_pearson value: 69.2000713045275 - type: manhattan_spearman value: 68.16002901587316 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.21849551039082 - type: cos_sim_spearman value: 85.6392959372461 - type: euclidean_pearson value: 85.92050852609488 - type: euclidean_spearman value: 85.97205649009734 - type: manhattan_pearson value: 86.1031154802254 - type: manhattan_spearman value: 86.26791155517466 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.83953958636627 - type: mrr value: 96.71167612344082 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 64.994 - type: map_at_10 value: 74.763 - type: map_at_100 value: 75.127 - type: map_at_1000 value: 75.143 - type: map_at_3 value: 71.824 - type: map_at_5 value: 73.71 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 75.749 - type: mrr_at_100 value: 75.922 - type: mrr_at_1000 value: 75.938 - type: mrr_at_3 value: 73.556 - type: mrr_at_5 value: 74.739 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 79.174 - type: ndcg_at_100 value: 80.41 - type: ndcg_at_1000 value: 80.804 - type: ndcg_at_3 value: 74.361 - type: ndcg_at_5 value: 76.861 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.333 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.778 - type: precision_at_5 value: 19.067 - type: recall_at_1 value: 64.994 - type: recall_at_10 value: 91.822 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.878 - type: recall_at_5 value: 85.172 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72079207920792 - type: cos_sim_ap value: 93.00265215525152 - type: cos_sim_f1 value: 85.06596306068602 - type: cos_sim_precision value: 90.05586592178771 - type: cos_sim_recall value: 80.60000000000001 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 91.22371407479089 - type: dot_f1 value: 82.34693877551021 - type: dot_precision value: 84.0625 - type: dot_recall value: 80.7 - type: euclidean_accuracy value: 99.71881188118812 - type: euclidean_ap value: 92.88449963304728 - type: euclidean_f1 value: 85.19480519480518 - type: euclidean_precision value: 88.64864864864866 - type: euclidean_recall value: 82.0 - type: manhattan_accuracy value: 99.73267326732673 - type: manhattan_ap value: 93.23055393056883 - type: manhattan_f1 value: 85.88957055214725 - type: manhattan_precision value: 87.86610878661088 - type: manhattan_recall value: 84.0 - type: max_accuracy value: 99.73267326732673 - type: max_ap value: 93.23055393056883 - type: max_f1 value: 85.88957055214725 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 77.3305735900358 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 41.32967136540674 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.95514866379359 - type: mrr value: 56.95423245055598 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.783007208997144 - type: cos_sim_spearman value: 30.373444721540533 - type: dot_pearson value: 29.210604111143905 - type: dot_spearman value: 29.98809758085659 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.234 - type: map_at_10 value: 1.894 - type: map_at_100 value: 1.894 - type: map_at_1000 value: 1.894 - type: map_at_3 value: 0.636 - type: map_at_5 value: 1.0 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.667 - type: mrr_at_100 value: 93.667 - type: mrr_at_1000 value: 93.667 - type: mrr_at_3 value: 93.667 - type: mrr_at_5 value: 93.667 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 74.798 - type: ndcg_at_100 value: 16.462 - type: ndcg_at_1000 value: 7.0889999999999995 - type: ndcg_at_3 value: 80.754 - type: ndcg_at_5 value: 77.319 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 7.8 - type: precision_at_1000 value: 0.7799999999999999 - type: precision_at_3 value: 83.333 - type: precision_at_5 value: 80.80000000000001 - type: recall_at_1 value: 0.234 - type: recall_at_10 value: 2.093 - type: recall_at_100 value: 2.093 - type: recall_at_1000 value: 2.093 - type: recall_at_3 value: 0.662 - type: recall_at_5 value: 1.0739999999999998 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 10.866000000000001 - type: map_at_100 value: 10.866000000000001 - type: map_at_1000 value: 10.866000000000001 - type: map_at_3 value: 5.909 - type: map_at_5 value: 7.35 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 53.583000000000006 - type: mrr_at_100 value: 53.583000000000006 - type: mrr_at_1000 value: 53.583000000000006 - type: mrr_at_3 value: 49.32 - type: mrr_at_5 value: 51.769 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 27.926000000000002 - type: ndcg_at_100 value: 22.701 - type: ndcg_at_1000 value: 22.701 - type: ndcg_at_3 value: 32.073 - type: ndcg_at_5 value: 28.327999999999996 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 24.694 - type: precision_at_100 value: 2.469 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 17.702 - type: recall_at_100 value: 17.702 - type: recall_at_1000 value: 17.702 - type: recall_at_3 value: 7.208 - type: recall_at_5 value: 9.748999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.79960000000001 - type: ap value: 15.467565415565815 - type: f1 value: 55.28639823443618 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.7792869269949 - type: f1 value: 65.08597154774318 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 55.70352297774293 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 88.27561542588067 - type: cos_sim_ap value: 81.08262141256193 - type: cos_sim_f1 value: 73.82341501361338 - type: cos_sim_precision value: 72.5720112159062 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 86.66030875603504 - type: dot_ap value: 76.6052349228621 - type: dot_f1 value: 70.13897280966768 - type: dot_precision value: 64.70457079152732 - type: dot_recall value: 76.56992084432717 - type: euclidean_accuracy value: 88.37098408535495 - type: euclidean_ap value: 81.12515230092113 - type: euclidean_f1 value: 74.10338225909379 - type: euclidean_precision value: 71.76761433868974 - type: euclidean_recall value: 76.59630606860158 - type: manhattan_accuracy value: 88.34118137926924 - type: manhattan_ap value: 80.95751834536561 - type: manhattan_f1 value: 73.9119496855346 - type: manhattan_precision value: 70.625 - type: manhattan_recall value: 77.5197889182058 - type: max_accuracy value: 88.37098408535495 - type: max_ap value: 81.12515230092113 - type: max_f1 value: 74.10338225909379 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.79896767182831 - type: cos_sim_ap value: 87.40071784061065 - type: cos_sim_f1 value: 79.87753144712087 - type: cos_sim_precision value: 76.67304015296367 - type: cos_sim_recall value: 83.3615645210964 - type: dot_accuracy value: 88.95486474948578 - type: dot_ap value: 86.00227979119943 - type: dot_f1 value: 78.54601474525914 - type: dot_precision value: 75.00525394045535 - type: dot_recall value: 82.43763473975977 - type: euclidean_accuracy value: 89.7892653393876 - type: euclidean_ap value: 87.42174706480819 - type: euclidean_f1 value: 80.07283321194465 - type: euclidean_precision value: 75.96738529574351 - type: euclidean_recall value: 84.6473668001232 - type: manhattan_accuracy value: 89.8474793340319 - type: manhattan_ap value: 87.47814292587448 - type: manhattan_f1 value: 80.15461150280949 - type: manhattan_precision value: 74.88798234468 - type: manhattan_recall value: 86.21804742839544 - type: max_accuracy value: 89.8474793340319 - type: max_ap value: 87.47814292587448 - type: max_f1 value: 80.15461150280949 --- # Model Summary > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm) - **Paper:** https://arxiv.org/abs/2402.09906 - **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview - **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh | Model | Description | |-------|-------------| | [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT | | [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT | # Use The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference). # Citation ```bibtex @misc{muennighoff2024generative, title={Generative Representational Instruction Tuning}, author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela}, year={2024}, eprint={2402.09906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{}

RichardErkhov/GritLM_-_GritLM-7B-gguf

2024-05-03T17:18:16+00:00

TAGS #gguf #region-us

Quantization made by Richard Erkhov. Github Discord Request more models GritLM-7B - GGUF * Model creator: URL * Original model: URL Name: GritLM-7B.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB Name: GritLM-7B.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.81GB Name: GritLM-7B.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.96GB Name: GritLM-7B.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.95GB Name: GritLM-7B.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB Name: GritLM-7B.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.28GB Name: GritLM-7B.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.28GB Name: GritLM-7B.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.56GB Name: GritLM-7B.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.67GB Name: GritLM-7B.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.83GB Name: GritLM-7B.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.87GB Name: GritLM-7B.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.86GB Name: GritLM-7B.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.07GB Name: GritLM-7B.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.07GB Name: GritLM-7B.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.24GB Name: GritLM-7B.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.65GB Name: GritLM-7B.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.65GB Name: GritLM-7B.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.78GB Name: GritLM-7B.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.78GB Name: GritLM-7B.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.07GB Name: GritLM-7B.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.53GB Original model description: --------------------------- pipeline\_tag: text-generation inference: true license: apache-2.0 datasets: * GritLM/tulu2 tags: * mteb model-index: * name: GritLM-7B results: + task: type: Classification dataset: type: mteb/amazon\_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 + task: type: Classification dataset: type: mteb/amazon\_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 + task: type: Classification dataset: type: mteb/amazon\_reviews\_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 + task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.478 - type: map\_at\_10 value: 54.955 - type: map\_at\_100 value: 54.955 - type: map\_at\_1000 value: 54.955 - type: map\_at\_3 value: 50.888999999999996 - type: map\_at\_5 value: 53.349999999999994 - type: mrr\_at\_1 value: 39.757999999999996 - type: mrr\_at\_10 value: 55.449000000000005 - type: mrr\_at\_100 value: 55.449000000000005 - type: mrr\_at\_1000 value: 55.449000000000005 - type: mrr\_at\_3 value: 51.37500000000001 - type: mrr\_at\_5 value: 53.822 - type: ndcg\_at\_1 value: 38.478 - type: ndcg\_at\_10 value: 63.239999999999995 - type: ndcg\_at\_100 value: 63.239999999999995 - type: ndcg\_at\_1000 value: 63.239999999999995 - type: ndcg\_at\_3 value: 54.935 - type: ndcg\_at\_5 value: 59.379000000000005 - type: precision\_at\_1 value: 38.478 - type: precision\_at\_10 value: 8.933 - type: precision\_at\_100 value: 0.893 - type: precision\_at\_1000 value: 0.089 - type: precision\_at\_3 value: 22.214 - type: precision\_at\_5 value: 15.491 - type: recall\_at\_1 value: 38.478 - type: recall\_at\_10 value: 89.331 - type: recall\_at\_100 value: 89.331 - type: recall\_at\_1000 value: 89.331 - type: recall\_at\_3 value: 66.643 - type: recall\_at\_5 value: 77.45400000000001 + task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v\_measure value: 51.67144081472449 + task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v\_measure value: 48.11256154264126 + task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 + task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos\_sim\_pearson value: 88.1935203751726 - type: cos\_sim\_spearman value: 86.35497970498659 - type: euclidean\_pearson value: 85.46910708503744 - type: euclidean\_spearman value: 85.13928935405485 - type: manhattan\_pearson value: 85.68373836333303 - type: manhattan\_spearman value: 85.40013867117746 + task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v\_measure value: 40.86793640310432 + task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v\_measure value: 39.80291334130727 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.421 - type: map\_at\_10 value: 52.349000000000004 - type: map\_at\_100 value: 52.349000000000004 - type: map\_at\_1000 value: 52.349000000000004 - type: map\_at\_3 value: 48.17 - type: map\_at\_5 value: 50.432 - type: mrr\_at\_1 value: 47.353 - type: mrr\_at\_10 value: 58.387 - type: mrr\_at\_100 value: 58.387 - type: mrr\_at\_1000 value: 58.387 - type: mrr\_at\_3 value: 56.199 - type: mrr\_at\_5 value: 57.487 - type: ndcg\_at\_1 value: 47.353 - type: ndcg\_at\_10 value: 59.202 - type: ndcg\_at\_100 value: 58.848 - type: ndcg\_at\_1000 value: 58.831999999999994 - type: ndcg\_at\_3 value: 54.112 - type: ndcg\_at\_5 value: 56.312 - type: precision\_at\_1 value: 47.353 - type: precision\_at\_10 value: 11.459 - type: precision\_at\_100 value: 1.146 - type: precision\_at\_1000 value: 0.11499999999999999 - type: precision\_at\_3 value: 26.133 - type: precision\_at\_5 value: 18.627 - type: recall\_at\_1 value: 38.421 - type: recall\_at\_10 value: 71.89 - type: recall\_at\_100 value: 71.89 - type: recall\_at\_1000 value: 71.89 - type: recall\_at\_3 value: 56.58 - type: recall\_at\_5 value: 63.125 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 38.025999999999996 - type: map\_at\_10 value: 50.590999999999994 - type: map\_at\_100 value: 51.99700000000001 - type: map\_at\_1000 value: 52.11599999999999 - type: map\_at\_3 value: 47.435 - type: map\_at\_5 value: 49.236000000000004 - type: mrr\_at\_1 value: 48.28 - type: mrr\_at\_10 value: 56.814 - type: mrr\_at\_100 value: 57.446 - type: mrr\_at\_1000 value: 57.476000000000006 - type: mrr\_at\_3 value: 54.958 - type: mrr\_at\_5 value: 56.084999999999994 - type: ndcg\_at\_1 value: 48.28 - type: ndcg\_at\_10 value: 56.442 - type: ndcg\_at\_100 value: 60.651999999999994 - type: ndcg\_at\_1000 value: 62.187000000000005 - type: ndcg\_at\_3 value: 52.866 - type: ndcg\_at\_5 value: 54.515 - type: precision\_at\_1 value: 48.28 - type: precision\_at\_10 value: 10.586 - type: precision\_at\_100 value: 1.6310000000000002 - type: precision\_at\_1000 value: 0.20600000000000002 - type: precision\_at\_3 value: 25.945 - type: precision\_at\_5 value: 18.076 - type: recall\_at\_1 value: 38.025999999999996 - type: recall\_at\_10 value: 66.11399999999999 - type: recall\_at\_100 value: 83.339 - type: recall\_at\_1000 value: 92.413 - type: recall\_at\_3 value: 54.493 - type: recall\_at\_5 value: 59.64699999999999 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 47.905 - type: map\_at\_10 value: 61.58 - type: map\_at\_100 value: 62.605 - type: map\_at\_1000 value: 62.637 - type: map\_at\_3 value: 58.074000000000005 - type: map\_at\_5 value: 60.260000000000005 - type: mrr\_at\_1 value: 54.42 - type: mrr\_at\_10 value: 64.847 - type: mrr\_at\_100 value: 65.403 - type: mrr\_at\_1000 value: 65.41900000000001 - type: mrr\_at\_3 value: 62.675000000000004 - type: mrr\_at\_5 value: 64.101 - type: ndcg\_at\_1 value: 54.42 - type: ndcg\_at\_10 value: 67.394 - type: ndcg\_at\_100 value: 70.846 - type: ndcg\_at\_1000 value: 71.403 - type: ndcg\_at\_3 value: 62.025 - type: ndcg\_at\_5 value: 65.032 - type: precision\_at\_1 value: 54.42 - type: precision\_at\_10 value: 10.646 - type: precision\_at\_100 value: 1.325 - type: precision\_at\_1000 value: 0.13999999999999999 - type: precision\_at\_3 value: 27.398 - type: precision\_at\_5 value: 18.796 - type: recall\_at\_1 value: 47.905 - type: recall\_at\_10 value: 80.84599999999999 - type: recall\_at\_100 value: 95.078 - type: recall\_at\_1000 value: 98.878 - type: recall\_at\_3 value: 67.05600000000001 - type: recall\_at\_5 value: 74.261 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 30.745 - type: map\_at\_10 value: 41.021 - type: map\_at\_100 value: 41.021 - type: map\_at\_1000 value: 41.021 - type: map\_at\_3 value: 37.714999999999996 - type: map\_at\_5 value: 39.766 - type: mrr\_at\_1 value: 33.559 - type: mrr\_at\_10 value: 43.537 - type: mrr\_at\_100 value: 43.537 - type: mrr\_at\_1000 value: 43.537 - type: mrr\_at\_3 value: 40.546 - type: mrr\_at\_5 value: 42.439 - type: ndcg\_at\_1 value: 33.559 - type: ndcg\_at\_10 value: 46.781 - type: ndcg\_at\_100 value: 46.781 - type: ndcg\_at\_1000 value: 46.781 - type: ndcg\_at\_3 value: 40.516000000000005 - type: ndcg\_at\_5 value: 43.957 - type: precision\_at\_1 value: 33.559 - type: precision\_at\_10 value: 7.198 - type: precision\_at\_100 value: 0.72 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 17.1 - type: precision\_at\_5 value: 12.316 - type: recall\_at\_1 value: 30.745 - type: recall\_at\_10 value: 62.038000000000004 - type: recall\_at\_100 value: 62.038000000000004 - type: recall\_at\_1000 value: 62.038000000000004 - type: recall\_at\_3 value: 45.378 - type: recall\_at\_5 value: 53.580000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 19.637999999999998 - type: map\_at\_10 value: 31.05 - type: map\_at\_100 value: 31.05 - type: map\_at\_1000 value: 31.05 - type: map\_at\_3 value: 27.628000000000004 - type: map\_at\_5 value: 29.767 - type: mrr\_at\_1 value: 25.0 - type: mrr\_at\_10 value: 36.131 - type: mrr\_at\_100 value: 36.131 - type: mrr\_at\_1000 value: 36.131 - type: mrr\_at\_3 value: 33.333 - type: mrr\_at\_5 value: 35.143 - type: ndcg\_at\_1 value: 25.0 - type: ndcg\_at\_10 value: 37.478 - type: ndcg\_at\_100 value: 37.469 - type: ndcg\_at\_1000 value: 37.469 - type: ndcg\_at\_3 value: 31.757999999999996 - type: ndcg\_at\_5 value: 34.821999999999996 - type: precision\_at\_1 value: 25.0 - type: precision\_at\_10 value: 7.188999999999999 - type: precision\_at\_100 value: 0.719 - type: precision\_at\_1000 value: 0.07200000000000001 - type: precision\_at\_3 value: 15.837000000000002 - type: precision\_at\_5 value: 11.841 - type: recall\_at\_1 value: 19.637999999999998 - type: recall\_at\_10 value: 51.836000000000006 - type: recall\_at\_100 value: 51.836000000000006 - type: recall\_at\_1000 value: 51.836000000000006 - type: recall\_at\_3 value: 36.384 - type: recall\_at\_5 value: 43.964 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 34.884 - type: map\_at\_10 value: 47.88 - type: map\_at\_100 value: 47.88 - type: map\_at\_1000 value: 47.88 - type: map\_at\_3 value: 43.85 - type: map\_at\_5 value: 46.414 - type: mrr\_at\_1 value: 43.022 - type: mrr\_at\_10 value: 53.569 - type: mrr\_at\_100 value: 53.569 - type: mrr\_at\_1000 value: 53.569 - type: mrr\_at\_3 value: 51.075 - type: mrr\_at\_5 value: 52.725 - type: ndcg\_at\_1 value: 43.022 - type: ndcg\_at\_10 value: 54.461000000000006 - type: ndcg\_at\_100 value: 54.388000000000005 - type: ndcg\_at\_1000 value: 54.388000000000005 - type: ndcg\_at\_3 value: 48.864999999999995 - type: ndcg\_at\_5 value: 52.032000000000004 - type: precision\_at\_1 value: 43.022 - type: precision\_at\_10 value: 9.885 - type: precision\_at\_100 value: 0.988 - type: precision\_at\_1000 value: 0.099 - type: precision\_at\_3 value: 23.612 - type: precision\_at\_5 value: 16.997 - type: recall\_at\_1 value: 34.884 - type: recall\_at\_10 value: 68.12899999999999 - type: recall\_at\_100 value: 68.12899999999999 - type: recall\_at\_1000 value: 68.12899999999999 - type: recall\_at\_3 value: 52.428 - type: recall\_at\_5 value: 60.662000000000006 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.588 - type: map\_at\_10 value: 43.85 - type: map\_at\_100 value: 45.317 - type: map\_at\_1000 value: 45.408 - type: map\_at\_3 value: 39.73 - type: map\_at\_5 value: 42.122 - type: mrr\_at\_1 value: 38.927 - type: mrr\_at\_10 value: 49.582 - type: mrr\_at\_100 value: 50.39 - type: mrr\_at\_1000 value: 50.426 - type: mrr\_at\_3 value: 46.518 - type: mrr\_at\_5 value: 48.271 - type: ndcg\_at\_1 value: 38.927 - type: ndcg\_at\_10 value: 50.605999999999995 - type: ndcg\_at\_100 value: 56.22200000000001 - type: ndcg\_at\_1000 value: 57.724 - type: ndcg\_at\_3 value: 44.232 - type: ndcg\_at\_5 value: 47.233999999999995 - type: precision\_at\_1 value: 38.927 - type: precision\_at\_10 value: 9.429 - type: precision\_at\_100 value: 1.435 - type: precision\_at\_1000 value: 0.172 - type: precision\_at\_3 value: 21.271 - type: precision\_at\_5 value: 15.434000000000001 - type: recall\_at\_1 value: 31.588 - type: recall\_at\_10 value: 64.836 - type: recall\_at\_100 value: 88.066 - type: recall\_at\_1000 value: 97.748 - type: recall\_at\_3 value: 47.128 - type: recall\_at\_5 value: 54.954 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.956083333333336 - type: map\_at\_10 value: 43.33483333333333 - type: map\_at\_100 value: 44.64883333333333 - type: map\_at\_1000 value: 44.75 - type: map\_at\_3 value: 39.87741666666666 - type: map\_at\_5 value: 41.86766666666667 - type: mrr\_at\_1 value: 38.06341666666667 - type: mrr\_at\_10 value: 47.839666666666666 - type: mrr\_at\_100 value: 48.644000000000005 - type: mrr\_at\_1000 value: 48.68566666666667 - type: mrr\_at\_3 value: 45.26358333333334 - type: mrr\_at\_5 value: 46.790000000000006 - type: ndcg\_at\_1 value: 38.06341666666667 - type: ndcg\_at\_10 value: 49.419333333333334 - type: ndcg\_at\_100 value: 54.50166666666667 - type: ndcg\_at\_1000 value: 56.161166666666674 - type: ndcg\_at\_3 value: 43.982416666666666 - type: ndcg\_at\_5 value: 46.638083333333334 - type: precision\_at\_1 value: 38.06341666666667 - type: precision\_at\_10 value: 8.70858333333333 - type: precision\_at\_100 value: 1.327 - type: precision\_at\_1000 value: 0.165 - type: precision\_at\_3 value: 20.37816666666667 - type: precision\_at\_5 value: 14.516333333333334 - type: recall\_at\_1 value: 31.956083333333336 - type: recall\_at\_10 value: 62.69458333333334 - type: recall\_at\_100 value: 84.46433333333334 - type: recall\_at\_1000 value: 95.58449999999999 - type: recall\_at\_3 value: 47.52016666666666 - type: recall\_at\_5 value: 54.36066666666666 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 28.912 - type: map\_at\_10 value: 38.291 - type: map\_at\_100 value: 39.44 - type: map\_at\_1000 value: 39.528 - type: map\_at\_3 value: 35.638 - type: map\_at\_5 value: 37.218 - type: mrr\_at\_1 value: 32.822 - type: mrr\_at\_10 value: 41.661 - type: mrr\_at\_100 value: 42.546 - type: mrr\_at\_1000 value: 42.603 - type: mrr\_at\_3 value: 39.238 - type: mrr\_at\_5 value: 40.726 - type: ndcg\_at\_1 value: 32.822 - type: ndcg\_at\_10 value: 43.373 - type: ndcg\_at\_100 value: 48.638 - type: ndcg\_at\_1000 value: 50.654999999999994 - type: ndcg\_at\_3 value: 38.643 - type: ndcg\_at\_5 value: 41.126000000000005 - type: precision\_at\_1 value: 32.822 - type: precision\_at\_10 value: 6.8709999999999996 - type: precision\_at\_100 value: 1.032 - type: precision\_at\_1000 value: 0.128 - type: precision\_at\_3 value: 16.82 - type: precision\_at\_5 value: 11.718 - type: recall\_at\_1 value: 28.912 - type: recall\_at\_10 value: 55.376999999999995 - type: recall\_at\_100 value: 79.066 - type: recall\_at\_1000 value: 93.664 - type: recall\_at\_3 value: 42.569 - type: recall\_at\_5 value: 48.719 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 22.181 - type: map\_at\_10 value: 31.462 - type: map\_at\_100 value: 32.73 - type: map\_at\_1000 value: 32.848 - type: map\_at\_3 value: 28.57 - type: map\_at\_5 value: 30.182 - type: mrr\_at\_1 value: 27.185 - type: mrr\_at\_10 value: 35.846000000000004 - type: mrr\_at\_100 value: 36.811 - type: mrr\_at\_1000 value: 36.873 - type: mrr\_at\_3 value: 33.437 - type: mrr\_at\_5 value: 34.813 - type: ndcg\_at\_1 value: 27.185 - type: ndcg\_at\_10 value: 36.858000000000004 - type: ndcg\_at\_100 value: 42.501 - type: ndcg\_at\_1000 value: 44.945 - type: ndcg\_at\_3 value: 32.066 - type: ndcg\_at\_5 value: 34.29 - type: precision\_at\_1 value: 27.185 - type: precision\_at\_10 value: 6.752 - type: precision\_at\_100 value: 1.111 - type: precision\_at\_1000 value: 0.151 - type: precision\_at\_3 value: 15.290000000000001 - type: precision\_at\_5 value: 11.004999999999999 - type: recall\_at\_1 value: 22.181 - type: recall\_at\_10 value: 48.513 - type: recall\_at\_100 value: 73.418 - type: recall\_at\_1000 value: 90.306 - type: recall\_at\_3 value: 35.003 - type: recall\_at\_5 value: 40.876000000000005 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 33.934999999999995 - type: map\_at\_10 value: 44.727 - type: map\_at\_100 value: 44.727 - type: map\_at\_1000 value: 44.727 - type: map\_at\_3 value: 40.918 - type: map\_at\_5 value: 42.961 - type: mrr\_at\_1 value: 39.646 - type: mrr\_at\_10 value: 48.898 - type: mrr\_at\_100 value: 48.898 - type: mrr\_at\_1000 value: 48.898 - type: mrr\_at\_3 value: 45.896 - type: mrr\_at\_5 value: 47.514 - type: ndcg\_at\_1 value: 39.646 - type: ndcg\_at\_10 value: 50.817 - type: ndcg\_at\_100 value: 50.803 - type: ndcg\_at\_1000 value: 50.803 - type: ndcg\_at\_3 value: 44.507999999999996 - type: ndcg\_at\_5 value: 47.259 - type: precision\_at\_1 value: 39.646 - type: precision\_at\_10 value: 8.759 - type: precision\_at\_100 value: 0.876 - type: precision\_at\_1000 value: 0.08800000000000001 - type: precision\_at\_3 value: 20.274 - type: precision\_at\_5 value: 14.366000000000001 - type: recall\_at\_1 value: 33.934999999999995 - type: recall\_at\_10 value: 65.037 - type: recall\_at\_100 value: 65.037 - type: recall\_at\_1000 value: 65.037 - type: recall\_at\_3 value: 47.439 - type: recall\_at\_5 value: 54.567 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 32.058 - type: map\_at\_10 value: 43.137 - type: map\_at\_100 value: 43.137 - type: map\_at\_1000 value: 43.137 - type: map\_at\_3 value: 39.882 - type: map\_at\_5 value: 41.379 - type: mrr\_at\_1 value: 38.933 - type: mrr\_at\_10 value: 48.344 - type: mrr\_at\_100 value: 48.344 - type: mrr\_at\_1000 value: 48.344 - type: mrr\_at\_3 value: 45.652 - type: mrr\_at\_5 value: 46.877 - type: ndcg\_at\_1 value: 38.933 - type: ndcg\_at\_10 value: 49.964 - type: ndcg\_at\_100 value: 49.242000000000004 - type: ndcg\_at\_1000 value: 49.222 - type: ndcg\_at\_3 value: 44.605 - type: ndcg\_at\_5 value: 46.501999999999995 - type: precision\_at\_1 value: 38.933 - type: precision\_at\_10 value: 9.427000000000001 - type: precision\_at\_100 value: 0.943 - type: precision\_at\_1000 value: 0.094 - type: precision\_at\_3 value: 20.685000000000002 - type: precision\_at\_5 value: 14.585 - type: recall\_at\_1 value: 32.058 - type: recall\_at\_10 value: 63.074 - type: recall\_at\_100 value: 63.074 - type: recall\_at\_1000 value: 63.074 - type: recall\_at\_3 value: 47.509 - type: recall\_at\_5 value: 52.455 + task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 26.029000000000003 - type: map\_at\_10 value: 34.646 - type: map\_at\_100 value: 34.646 - type: map\_at\_1000 value: 34.646 - type: map\_at\_3 value: 31.456 - type: map\_at\_5 value: 33.138 - type: mrr\_at\_1 value: 28.281 - type: mrr\_at\_10 value: 36.905 - type: mrr\_at\_100 value: 36.905 - type: mrr\_at\_1000 value: 36.905 - type: mrr\_at\_3 value: 34.011 - type: mrr\_at\_5 value: 35.638 - type: ndcg\_at\_1 value: 28.281 - type: ndcg\_at\_10 value: 40.159 - type: ndcg\_at\_100 value: 40.159 - type: ndcg\_at\_1000 value: 40.159 - type: ndcg\_at\_3 value: 33.995 - type: ndcg\_at\_5 value: 36.836999999999996 - type: precision\_at\_1 value: 28.281 - type: precision\_at\_10 value: 6.358999999999999 - type: precision\_at\_100 value: 0.636 - type: precision\_at\_1000 value: 0.064 - type: precision\_at\_3 value: 14.233 - type: precision\_at\_5 value: 10.314 - type: recall\_at\_1 value: 26.029000000000003 - type: recall\_at\_10 value: 55.08 - type: recall\_at\_100 value: 55.08 - type: recall\_at\_1000 value: 55.08 - type: recall\_at\_3 value: 38.487 - type: recall\_at\_5 value: 45.308 + task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 12.842999999999998 - type: map\_at\_10 value: 22.101000000000003 - type: map\_at\_100 value: 24.319 - type: map\_at\_1000 value: 24.51 - type: map\_at\_3 value: 18.372 - type: map\_at\_5 value: 20.323 - type: mrr\_at\_1 value: 27.948 - type: mrr\_at\_10 value: 40.321 - type: mrr\_at\_100 value: 41.262 - type: mrr\_at\_1000 value: 41.297 - type: mrr\_at\_3 value: 36.558 - type: mrr\_at\_5 value: 38.824999999999996 - type: ndcg\_at\_1 value: 27.948 - type: ndcg\_at\_10 value: 30.906 - type: ndcg\_at\_100 value: 38.986 - type: ndcg\_at\_1000 value: 42.136 - type: ndcg\_at\_3 value: 24.911 - type: ndcg\_at\_5 value: 27.168999999999997 - type: precision\_at\_1 value: 27.948 - type: precision\_at\_10 value: 9.798 - type: precision\_at\_100 value: 1.8399999999999999 - type: precision\_at\_1000 value: 0.243 - type: precision\_at\_3 value: 18.328 - type: precision\_at\_5 value: 14.502 - type: recall\_at\_1 value: 12.842999999999998 - type: recall\_at\_10 value: 37.245 - type: recall\_at\_100 value: 64.769 - type: recall\_at\_1000 value: 82.055 - type: recall\_at\_3 value: 23.159 - type: recall\_at\_5 value: 29.113 + task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map\_at\_1 value: 8.934000000000001 - type: map\_at\_10 value: 21.915000000000003 - type: map\_at\_100 value: 21.915000000000003 - type: map\_at\_1000 value: 21.915000000000003 - type: map\_at\_3 value: 14.623 - type: map\_at\_5 value: 17.841 - type: mrr\_at\_1 value: 71.25 - type: mrr\_at\_10 value: 78.994 - type: mrr\_at\_100 value: 78.994 - type: mrr\_at\_1000 value: 78.994 - type: mrr\_at\_3 value: 77.208 - type: mrr\_at\_5 value: 78.55799999999999 - type: ndcg\_at\_1 value: 60.62499999999999 - type: ndcg\_at\_10 value: 46.604 - type: ndcg\_at\_100 value: 35.653 - type: ndcg\_at\_1000 value: 35.531 - type: ndcg\_at\_3 value: 50.605 - type: ndcg\_at\_5 value: 48.730000000000004 - type: precision\_at\_1 value: 71.25 - type: precision\_at\_10 value: 37.75 - type: precision\_at\_100 value: 3.775 - type: precision\_at\_1000 value: 0.377 - type: precision\_at\_3 value: 54.417 - type: precision\_at\_5 value: 48.15 - type: recall\_at\_1 value: 8.934000000000001 - type: recall\_at\_10 value: 28.471000000000004 - type: recall\_at\_100 value: 28.471000000000004 - type: recall\_at\_1000 value: 28.471000000000004 - type: recall\_at\_3 value: 16.019 - type: recall\_at\_5 value: 21.410999999999998 + task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 + task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map\_at\_1 value: 66.81899999999999 - type: map\_at\_10 value: 78.034 - type: map\_at\_100 value: 78.034 - type: map\_at\_1000 value: 78.034 - type: map\_at\_3 value: 76.43100000000001 - type: map\_at\_5 value: 77.515 - type: mrr\_at\_1 value: 71.542 - type: mrr\_at\_10 value: 81.638 - type: mrr\_at\_100 value: 81.638 - type: mrr\_at\_1000 value: 81.638 - type: mrr\_at\_3 value: 80.403 - type: mrr\_at\_5 value: 81.256 - type: ndcg\_at\_1 value: 71.542 - type: ndcg\_at\_10 value: 82.742 - type: ndcg\_at\_100 value: 82.741 - type: ndcg\_at\_1000 value: 82.741 - type: ndcg\_at\_3 value: 80.039 - type: ndcg\_at\_5 value: 81.695 - type: precision\_at\_1 value: 71.542 - type: precision\_at\_10 value: 10.387 - type: precision\_at\_100 value: 1.039 - type: precision\_at\_1000 value: 0.104 - type: precision\_at\_3 value: 31.447999999999997 - type: precision\_at\_5 value: 19.91 - type: recall\_at\_1 value: 66.81899999999999 - type: recall\_at\_10 value: 93.372 - type: recall\_at\_100 value: 93.372 - type: recall\_at\_1000 value: 93.372 - type: recall\_at\_3 value: 86.33 - type: recall\_at\_5 value: 90.347 + task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map\_at\_1 value: 31.158 - type: map\_at\_10 value: 52.017 - type: map\_at\_100 value: 54.259 - type: map\_at\_1000 value: 54.367 - type: map\_at\_3 value: 45.738 - type: map\_at\_5 value: 49.283 - type: mrr\_at\_1 value: 57.87 - type: mrr\_at\_10 value: 66.215 - type: mrr\_at\_100 value: 66.735 - type: mrr\_at\_1000 value: 66.75 - type: mrr\_at\_3 value: 64.043 - type: mrr\_at\_5 value: 65.116 - type: ndcg\_at\_1 value: 57.87 - type: ndcg\_at\_10 value: 59.946999999999996 - type: ndcg\_at\_100 value: 66.31099999999999 - type: ndcg\_at\_1000 value: 67.75999999999999 - type: ndcg\_at\_3 value: 55.483000000000004 - type: ndcg\_at\_5 value: 56.891000000000005 - type: precision\_at\_1 value: 57.87 - type: precision\_at\_10 value: 16.497 - type: precision\_at\_100 value: 2.321 - type: precision\_at\_1000 value: 0.258 - type: precision\_at\_3 value: 37.14 - type: precision\_at\_5 value: 27.067999999999998 - type: recall\_at\_1 value: 31.158 - type: recall\_at\_10 value: 67.381 - type: recall\_at\_100 value: 89.464 - type: recall\_at\_1000 value: 97.989 - type: recall\_at\_3 value: 50.553000000000004 - type: recall\_at\_5 value: 57.824 + task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map\_at\_1 value: 42.073 - type: map\_at\_10 value: 72.418 - type: map\_at\_100 value: 73.175 - type: map\_at\_1000 value: 73.215 - type: map\_at\_3 value: 68.791 - type: map\_at\_5 value: 71.19 - type: mrr\_at\_1 value: 84.146 - type: mrr\_at\_10 value: 88.994 - type: mrr\_at\_100 value: 89.116 - type: mrr\_at\_1000 value: 89.12 - type: mrr\_at\_3 value: 88.373 - type: mrr\_at\_5 value: 88.82 - type: ndcg\_at\_1 value: 84.146 - type: ndcg\_at\_10 value: 79.404 - type: ndcg\_at\_100 value: 81.83200000000001 - type: ndcg\_at\_1000 value: 82.524 - type: ndcg\_at\_3 value: 74.595 - type: ndcg\_at\_5 value: 77.474 - type: precision\_at\_1 value: 84.146 - type: precision\_at\_10 value: 16.753999999999998 - type: precision\_at\_100 value: 1.8599999999999999 - type: precision\_at\_1000 value: 0.19499999999999998 - type: precision\_at\_3 value: 48.854 - type: precision\_at\_5 value: 31.579 - type: recall\_at\_1 value: 42.073 - type: recall\_at\_10 value: 83.768 - type: recall\_at\_100 value: 93.018 - type: recall\_at\_1000 value: 97.481 - type: recall\_at\_3 value: 73.282 - type: recall\_at\_5 value: 78.947 + task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 + task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map\_at\_1 value: 21.698 - type: map\_at\_10 value: 34.585 - type: map\_at\_100 value: 35.782000000000004 - type: map\_at\_1000 value: 35.825 - type: map\_at\_3 value: 30.397999999999996 - type: map\_at\_5 value: 32.72 - type: mrr\_at\_1 value: 22.192 - type: mrr\_at\_10 value: 35.085 - type: mrr\_at\_100 value: 36.218 - type: mrr\_at\_1000 value: 36.256 - type: mrr\_at\_3 value: 30.986000000000004 - type: mrr\_at\_5 value: 33.268 - type: ndcg\_at\_1 value: 22.192 - type: ndcg\_at\_10 value: 41.957 - type: ndcg\_at\_100 value: 47.658 - type: ndcg\_at\_1000 value: 48.697 - type: ndcg\_at\_3 value: 33.433 - type: ndcg\_at\_5 value: 37.551 - type: precision\_at\_1 value: 22.192 - type: precision\_at\_10 value: 6.781 - type: precision\_at\_100 value: 0.963 - type: precision\_at\_1000 value: 0.105 - type: precision\_at\_3 value: 14.365 - type: precision\_at\_5 value: 10.713000000000001 - type: recall\_at\_1 value: 21.698 - type: recall\_at\_10 value: 64.79 - type: recall\_at\_100 value: 91.071 - type: recall\_at\_1000 value: 98.883 - type: recall\_at\_3 value: 41.611 - type: recall\_at\_5 value: 51.459999999999994 + task: type: Classification dataset: type: mteb/mtop\_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 + task: type: Classification dataset: type: mteb/mtop\_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 + task: type: Classification dataset: type: mteb/amazon\_massive\_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 + task: type: Classification dataset: type: mteb/amazon\_massive\_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v\_measure value: 36.52153488185864 + task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v\_measure value: 36.80090398444147 + task: type: Reranking dataset: type: mteb/mind\_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 + task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map\_at\_1 value: 6.920999999999999 - type: map\_at\_10 value: 16.049 - type: map\_at\_100 value: 16.049 - type: map\_at\_1000 value: 16.049 - type: map\_at\_3 value: 11.865 - type: map\_at\_5 value: 13.657 - type: mrr\_at\_1 value: 53.87 - type: mrr\_at\_10 value: 62.291 - type: mrr\_at\_100 value: 62.291 - type: mrr\_at\_1000 value: 62.291 - type: mrr\_at\_3 value: 60.681 - type: mrr\_at\_5 value: 61.61 - type: ndcg\_at\_1 value: 51.23799999999999 - type: ndcg\_at\_10 value: 40.892 - type: ndcg\_at\_100 value: 26.951999999999998 - type: ndcg\_at\_1000 value: 26.474999999999998 - type: ndcg\_at\_3 value: 46.821 - type: ndcg\_at\_5 value: 44.333 - type: precision\_at\_1 value: 53.251000000000005 - type: precision\_at\_10 value: 30.124000000000002 - type: precision\_at\_100 value: 3.012 - type: precision\_at\_1000 value: 0.301 - type: precision\_at\_3 value: 43.55 - type: precision\_at\_5 value: 38.266 - type: recall\_at\_1 value: 6.920999999999999 - type: recall\_at\_10 value: 20.852 - type: recall\_at\_100 value: 20.852 - type: recall\_at\_1000 value: 20.852 - type: recall\_at\_3 value: 13.628000000000002 - type: recall\_at\_5 value: 16.273 + task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map\_at\_1 value: 46.827999999999996 - type: map\_at\_10 value: 63.434000000000005 - type: map\_at\_100 value: 63.434000000000005 - type: map\_at\_1000 value: 63.434000000000005 - type: map\_at\_3 value: 59.794000000000004 - type: map\_at\_5 value: 62.08 - type: mrr\_at\_1 value: 52.288999999999994 - type: mrr\_at\_10 value: 65.95 - type: mrr\_at\_100 value: 65.95 - type: mrr\_at\_1000 value: 65.95 - type: mrr\_at\_3 value: 63.413 - type: mrr\_at\_5 value: 65.08 - type: ndcg\_at\_1 value: 52.288999999999994 - type: ndcg\_at\_10 value: 70.301 - type: ndcg\_at\_100 value: 70.301 - type: ndcg\_at\_1000 value: 70.301 - type: ndcg\_at\_3 value: 63.979 - type: ndcg\_at\_5 value: 67.582 - type: precision\_at\_1 value: 52.288999999999994 - type: precision\_at\_10 value: 10.576 - type: precision\_at\_100 value: 1.058 - type: precision\_at\_1000 value: 0.106 - type: precision\_at\_3 value: 28.177000000000003 - type: precision\_at\_5 value: 19.073 - type: recall\_at\_1 value: 46.827999999999996 - type: recall\_at\_10 value: 88.236 - type: recall\_at\_100 value: 88.236 - type: recall\_at\_1000 value: 88.236 - type: recall\_at\_3 value: 72.371 - type: recall\_at\_5 value: 80.56 + task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map\_at\_1 value: 71.652 - type: map\_at\_10 value: 85.953 - type: map\_at\_100 value: 85.953 - type: map\_at\_1000 value: 85.953 - type: map\_at\_3 value: 83.05399999999999 - type: map\_at\_5 value: 84.89 - type: mrr\_at\_1 value: 82.42 - type: mrr\_at\_10 value: 88.473 - type: mrr\_at\_100 value: 88.473 - type: mrr\_at\_1000 value: 88.473 - type: mrr\_at\_3 value: 87.592 - type: mrr\_at\_5 value: 88.211 - type: ndcg\_at\_1 value: 82.44 - type: ndcg\_at\_10 value: 89.467 - type: ndcg\_at\_100 value: 89.33 - type: ndcg\_at\_1000 value: 89.33 - type: ndcg\_at\_3 value: 86.822 - type: ndcg\_at\_5 value: 88.307 - type: precision\_at\_1 value: 82.44 - type: precision\_at\_10 value: 13.616 - type: precision\_at\_100 value: 1.362 - type: precision\_at\_1000 value: 0.136 - type: precision\_at\_3 value: 38.117000000000004 - type: precision\_at\_5 value: 25.05 - type: recall\_at\_1 value: 71.652 - type: recall\_at\_10 value: 96.224 - type: recall\_at\_100 value: 96.224 - type: recall\_at\_1000 value: 96.224 - type: recall\_at\_3 value: 88.571 - type: recall\_at\_5 value: 92.812 + task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v\_measure value: 61.295010338050474 + task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v\_measure value: 67.26380819328142 + task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map\_at\_1 value: 5.683 - type: map\_at\_10 value: 14.924999999999999 - type: map\_at\_100 value: 17.532 - type: map\_at\_1000 value: 17.875 - type: map\_at\_3 value: 10.392 - type: map\_at\_5 value: 12.592 - type: mrr\_at\_1 value: 28.000000000000004 - type: mrr\_at\_10 value: 39.951 - type: mrr\_at\_100 value: 41.025 - type: mrr\_at\_1000 value: 41.056 - type: mrr\_at\_3 value: 36.317 - type: mrr\_at\_5 value: 38.412 - type: ndcg\_at\_1 value: 28.000000000000004 - type: ndcg\_at\_10 value: 24.410999999999998 - type: ndcg\_at\_100 value: 33.79 - type: ndcg\_at\_1000 value: 39.035 - type: ndcg\_at\_3 value: 22.845 - type: ndcg\_at\_5 value: 20.080000000000002 - type: precision\_at\_1 value: 28.000000000000004 - type: precision\_at\_10 value: 12.790000000000001 - type: precision\_at\_100 value: 2.633 - type: precision\_at\_1000 value: 0.388 - type: precision\_at\_3 value: 21.367 - type: precision\_at\_5 value: 17.7 - type: recall\_at\_1 value: 5.683 - type: recall\_at\_10 value: 25.91 - type: recall\_at\_100 value: 53.443 - type: recall\_at\_1000 value: 78.73 - type: recall\_at\_3 value: 13.003 - type: recall\_at\_5 value: 17.932000000000002 + task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos\_sim\_pearson value: 84.677978681023 - type: cos\_sim\_spearman value: 83.13093441058189 - type: euclidean\_pearson value: 83.35535759341572 - type: euclidean\_spearman value: 83.42583744219611 - type: manhattan\_pearson value: 83.2243124045889 - type: manhattan\_spearman value: 83.39801618652632 + task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos\_sim\_pearson value: 81.68960206569666 - type: cos\_sim\_spearman value: 77.3368966488535 - type: euclidean\_pearson value: 77.62828980560303 - type: euclidean\_spearman value: 76.77951481444651 - type: manhattan\_pearson value: 77.88637240839041 - 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type: cos\_sim\_pearson value: 87.82422928098886 - type: cos\_sim\_spearman value: 88.12660311894628 - type: euclidean\_pearson value: 87.50974805056555 - type: euclidean\_spearman value: 87.91957275596677 - type: manhattan\_pearson value: 87.74119404878883 - type: manhattan\_spearman value: 88.2808922165719 + task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos\_sim\_pearson value: 84.80605838552093 - type: cos\_sim\_spearman value: 86.24123388765678 - type: euclidean\_pearson value: 85.32648347339814 - type: euclidean\_spearman value: 85.60046671950158 - type: manhattan\_pearson value: 85.53800168487811 - type: manhattan\_spearman value: 85.89542420480763 + task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos\_sim\_pearson value: 89.87540978988132 - 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type: manhattan\_ap value: 80.95751834536561 - type: manhattan\_f1 value: 73.9119496855346 - type: manhattan\_precision value: 70.625 - type: manhattan\_recall value: 77.5197889182058 - type: max\_accuracy value: 88.37098408535495 - type: max\_ap value: 81.12515230092113 - type: max\_f1 value: 74.10338225909379 + task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos\_sim\_accuracy value: 89.79896767182831 - type: cos\_sim\_ap value: 87.40071784061065 - type: cos\_sim\_f1 value: 79.87753144712087 - type: cos\_sim\_precision value: 76.67304015296367 - type: cos\_sim\_recall value: 83.3615645210964 - type: dot\_accuracy value: 88.95486474948578 - type: dot\_ap value: 86.00227979119943 - type: dot\_f1 value: 78.54601474525914 - type: dot\_precision value: 75.00525394045535 - type: dot\_recall value: 82.43763473975977 - type: euclidean\_accuracy value: 89.7892653393876 - type: euclidean\_ap value: 87.42174706480819 - type: euclidean\_f1 value: 80.07283321194465 - type: euclidean\_precision value: 75.96738529574351 - type: euclidean\_recall value: 84.6473668001232 - type: manhattan\_accuracy value: 89.8474793340319 - type: manhattan\_ap value: 87.47814292587448 - type: manhattan\_f1 value: 80.15461150280949 - type: manhattan\_precision value: 74.88798234468 - type: manhattan\_recall value: 86.21804742839544 - type: max\_accuracy value: 89.8474793340319 - type: max\_ap value: 87.47814292587448 - type: max\_f1 value: 80.15461150280949 --- Model Summary ============= > > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. > > > * Repository: ContextualAI/gritlm * Paper: URL * Logs: URL * Script: URL Use === The model usage is documented here.

multiple-choice

transformers

<!-- 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. --> # fine_tuned_copa_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0295 - Accuracy: 0.54 - F1: 0.5407 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7066 | 1.0 | 50 | 0.6907 | 0.54 | 0.5411 | | 0.6897 | 2.0 | 100 | 0.6880 | 0.57 | 0.5709 | | 0.6001 | 3.0 | 150 | 0.7025 | 0.55 | 0.5511 | | 0.4629 | 4.0 | 200 | 0.7810 | 0.53 | 0.5310 | | 0.3402 | 5.0 | 250 | 1.0003 | 0.55 | 0.5511 | | 0.2299 | 6.0 | 300 | 1.0220 | 0.55 | 0.5511 | | 0.1874 | 7.0 | 350 | 0.9956 | 0.56 | 0.5611 | | 0.1133 | 8.0 | 400 | 1.0295 | 0.54 | 0.5407 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "fine_tuned_copa_bert", "results": []}]}

lenatr99/fine_tuned_copa_bert

2024-05-03T17:18:56+00:00

TAGS #transformers #safetensors #bert #multiple-choice #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us

fine\_tuned\_copa\_bert ======================= This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.0295 * Accuracy: 0.54 * F1: 0.5407 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: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

text-generation

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/r2igr19

2024-05-03T17:20:47+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # cosmosDPO_CodeTest This model is a fine-tuned version of [ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5271 - Rewards/chosen: -2.6242 - Rewards/rejected: -6.3552 - Rewards/accuracies: 0.2667 - Rewards/margins: 3.7309 - Logps/rejected: -749.125 - Logps/chosen: -350.9360 - Logits/rejected: -5.2606 - Logits/chosen: -4.5085 ## 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: 5e-06 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5159 | 1.3072 | 100 | 0.5241 | -0.9182 | -3.2061 | 0.2676 | 2.2879 | -434.2115 | -180.3287 | -4.0572 | -3.5729 | | 0.5227 | 2.6144 | 200 | 0.5217 | -2.1076 | -5.3791 | 0.2695 | 3.2715 | -651.5153 | -299.2687 | -4.8098 | -4.1931 | | 0.4937 | 3.9216 | 300 | 0.5271 | -2.6242 | -6.3552 | 0.2667 | 3.7309 | -749.125 | -350.9360 | -5.2606 | -4.5085 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "model-index": [{"name": "cosmosDPO_v0.1", "results": []}]}

meguzn/cosmosDPO_v0.1

2024-05-03T17:21:46+00:00

TAGS #peft #tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1 #license-mit #region-us

cosmosDPO\_CodeTest =================== This model is a fine-tuned version of ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5271 * Rewards/chosen: -2.6242 * Rewards/rejected: -6.3552 * Rewards/accuracies: 0.2667 * Rewards/margins: 3.7309 * Logps/rejected: -749.125 * Logps/chosen: -350.9360 * Logits/rejected: -5.2606 * Logits/chosen: -4.5085 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: 5e-06 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.10.0 * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4181 - F1 Score: 0.8048 - Accuracy: 0.8049 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5559 | 0.54 | 200 | 0.4704 | 0.7803 | 0.7804 | | 0.4687 | 1.08 | 400 | 0.4646 | 0.7875 | 0.7878 | | 0.4525 | 1.62 | 600 | 0.4497 | 0.7929 | 0.7931 | | 0.4437 | 2.16 | 800 | 0.4473 | 0.7944 | 0.7944 | | 0.4405 | 2.7 | 1000 | 0.4449 | 0.7921 | 0.7922 | | 0.4363 | 3.24 | 1200 | 0.4399 | 0.7961 | 0.7963 | | 0.4331 | 3.78 | 1400 | 0.4419 | 0.7909 | 0.7912 | | 0.4313 | 4.32 | 1600 | 0.4438 | 0.7967 | 0.7968 | | 0.4309 | 4.86 | 1800 | 0.4419 | 0.7937 | 0.7941 | | 0.4266 | 5.41 | 2000 | 0.4388 | 0.7927 | 0.7929 | | 0.4243 | 5.95 | 2200 | 0.4391 | 0.7981 | 0.7981 | | 0.4279 | 6.49 | 2400 | 0.4341 | 0.7965 | 0.7965 | | 0.4206 | 7.03 | 2600 | 0.4416 | 0.7977 | 0.7981 | | 0.4231 | 7.57 | 2800 | 0.4348 | 0.7976 | 0.7976 | | 0.4171 | 8.11 | 3000 | 0.4362 | 0.7944 | 0.7946 | | 0.419 | 8.65 | 3200 | 0.4297 | 0.8017 | 0.8017 | | 0.4207 | 9.19 | 3400 | 0.4331 | 0.7992 | 0.7992 | | 0.418 | 9.73 | 3600 | 0.4378 | 0.7949 | 0.7954 | | 0.4182 | 10.27 | 3800 | 0.4330 | 0.7982 | 0.7983 | | 0.4164 | 10.81 | 4000 | 0.4360 | 0.7977 | 0.7978 | | 0.414 | 11.35 | 4200 | 0.4330 | 0.7973 | 0.7975 | | 0.4143 | 11.89 | 4400 | 0.4336 | 0.7964 | 0.7966 | | 0.4115 | 12.43 | 4600 | 0.4335 | 0.8025 | 0.8025 | | 0.4108 | 12.97 | 4800 | 0.4331 | 0.7990 | 0.7992 | | 0.4133 | 13.51 | 5000 | 0.4407 | 0.7934 | 0.7943 | | 0.4114 | 14.05 | 5200 | 0.4303 | 0.8029 | 0.8029 | | 0.4085 | 14.59 | 5400 | 0.4288 | 0.8022 | 0.8022 | | 0.4081 | 15.14 | 5600 | 0.4326 | 0.8021 | 0.8022 | | 0.4096 | 15.68 | 5800 | 0.4334 | 0.7985 | 0.7988 | | 0.4037 | 16.22 | 6000 | 0.4312 | 0.8023 | 0.8025 | | 0.4114 | 16.76 | 6200 | 0.4254 | 0.8015 | 0.8015 | | 0.4119 | 17.3 | 6400 | 0.4278 | 0.8046 | 0.8047 | | 0.4072 | 17.84 | 6600 | 0.4294 | 0.8014 | 0.8015 | | 0.4035 | 18.38 | 6800 | 0.4337 | 0.7972 | 0.7978 | | 0.4047 | 18.92 | 7000 | 0.4277 | 0.8021 | 0.8022 | | 0.4011 | 19.46 | 7200 | 0.4286 | 0.8035 | 0.8035 | | 0.4118 | 20.0 | 7400 | 0.4264 | 0.8045 | 0.8046 | | 0.4066 | 20.54 | 7600 | 0.4286 | 0.8025 | 0.8027 | | 0.4031 | 21.08 | 7800 | 0.4275 | 0.8038 | 0.8039 | | 0.4044 | 21.62 | 8000 | 0.4255 | 0.8037 | 0.8037 | | 0.402 | 22.16 | 8200 | 0.4259 | 0.8040 | 0.8041 | | 0.4101 | 22.7 | 8400 | 0.4265 | 0.8027 | 0.8029 | | 0.4006 | 23.24 | 8600 | 0.4249 | 0.8047 | 0.8047 | | 0.4005 | 23.78 | 8800 | 0.4271 | 0.8038 | 0.8039 | | 0.3983 | 24.32 | 9000 | 0.4269 | 0.8045 | 0.8046 | | 0.4017 | 24.86 | 9200 | 0.4259 | 0.8038 | 0.8039 | | 0.4117 | 25.41 | 9400 | 0.4257 | 0.8043 | 0.8044 | | 0.3956 | 25.95 | 9600 | 0.4271 | 0.8048 | 0.8049 | | 0.4029 | 26.49 | 9800 | 0.4272 | 0.8050 | 0.8051 | | 0.4004 | 27.03 | 10000 | 0.4271 | 0.8046 | 0.8047 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f

2024-05-03T17:22:07+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_15M-L8\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.4181 * F1 Score: 0.8048 * Accuracy: 0.8049 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3823 - F1 Score: 0.8291 - Accuracy: 0.8291 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5574 | 0.6 | 200 | 0.4140 | 0.8126 | 0.8127 | | 0.4313 | 1.2 | 400 | 0.3888 | 0.8259 | 0.8259 | | 0.4159 | 1.81 | 600 | 0.3816 | 0.8268 | 0.8268 | | 0.4102 | 2.41 | 800 | 0.3759 | 0.8310 | 0.8310 | | 0.4006 | 3.01 | 1000 | 0.3744 | 0.8299 | 0.8298 | | 0.3973 | 3.61 | 1200 | 0.3701 | 0.8379 | 0.8379 | | 0.3992 | 4.22 | 1400 | 0.3703 | 0.8355 | 0.8355 | | 0.396 | 4.82 | 1600 | 0.3687 | 0.8381 | 0.8381 | | 0.3877 | 5.42 | 1800 | 0.3757 | 0.8292 | 0.8293 | | 0.3935 | 6.02 | 2000 | 0.3690 | 0.8383 | 0.8383 | | 0.3911 | 6.63 | 2200 | 0.3674 | 0.8381 | 0.8381 | | 0.3879 | 7.23 | 2400 | 0.3679 | 0.8380 | 0.8381 | | 0.3887 | 7.83 | 2600 | 0.3662 | 0.8396 | 0.8396 | | 0.3825 | 8.43 | 2800 | 0.3721 | 0.8344 | 0.8349 | | 0.3879 | 9.04 | 3000 | 0.3663 | 0.8402 | 0.8402 | | 0.3812 | 9.64 | 3200 | 0.3637 | 0.8397 | 0.8396 | | 0.3823 | 10.24 | 3400 | 0.3647 | 0.8406 | 0.8406 | | 0.383 | 10.84 | 3600 | 0.3643 | 0.8400 | 0.8400 | | 0.3815 | 11.45 | 3800 | 0.3640 | 0.8382 | 0.8381 | | 0.3804 | 12.05 | 4000 | 0.3629 | 0.8381 | 0.8381 | | 0.3746 | 12.65 | 4200 | 0.3634 | 0.8382 | 0.8383 | | 0.3799 | 13.25 | 4400 | 0.3635 | 0.8376 | 0.8376 | | 0.378 | 13.86 | 4600 | 0.3636 | 0.8400 | 0.8400 | | 0.3771 | 14.46 | 4800 | 0.3633 | 0.8415 | 0.8415 | | 0.3741 | 15.06 | 5000 | 0.3615 | 0.8415 | 0.8415 | | 0.371 | 15.66 | 5200 | 0.3612 | 0.8412 | 0.8412 | | 0.3728 | 16.27 | 5400 | 0.3642 | 0.8400 | 0.8400 | | 0.3718 | 16.87 | 5600 | 0.3679 | 0.8361 | 0.8364 | | 0.3698 | 17.47 | 5800 | 0.3664 | 0.8369 | 0.8372 | | 0.3758 | 18.07 | 6000 | 0.3624 | 0.8393 | 0.8395 | | 0.3725 | 18.67 | 6200 | 0.3605 | 0.8412 | 0.8413 | | 0.3716 | 19.28 | 6400 | 0.3618 | 0.8408 | 0.8408 | | 0.3703 | 19.88 | 6600 | 0.3613 | 0.8388 | 0.8389 | | 0.3658 | 20.48 | 6800 | 0.3606 | 0.8409 | 0.8410 | | 0.3759 | 21.08 | 7000 | 0.3640 | 0.8363 | 0.8366 | | 0.3748 | 21.69 | 7200 | 0.3612 | 0.8415 | 0.8415 | | 0.3651 | 22.29 | 7400 | 0.3610 | 0.8399 | 0.8400 | | 0.3673 | 22.89 | 7600 | 0.3609 | 0.8424 | 0.8425 | | 0.3681 | 23.49 | 7800 | 0.3622 | 0.8380 | 0.8381 | | 0.3688 | 24.1 | 8000 | 0.3629 | 0.8393 | 0.8395 | | 0.3692 | 24.7 | 8200 | 0.3639 | 0.8388 | 0.8391 | | 0.3645 | 25.3 | 8400 | 0.3642 | 0.8396 | 0.8398 | | 0.3692 | 25.9 | 8600 | 0.3609 | 0.8422 | 0.8423 | | 0.3687 | 26.51 | 8800 | 0.3615 | 0.8415 | 0.8415 | | 0.3671 | 27.11 | 9000 | 0.3610 | 0.8409 | 0.8410 | | 0.3726 | 27.71 | 9200 | 0.3617 | 0.8399 | 0.8400 | | 0.3626 | 28.31 | 9400 | 0.3631 | 0.8387 | 0.8389 | | 0.3658 | 28.92 | 9600 | 0.3618 | 0.8396 | 0.8396 | | 0.3724 | 29.52 | 9800 | 0.3614 | 0.8392 | 0.8393 | | 0.3612 | 30.12 | 10000 | 0.3615 | 0.8395 | 0.8396 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f

2024-05-03T17:22:07+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_15M-L8\_f ============================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3823 * F1 Score: 0.8291 * Accuracy: 0.8291 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

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

{"library_name": "transformers", "tags": []}

hrangel/Mistral_7B_qlora_CoT_Matematicals

2024-05-03T17:23:34+00:00

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - F1 Score: 0.8233 - Accuracy: 0.8233 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6131 | 0.6 | 200 | 0.4693 | 0.7777 | 0.7777 | | 0.4721 | 1.2 | 400 | 0.4169 | 0.8106 | 0.8106 | | 0.4443 | 1.81 | 600 | 0.4046 | 0.8150 | 0.8150 | | 0.4403 | 2.41 | 800 | 0.3968 | 0.8210 | 0.8210 | | 0.4255 | 3.01 | 1000 | 0.3953 | 0.8213 | 0.8214 | | 0.4233 | 3.61 | 1200 | 0.3889 | 0.8217 | 0.8217 | | 0.4223 | 4.22 | 1400 | 0.3869 | 0.8225 | 0.8225 | | 0.4197 | 4.82 | 1600 | 0.3844 | 0.8223 | 0.8223 | | 0.4106 | 5.42 | 1800 | 0.3869 | 0.8248 | 0.8249 | | 0.4124 | 6.02 | 2000 | 0.3819 | 0.8262 | 0.8263 | | 0.4112 | 6.63 | 2200 | 0.3791 | 0.8285 | 0.8285 | | 0.407 | 7.23 | 2400 | 0.3801 | 0.8313 | 0.8314 | | 0.4063 | 7.83 | 2600 | 0.3787 | 0.8288 | 0.8289 | | 0.4012 | 8.43 | 2800 | 0.3808 | 0.8296 | 0.8298 | | 0.406 | 9.04 | 3000 | 0.3761 | 0.8322 | 0.8323 | | 0.3994 | 9.64 | 3200 | 0.3734 | 0.8312 | 0.8312 | | 0.4003 | 10.24 | 3400 | 0.3750 | 0.8323 | 0.8323 | | 0.4008 | 10.84 | 3600 | 0.3741 | 0.8336 | 0.8336 | | 0.3994 | 11.45 | 3800 | 0.3736 | 0.8327 | 0.8327 | | 0.3982 | 12.05 | 4000 | 0.3729 | 0.8340 | 0.8340 | | 0.3933 | 12.65 | 4200 | 0.3739 | 0.8342 | 0.8342 | | 0.3995 | 13.25 | 4400 | 0.3707 | 0.8349 | 0.8349 | | 0.3967 | 13.86 | 4600 | 0.3721 | 0.8355 | 0.8355 | | 0.3951 | 14.46 | 4800 | 0.3723 | 0.8351 | 0.8351 | | 0.3916 | 15.06 | 5000 | 0.3705 | 0.8336 | 0.8336 | | 0.3907 | 15.66 | 5200 | 0.3703 | 0.8376 | 0.8376 | | 0.3905 | 16.27 | 5400 | 0.3728 | 0.8355 | 0.8355 | | 0.3917 | 16.87 | 5600 | 0.3738 | 0.8364 | 0.8366 | | 0.39 | 17.47 | 5800 | 0.3720 | 0.8365 | 0.8366 | | 0.3961 | 18.07 | 6000 | 0.3706 | 0.8377 | 0.8378 | | 0.3917 | 18.67 | 6200 | 0.3694 | 0.8379 | 0.8379 | | 0.3923 | 19.28 | 6400 | 0.3711 | 0.8374 | 0.8374 | | 0.389 | 19.88 | 6600 | 0.3690 | 0.8377 | 0.8378 | | 0.3847 | 20.48 | 6800 | 0.3701 | 0.8371 | 0.8372 | | 0.3949 | 21.08 | 7000 | 0.3710 | 0.8359 | 0.8361 | | 0.3961 | 21.69 | 7200 | 0.3680 | 0.8379 | 0.8379 | | 0.386 | 22.29 | 7400 | 0.3684 | 0.8393 | 0.8393 | | 0.387 | 22.89 | 7600 | 0.3698 | 0.8378 | 0.8378 | | 0.388 | 23.49 | 7800 | 0.3683 | 0.8391 | 0.8391 | | 0.3887 | 24.1 | 8000 | 0.3689 | 0.8381 | 0.8381 | | 0.3889 | 24.7 | 8200 | 0.3693 | 0.8360 | 0.8361 | | 0.3844 | 25.3 | 8400 | 0.3699 | 0.8389 | 0.8389 | | 0.3902 | 25.9 | 8600 | 0.3678 | 0.8398 | 0.8398 | | 0.3906 | 26.51 | 8800 | 0.3681 | 0.8383 | 0.8383 | | 0.3874 | 27.11 | 9000 | 0.3682 | 0.8389 | 0.8389 | | 0.3929 | 27.71 | 9200 | 0.3682 | 0.8393 | 0.8393 | | 0.3847 | 28.31 | 9400 | 0.3689 | 0.8396 | 0.8396 | | 0.3874 | 28.92 | 9600 | 0.3684 | 0.8393 | 0.8393 | | 0.3929 | 29.52 | 9800 | 0.3680 | 0.8391 | 0.8391 | | 0.3819 | 30.12 | 10000 | 0.3682 | 0.8391 | 0.8391 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f

2024-05-03T17:24:03+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_15M-L1\_f ============================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3910 * F1 Score: 0.8233 * Accuracy: 0.8233 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

OwOpeepeepoopoo/herewegoagain18

2024-05-03T17:24:38+00:00

TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3783 - F1 Score: 0.8328 - Accuracy: 0.8329 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5299 | 0.6 | 200 | 0.4006 | 0.8167 | 0.8168 | | 0.4157 | 1.2 | 400 | 0.3794 | 0.8336 | 0.8336 | | 0.4031 | 1.81 | 600 | 0.3786 | 0.8307 | 0.8308 | | 0.399 | 2.41 | 800 | 0.3696 | 0.8336 | 0.8336 | | 0.3916 | 3.01 | 1000 | 0.3681 | 0.8352 | 0.8353 | | 0.387 | 3.61 | 1200 | 0.3638 | 0.8390 | 0.8391 | | 0.3907 | 4.22 | 1400 | 0.3662 | 0.8395 | 0.8395 | | 0.386 | 4.82 | 1600 | 0.3624 | 0.8418 | 0.8419 | | 0.377 | 5.42 | 1800 | 0.3715 | 0.8339 | 0.8340 | | 0.3833 | 6.02 | 2000 | 0.3665 | 0.8392 | 0.8393 | | 0.3794 | 6.63 | 2200 | 0.3616 | 0.8398 | 0.8398 | | 0.3765 | 7.23 | 2400 | 0.3654 | 0.8416 | 0.8417 | | 0.3776 | 7.83 | 2600 | 0.3619 | 0.8391 | 0.8391 | | 0.3695 | 8.43 | 2800 | 0.3655 | 0.8373 | 0.8378 | | 0.3752 | 9.04 | 3000 | 0.3597 | 0.8442 | 0.8442 | | 0.368 | 9.64 | 3200 | 0.3595 | 0.8425 | 0.8425 | | 0.3675 | 10.24 | 3400 | 0.3602 | 0.8417 | 0.8417 | | 0.3692 | 10.84 | 3600 | 0.3594 | 0.8407 | 0.8408 | | 0.3657 | 11.45 | 3800 | 0.3580 | 0.8440 | 0.8440 | | 0.3651 | 12.05 | 4000 | 0.3583 | 0.8419 | 0.8419 | | 0.3594 | 12.65 | 4200 | 0.3580 | 0.8431 | 0.8432 | | 0.3633 | 13.25 | 4400 | 0.3588 | 0.8428 | 0.8428 | | 0.361 | 13.86 | 4600 | 0.3606 | 0.8413 | 0.8413 | | 0.359 | 14.46 | 4800 | 0.3588 | 0.8434 | 0.8434 | | 0.3573 | 15.06 | 5000 | 0.3560 | 0.8452 | 0.8453 | | 0.3505 | 15.66 | 5200 | 0.3603 | 0.8428 | 0.8428 | | 0.3549 | 16.27 | 5400 | 0.3618 | 0.8434 | 0.8434 | | 0.3528 | 16.87 | 5600 | 0.3677 | 0.8386 | 0.8391 | | 0.3501 | 17.47 | 5800 | 0.3639 | 0.8427 | 0.8430 | | 0.3573 | 18.07 | 6000 | 0.3615 | 0.8446 | 0.8447 | | 0.3517 | 18.67 | 6200 | 0.3582 | 0.8442 | 0.8444 | | 0.3509 | 19.28 | 6400 | 0.3615 | 0.8432 | 0.8432 | | 0.3489 | 19.88 | 6600 | 0.3584 | 0.8425 | 0.8427 | | 0.3444 | 20.48 | 6800 | 0.3580 | 0.8447 | 0.8447 | | 0.3544 | 21.08 | 7000 | 0.3644 | 0.8404 | 0.8408 | | 0.3525 | 21.69 | 7200 | 0.3604 | 0.8423 | 0.8423 | | 0.3441 | 22.29 | 7400 | 0.3598 | 0.8448 | 0.8449 | | 0.346 | 22.89 | 7600 | 0.3610 | 0.8424 | 0.8425 | | 0.346 | 23.49 | 7800 | 0.3613 | 0.8412 | 0.8413 | | 0.347 | 24.1 | 8000 | 0.3645 | 0.8417 | 0.8419 | | 0.3462 | 24.7 | 8200 | 0.3650 | 0.8416 | 0.8419 | | 0.3401 | 25.3 | 8400 | 0.3669 | 0.8421 | 0.8423 | | 0.3471 | 25.9 | 8600 | 0.3612 | 0.8428 | 0.8428 | | 0.3451 | 26.51 | 8800 | 0.3618 | 0.8432 | 0.8432 | | 0.3456 | 27.11 | 9000 | 0.3604 | 0.8432 | 0.8432 | | 0.3485 | 27.71 | 9200 | 0.3626 | 0.8425 | 0.8427 | | 0.3388 | 28.31 | 9400 | 0.3632 | 0.8442 | 0.8444 | | 0.3412 | 28.92 | 9600 | 0.3632 | 0.8420 | 0.8421 | | 0.3492 | 29.52 | 9800 | 0.3614 | 0.8422 | 0.8423 | | 0.3355 | 30.12 | 10000 | 0.3620 | 0.8431 | 0.8432 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f

2024-05-03T17:24:39+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_15M-L32\_f ============================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3783 * F1 Score: 0.8328 * Accuracy: 0.8329 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4439 - F1 Score: 0.8286 - Accuracy: 0.8287 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.629 | 5.13 | 200 | 0.5857 | 0.7072 | 0.7080 | | 0.5743 | 10.26 | 400 | 0.5824 | 0.6910 | 0.6949 | | 0.5505 | 15.38 | 600 | 0.5729 | 0.7040 | 0.7096 | | 0.53 | 20.51 | 800 | 0.5425 | 0.7238 | 0.7243 | | 0.5137 | 25.64 | 1000 | 0.5271 | 0.7352 | 0.7357 | | 0.4924 | 30.77 | 1200 | 0.4966 | 0.7730 | 0.7732 | | 0.466 | 35.9 | 1400 | 0.4742 | 0.7879 | 0.7879 | | 0.4452 | 41.03 | 1600 | 0.4655 | 0.7808 | 0.7814 | | 0.4341 | 46.15 | 1800 | 0.4457 | 0.8010 | 0.8010 | | 0.4182 | 51.28 | 2000 | 0.4385 | 0.8042 | 0.8042 | | 0.4107 | 56.41 | 2200 | 0.4363 | 0.8075 | 0.8075 | | 0.4042 | 61.54 | 2400 | 0.4199 | 0.8074 | 0.8075 | | 0.3981 | 66.67 | 2600 | 0.4153 | 0.8108 | 0.8108 | | 0.3883 | 71.79 | 2800 | 0.4141 | 0.8075 | 0.8075 | | 0.383 | 76.92 | 3000 | 0.4142 | 0.8140 | 0.8140 | | 0.3755 | 82.05 | 3200 | 0.4044 | 0.8205 | 0.8206 | | 0.3734 | 87.18 | 3400 | 0.4064 | 0.8222 | 0.8222 | | 0.3695 | 92.31 | 3600 | 0.4026 | 0.8238 | 0.8238 | | 0.3625 | 97.44 | 3800 | 0.3999 | 0.8352 | 0.8352 | | 0.3664 | 102.56 | 4000 | 0.3976 | 0.8303 | 0.8303 | | 0.3595 | 107.69 | 4200 | 0.3992 | 0.8303 | 0.8303 | | 0.352 | 112.82 | 4400 | 0.3970 | 0.8303 | 0.8303 | | 0.347 | 117.95 | 4600 | 0.3906 | 0.8303 | 0.8303 | | 0.3497 | 123.08 | 4800 | 0.3944 | 0.8351 | 0.8352 | | 0.3398 | 128.21 | 5000 | 0.3941 | 0.8352 | 0.8352 | | 0.3432 | 133.33 | 5200 | 0.3897 | 0.8352 | 0.8352 | | 0.3371 | 138.46 | 5400 | 0.3878 | 0.8369 | 0.8369 | | 0.3331 | 143.59 | 5600 | 0.3882 | 0.8352 | 0.8352 | | 0.3377 | 148.72 | 5800 | 0.3883 | 0.8352 | 0.8352 | | 0.3288 | 153.85 | 6000 | 0.3889 | 0.8352 | 0.8352 | | 0.3261 | 158.97 | 6200 | 0.3843 | 0.8401 | 0.8401 | | 0.3284 | 164.1 | 6400 | 0.3902 | 0.8335 | 0.8336 | | 0.3293 | 169.23 | 6600 | 0.3837 | 0.8384 | 0.8385 | | 0.3242 | 174.36 | 6800 | 0.3899 | 0.8385 | 0.8385 | | 0.3263 | 179.49 | 7000 | 0.3861 | 0.8352 | 0.8352 | | 0.3193 | 184.62 | 7200 | 0.3874 | 0.8434 | 0.8434 | | 0.3187 | 189.74 | 7400 | 0.3903 | 0.8385 | 0.8385 | | 0.3201 | 194.87 | 7600 | 0.3908 | 0.8385 | 0.8385 | | 0.3194 | 200.0 | 7800 | 0.3860 | 0.8466 | 0.8467 | | 0.3187 | 205.13 | 8000 | 0.3869 | 0.8449 | 0.8450 | | 0.3163 | 210.26 | 8200 | 0.3877 | 0.8401 | 0.8401 | | 0.313 | 215.38 | 8400 | 0.3892 | 0.8417 | 0.8418 | | 0.316 | 220.51 | 8600 | 0.3888 | 0.8385 | 0.8385 | | 0.3144 | 225.64 | 8800 | 0.3886 | 0.8417 | 0.8418 | | 0.3124 | 230.77 | 9000 | 0.3866 | 0.8449 | 0.8450 | | 0.3119 | 235.9 | 9200 | 0.3874 | 0.8417 | 0.8418 | | 0.3125 | 241.03 | 9400 | 0.3884 | 0.8450 | 0.8450 | | 0.3151 | 246.15 | 9600 | 0.3868 | 0.8417 | 0.8418 | | 0.3084 | 251.28 | 9800 | 0.3879 | 0.8417 | 0.8418 | | 0.3116 | 256.41 | 10000 | 0.3878 | 0.8450 | 0.8450 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f

2024-05-03T17:25:18+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_15M-L1\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4439 * F1 Score: 0.8286 * Accuracy: 0.8287 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

image-segmentation

transformers

<!-- 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. --> # segformer-b0-finetuned-raw_img_ready2train_patches This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the raw_img_ready2train_patches dataset. It achieves the following results on the evaluation set: - Loss: 0.6829 - Mean Iou: 0.4110 - Mean Accuracy: 0.7629 - Overall Accuracy: 0.7631 - Accuracy Unlabeled: nan - Accuracy Eczema: 0.7673 - Accuracy Background: 0.7585 - Iou Unlabeled: 0.0 - Iou Eczema: 0.6284 - Iou Background: 0.6047 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Eczema | Accuracy Background | Iou Unlabeled | Iou Eczema | Iou Background | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------:|:----------:|:--------------:| | 1.0753 | 0.0312 | 5 | 1.0925 | 0.2358 | 0.4682 | 0.4698 | nan | 0.5042 | 0.4322 | 0.0 | 0.3705 | 0.3367 | | 0.9863 | 0.0625 | 10 | 1.0697 | 0.2994 | 0.6182 | 0.6306 | nan | 0.8979 | 0.3385 | 0.0 | 0.5784 | 0.3198 | | 1.0056 | 0.0938 | 15 | 1.0377 | 0.3303 | 0.6678 | 0.6792 | nan | 0.9236 | 0.4121 | 0.0 | 0.6064 | 0.3844 | | 1.0133 | 0.125 | 20 | 1.0006 | 0.3478 | 0.6869 | 0.6950 | nan | 0.8710 | 0.5027 | 0.0 | 0.6008 | 0.4425 | | 0.9748 | 0.1562 | 25 | 0.9689 | 0.3543 | 0.6947 | 0.7022 | nan | 0.8647 | 0.5246 | 0.0 | 0.6043 | 0.4586 | | 0.9367 | 0.1875 | 30 | 0.9417 | 0.3566 | 0.6950 | 0.6965 | nan | 0.7290 | 0.6610 | 0.0 | 0.5583 | 0.5114 | | 0.8363 | 0.2188 | 35 | 0.9118 | 0.3557 | 0.6940 | 0.6959 | nan | 0.7366 | 0.6514 | 0.0 | 0.5600 | 0.5069 | | 1.1431 | 0.25 | 40 | 0.8830 | 0.3575 | 0.6963 | 0.6989 | nan | 0.7556 | 0.6370 | 0.0 | 0.5686 | 0.5039 | | 0.7312 | 0.2812 | 45 | 0.8592 | 0.3680 | 0.7098 | 0.7133 | nan | 0.7888 | 0.6307 | 0.0 | 0.5907 | 0.5133 | | 0.8135 | 0.3125 | 50 | 0.8268 | 0.3559 | 0.6994 | 0.7083 | nan | 0.8992 | 0.4997 | 0.0 | 0.6173 | 0.4505 | | 0.7528 | 0.3438 | 55 | 0.8110 | 0.3525 | 0.6960 | 0.7053 | nan | 0.9055 | 0.4866 | 0.0 | 0.6162 | 0.4412 | | 0.8405 | 0.375 | 60 | 0.7967 | 0.3518 | 0.6950 | 0.7041 | nan | 0.9008 | 0.4893 | 0.0 | 0.6140 | 0.4415 | | 0.7865 | 0.4062 | 65 | 0.7791 | 0.3561 | 0.6992 | 0.7075 | nan | 0.8869 | 0.5116 | 0.0 | 0.6130 | 0.4553 | | 0.8309 | 0.4375 | 70 | 0.7650 | 0.3652 | 0.7083 | 0.7147 | nan | 0.8512 | 0.5655 | 0.0 | 0.6090 | 0.4864 | | 0.6775 | 0.4688 | 75 | 0.7615 | 0.3613 | 0.7044 | 0.7115 | nan | 0.8651 | 0.5437 | 0.0 | 0.6102 | 0.4738 | | 0.7033 | 0.5 | 80 | 0.7498 | 0.3737 | 0.7179 | 0.7227 | nan | 0.8260 | 0.6099 | 0.0 | 0.6087 | 0.5125 | | 0.8377 | 0.5312 | 85 | 0.7443 | 0.3790 | 0.7243 | 0.7290 | nan | 0.8303 | 0.6184 | 0.0 | 0.6154 | 0.5217 | | 0.825 | 0.5625 | 90 | 0.7547 | 0.3676 | 0.7125 | 0.7201 | nan | 0.8840 | 0.5411 | 0.0 | 0.6225 | 0.4802 | | 0.7408 | 0.5938 | 95 | 0.7415 | 0.3767 | 0.7228 | 0.7295 | nan | 0.8747 | 0.5708 | 0.0 | 0.6281 | 0.5021 | | 0.8087 | 0.625 | 100 | 0.7201 | 0.3926 | 0.7404 | 0.7445 | nan | 0.8318 | 0.6491 | 0.0 | 0.6296 | 0.5483 | | 0.7146 | 0.6562 | 105 | 0.7096 | 0.4002 | 0.7493 | 0.7520 | nan | 0.8109 | 0.6877 | 0.0 | 0.6307 | 0.5699 | | 0.6875 | 0.6875 | 110 | 0.7047 | 0.4010 | 0.7502 | 0.7541 | nan | 0.8398 | 0.6606 | 0.0 | 0.6407 | 0.5621 | | 0.6382 | 0.7188 | 115 | 0.7031 | 0.3982 | 0.7471 | 0.7519 | nan | 0.8543 | 0.6400 | 0.0 | 0.6426 | 0.5521 | | 0.6551 | 0.75 | 120 | 0.6953 | 0.4018 | 0.7512 | 0.7553 | nan | 0.8450 | 0.6573 | 0.0 | 0.6433 | 0.5621 | | 0.7074 | 0.7812 | 125 | 0.6912 | 0.4054 | 0.7553 | 0.7583 | nan | 0.8236 | 0.6871 | 0.0 | 0.6402 | 0.5760 | | 0.768 | 0.8125 | 130 | 0.6866 | 0.4048 | 0.7546 | 0.7579 | nan | 0.8278 | 0.6814 | 0.0 | 0.6410 | 0.5736 | | 0.7543 | 0.8438 | 135 | 0.6851 | 0.4031 | 0.7526 | 0.7564 | nan | 0.8374 | 0.6679 | 0.0 | 0.6422 | 0.5671 | | 0.7107 | 0.875 | 140 | 0.6803 | 0.6122 | 0.7586 | 0.7608 | nan | 0.8071 | 0.7101 | nan | 0.6379 | 0.5865 | | 0.7054 | 0.9062 | 145 | 0.6799 | 0.4098 | 0.7608 | 0.7622 | nan | 0.7924 | 0.7292 | 0.0 | 0.6350 | 0.5943 | | 1.1302 | 0.9375 | 150 | 0.6801 | 0.4103 | 0.7616 | 0.7626 | nan | 0.7840 | 0.7393 | 0.0 | 0.6330 | 0.5981 | | 0.6037 | 0.9688 | 155 | 0.6827 | 0.4111 | 0.7628 | 0.7632 | nan | 0.7721 | 0.7534 | 0.0 | 0.6300 | 0.6032 | | 0.8577 | 1.0 | 160 | 0.6829 | 0.4110 | 0.7629 | 0.7631 | nan | 0.7673 | 0.7585 | 0.0 | 0.6284 | 0.6047 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-finetuned-raw_img_ready2train_patches", "results": []}]}

ruisusanofi/segformer-b0-finetuned-raw_img_ready2train_patches

2024-05-03T17:27:04+00:00

TAGS #transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us

segformer-b0-finetuned-raw\_img\_ready2train\_patches ===================================================== This model is a fine-tuned version of nvidia/mit-b0 on the raw\_img\_ready2train\_patches dataset. It achieves the following results on the evaluation set: * Loss: 0.6829 * Mean Iou: 0.4110 * Mean Accuracy: 0.7629 * Overall Accuracy: 0.7631 * Accuracy Unlabeled: nan * Accuracy Eczema: 0.7673 * Accuracy Background: 0.7585 * Iou Unlabeled: 0.0 * Iou Eczema: 0.6284 * Iou Background: 0.6047 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: 5e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4594 - F1 Score: 0.8271 - Accuracy: 0.8271 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6032 | 5.13 | 200 | 0.5634 | 0.7185 | 0.7194 | | 0.5278 | 10.26 | 400 | 0.5256 | 0.7344 | 0.7357 | | 0.4699 | 15.38 | 600 | 0.4605 | 0.7856 | 0.7863 | | 0.4245 | 20.51 | 800 | 0.4272 | 0.7976 | 0.7977 | | 0.3889 | 25.64 | 1000 | 0.4069 | 0.8155 | 0.8157 | | 0.3665 | 30.77 | 1200 | 0.3898 | 0.8236 | 0.8238 | | 0.3478 | 35.9 | 1400 | 0.3919 | 0.8320 | 0.8320 | | 0.3314 | 41.03 | 1600 | 0.3985 | 0.8265 | 0.8271 | | 0.3178 | 46.15 | 1800 | 0.3865 | 0.8352 | 0.8352 | | 0.3045 | 51.28 | 2000 | 0.3880 | 0.8319 | 0.8320 | | 0.2962 | 56.41 | 2200 | 0.3923 | 0.8434 | 0.8434 | | 0.2901 | 61.54 | 2400 | 0.3825 | 0.8401 | 0.8401 | | 0.2789 | 66.67 | 2600 | 0.3828 | 0.8352 | 0.8352 | | 0.2688 | 71.79 | 2800 | 0.3823 | 0.8367 | 0.8369 | | 0.2668 | 76.92 | 3000 | 0.3948 | 0.8352 | 0.8352 | | 0.2553 | 82.05 | 3200 | 0.3873 | 0.8385 | 0.8385 | | 0.25 | 87.18 | 3400 | 0.3933 | 0.8385 | 0.8385 | | 0.2466 | 92.31 | 3600 | 0.3986 | 0.8466 | 0.8467 | | 0.2419 | 97.44 | 3800 | 0.3981 | 0.8465 | 0.8467 | | 0.2396 | 102.56 | 4000 | 0.3904 | 0.8596 | 0.8597 | | 0.2347 | 107.69 | 4200 | 0.4066 | 0.8548 | 0.8548 | | 0.2237 | 112.82 | 4400 | 0.4169 | 0.8548 | 0.8548 | | 0.2197 | 117.95 | 4600 | 0.4028 | 0.8613 | 0.8613 | | 0.2178 | 123.08 | 4800 | 0.4289 | 0.8483 | 0.8483 | | 0.2117 | 128.21 | 5000 | 0.4253 | 0.8499 | 0.8499 | | 0.2147 | 133.33 | 5200 | 0.4187 | 0.8596 | 0.8597 | | 0.2068 | 138.46 | 5400 | 0.4218 | 0.8611 | 0.8613 | | 0.2019 | 143.59 | 5600 | 0.4296 | 0.8466 | 0.8467 | | 0.2023 | 148.72 | 5800 | 0.4374 | 0.8548 | 0.8548 | | 0.1959 | 153.85 | 6000 | 0.4354 | 0.8515 | 0.8515 | | 0.1974 | 158.97 | 6200 | 0.4282 | 0.8564 | 0.8564 | | 0.1983 | 164.1 | 6400 | 0.4305 | 0.8515 | 0.8515 | | 0.1928 | 169.23 | 6600 | 0.4352 | 0.8581 | 0.8581 | | 0.1889 | 174.36 | 6800 | 0.4507 | 0.8532 | 0.8532 | | 0.1909 | 179.49 | 7000 | 0.4417 | 0.8450 | 0.8450 | | 0.1855 | 184.62 | 7200 | 0.4481 | 0.8548 | 0.8548 | | 0.1824 | 189.74 | 7400 | 0.4513 | 0.8564 | 0.8564 | | 0.1837 | 194.87 | 7600 | 0.4567 | 0.8515 | 0.8515 | | 0.1841 | 200.0 | 7800 | 0.4383 | 0.8630 | 0.8630 | | 0.1819 | 205.13 | 8000 | 0.4506 | 0.8532 | 0.8532 | | 0.1809 | 210.26 | 8200 | 0.4516 | 0.8499 | 0.8499 | | 0.1753 | 215.38 | 8400 | 0.4639 | 0.8467 | 0.8467 | | 0.1771 | 220.51 | 8600 | 0.4612 | 0.8548 | 0.8548 | | 0.1777 | 225.64 | 8800 | 0.4593 | 0.8483 | 0.8483 | | 0.1723 | 230.77 | 9000 | 0.4591 | 0.8499 | 0.8499 | | 0.1727 | 235.9 | 9200 | 0.4602 | 0.8467 | 0.8467 | | 0.1714 | 241.03 | 9400 | 0.4662 | 0.8548 | 0.8548 | | 0.1739 | 246.15 | 9600 | 0.4643 | 0.8450 | 0.8450 | | 0.1721 | 251.28 | 9800 | 0.4632 | 0.8532 | 0.8532 | | 0.1689 | 256.41 | 10000 | 0.4628 | 0.8532 | 0.8532 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f

2024-05-03T17:28:17+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_15M-L8\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4594 * F1 Score: 0.8271 * Accuracy: 0.8271 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4640 - F1 Score: 0.8320 - Accuracy: 0.8320 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5782 | 5.13 | 200 | 0.5315 | 0.7406 | 0.7406 | | 0.4665 | 10.26 | 400 | 0.4535 | 0.7895 | 0.7896 | | 0.3912 | 15.38 | 600 | 0.3940 | 0.8189 | 0.8189 | | 0.3406 | 20.51 | 800 | 0.3676 | 0.8482 | 0.8483 | | 0.301 | 25.64 | 1000 | 0.3680 | 0.8676 | 0.8679 | | 0.2781 | 30.77 | 1200 | 0.3465 | 0.8596 | 0.8597 | | 0.2586 | 35.9 | 1400 | 0.3497 | 0.8662 | 0.8662 | | 0.2365 | 41.03 | 1600 | 0.3888 | 0.8575 | 0.8581 | | 0.2231 | 46.15 | 1800 | 0.3801 | 0.8547 | 0.8548 | | 0.2111 | 51.28 | 2000 | 0.3956 | 0.8612 | 0.8613 | | 0.1949 | 56.41 | 2200 | 0.4369 | 0.8532 | 0.8532 | | 0.1843 | 61.54 | 2400 | 0.4161 | 0.8611 | 0.8613 | | 0.1706 | 66.67 | 2600 | 0.4586 | 0.8659 | 0.8662 | | 0.1597 | 71.79 | 2800 | 0.4525 | 0.8679 | 0.8679 | | 0.1529 | 76.92 | 3000 | 0.4764 | 0.8449 | 0.8450 | | 0.1405 | 82.05 | 3200 | 0.5161 | 0.8547 | 0.8548 | | 0.1323 | 87.18 | 3400 | 0.5201 | 0.8662 | 0.8662 | | 0.1275 | 92.31 | 3600 | 0.5121 | 0.8628 | 0.8630 | | 0.1212 | 97.44 | 3800 | 0.5360 | 0.8645 | 0.8646 | | 0.1135 | 102.56 | 4000 | 0.5797 | 0.8595 | 0.8597 | | 0.11 | 107.69 | 4200 | 0.5665 | 0.8613 | 0.8613 | | 0.1041 | 112.82 | 4400 | 0.5754 | 0.8597 | 0.8597 | | 0.1008 | 117.95 | 4600 | 0.5795 | 0.8547 | 0.8548 | | 0.093 | 123.08 | 4800 | 0.6056 | 0.8630 | 0.8630 | | 0.0896 | 128.21 | 5000 | 0.6137 | 0.8564 | 0.8564 | | 0.0883 | 133.33 | 5200 | 0.6119 | 0.8564 | 0.8564 | | 0.0813 | 138.46 | 5400 | 0.6257 | 0.8629 | 0.8630 | | 0.0794 | 143.59 | 5600 | 0.6374 | 0.8630 | 0.8630 | | 0.0781 | 148.72 | 5800 | 0.6801 | 0.8597 | 0.8597 | | 0.0753 | 153.85 | 6000 | 0.6478 | 0.8580 | 0.8581 | | 0.0709 | 158.97 | 6200 | 0.6664 | 0.8630 | 0.8630 | | 0.0725 | 164.1 | 6400 | 0.6262 | 0.8564 | 0.8564 | | 0.067 | 169.23 | 6600 | 0.6659 | 0.8581 | 0.8581 | | 0.0632 | 174.36 | 6800 | 0.6947 | 0.8564 | 0.8564 | | 0.067 | 179.49 | 7000 | 0.6948 | 0.8564 | 0.8564 | | 0.0627 | 184.62 | 7200 | 0.7080 | 0.8564 | 0.8564 | | 0.0611 | 189.74 | 7400 | 0.7102 | 0.8548 | 0.8548 | | 0.0595 | 194.87 | 7600 | 0.7069 | 0.8629 | 0.8630 | | 0.062 | 200.0 | 7800 | 0.6852 | 0.8646 | 0.8646 | | 0.0554 | 205.13 | 8000 | 0.7127 | 0.8613 | 0.8613 | | 0.0596 | 210.26 | 8200 | 0.6846 | 0.8548 | 0.8548 | | 0.0534 | 215.38 | 8400 | 0.7266 | 0.8597 | 0.8597 | | 0.0561 | 220.51 | 8600 | 0.7142 | 0.8532 | 0.8532 | | 0.0517 | 225.64 | 8800 | 0.7146 | 0.8532 | 0.8532 | | 0.0512 | 230.77 | 9000 | 0.7151 | 0.8564 | 0.8564 | | 0.0523 | 235.9 | 9200 | 0.6998 | 0.8581 | 0.8581 | | 0.053 | 241.03 | 9400 | 0.7092 | 0.8662 | 0.8662 | | 0.0495 | 246.15 | 9600 | 0.7234 | 0.8613 | 0.8613 | | 0.0514 | 251.28 | 9800 | 0.7236 | 0.8613 | 0.8613 | | 0.0514 | 256.41 | 10000 | 0.7248 | 0.8597 | 0.8597 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f

2024-05-03T17:28:21+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_15M-L32\_f =========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4640 * F1 Score: 0.8320 * Accuracy: 0.8320 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-classification

transformers

<!-- 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. --> # question_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0621 - Accuracy: 1.0 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 0.1063 | 1.0 | | No log | 2.0 | 30 | 0.0621 | 1.0 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "question_classifier", "results": []}]}

philgrey/question_classifier

2024-05-03T17:29:08+00:00

TAGS #transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

question\_classifier ==================== This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0621 * Accuracy: 1.0 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: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.1.0 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - F1 Score: 0.9135 - Accuracy: 0.9135 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.4293 | 0.54 | 200 | 0.2948 | 0.8828 | 0.8828 | | 0.3066 | 1.08 | 400 | 0.2688 | 0.8919 | 0.8919 | | 0.2856 | 1.62 | 600 | 0.2528 | 0.8953 | 0.8953 | | 0.2612 | 2.16 | 800 | 0.2449 | 0.9021 | 0.9022 | | 0.2536 | 2.7 | 1000 | 0.2343 | 0.9061 | 0.9061 | | 0.2466 | 3.24 | 1200 | 0.2309 | 0.9101 | 0.9101 | | 0.2442 | 3.78 | 1400 | 0.2255 | 0.9123 | 0.9123 | | 0.2415 | 4.32 | 1600 | 0.2236 | 0.9142 | 0.9142 | | 0.2313 | 4.86 | 1800 | 0.2214 | 0.9160 | 0.9160 | | 0.232 | 5.41 | 2000 | 0.2196 | 0.9165 | 0.9166 | | 0.2312 | 5.95 | 2200 | 0.2174 | 0.9179 | 0.9179 | | 0.2288 | 6.49 | 2400 | 0.2151 | 0.9184 | 0.9184 | | 0.2271 | 7.03 | 2600 | 0.2132 | 0.9179 | 0.9179 | | 0.2222 | 7.57 | 2800 | 0.2103 | 0.9199 | 0.9199 | | 0.2241 | 8.11 | 3000 | 0.2105 | 0.9206 | 0.9206 | | 0.2221 | 8.65 | 3200 | 0.2076 | 0.9218 | 0.9218 | | 0.2162 | 9.19 | 3400 | 0.2091 | 0.9213 | 0.9213 | | 0.2148 | 9.73 | 3600 | 0.2041 | 0.9235 | 0.9235 | | 0.2211 | 10.27 | 3800 | 0.2025 | 0.9233 | 0.9233 | | 0.2149 | 10.81 | 4000 | 0.2022 | 0.9243 | 0.9243 | | 0.2168 | 11.35 | 4200 | 0.2010 | 0.9241 | 0.9242 | | 0.2128 | 11.89 | 4400 | 0.2016 | 0.9270 | 0.9270 | | 0.2117 | 12.43 | 4600 | 0.1994 | 0.9223 | 0.9223 | | 0.2135 | 12.97 | 4800 | 0.1967 | 0.9280 | 0.9280 | | 0.2084 | 13.51 | 5000 | 0.1976 | 0.9262 | 0.9262 | | 0.2139 | 14.05 | 5200 | 0.1957 | 0.9265 | 0.9265 | | 0.2089 | 14.59 | 5400 | 0.1966 | 0.9260 | 0.9260 | | 0.2067 | 15.14 | 5600 | 0.1960 | 0.9255 | 0.9255 | | 0.2062 | 15.68 | 5800 | 0.1948 | 0.9284 | 0.9284 | | 0.2084 | 16.22 | 6000 | 0.1950 | 0.9253 | 0.9253 | | 0.2052 | 16.76 | 6200 | 0.1935 | 0.9285 | 0.9285 | | 0.2056 | 17.3 | 6400 | 0.1949 | 0.9260 | 0.9260 | | 0.2074 | 17.84 | 6600 | 0.1934 | 0.9258 | 0.9258 | | 0.2021 | 18.38 | 6800 | 0.1926 | 0.9277 | 0.9277 | | 0.2082 | 18.92 | 7000 | 0.1913 | 0.9284 | 0.9284 | | 0.2074 | 19.46 | 7200 | 0.1923 | 0.9282 | 0.9282 | | 0.2013 | 20.0 | 7400 | 0.1917 | 0.9282 | 0.9282 | | 0.2033 | 20.54 | 7600 | 0.1910 | 0.9284 | 0.9284 | | 0.2014 | 21.08 | 7800 | 0.1903 | 0.9294 | 0.9294 | | 0.2051 | 21.62 | 8000 | 0.1904 | 0.9287 | 0.9287 | | 0.2025 | 22.16 | 8200 | 0.1903 | 0.9291 | 0.9291 | | 0.1986 | 22.7 | 8400 | 0.1903 | 0.9282 | 0.9282 | | 0.2057 | 23.24 | 8600 | 0.1898 | 0.9289 | 0.9289 | | 0.2012 | 23.78 | 8800 | 0.1893 | 0.9289 | 0.9289 | | 0.2033 | 24.32 | 9000 | 0.1896 | 0.9294 | 0.9294 | | 0.2009 | 24.86 | 9200 | 0.1898 | 0.9291 | 0.9291 | | 0.2009 | 25.41 | 9400 | 0.1902 | 0.9291 | 0.9291 | | 0.1996 | 25.95 | 9600 | 0.1899 | 0.9289 | 0.9289 | | 0.2019 | 26.49 | 9800 | 0.1894 | 0.9296 | 0.9296 | | 0.2001 | 27.03 | 10000 | 0.1895 | 0.9287 | 0.9287 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f

2024-05-03T17:29:51+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_15M-L1\_f ======================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.2156 * F1 Score: 0.9135 * Accuracy: 0.9135 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1974 - F1 Score: 0.9209 - Accuracy: 0.9209 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3768 | 0.54 | 200 | 0.2589 | 0.8973 | 0.8973 | | 0.2631 | 1.08 | 400 | 0.2351 | 0.9071 | 0.9071 | | 0.2483 | 1.62 | 600 | 0.2179 | 0.9130 | 0.9130 | | 0.2279 | 2.16 | 800 | 0.2134 | 0.9146 | 0.9147 | | 0.2247 | 2.7 | 1000 | 0.2063 | 0.9203 | 0.9203 | | 0.2187 | 3.24 | 1200 | 0.2048 | 0.9182 | 0.9182 | | 0.2199 | 3.78 | 1400 | 0.1983 | 0.9216 | 0.9216 | | 0.2136 | 4.32 | 1600 | 0.1935 | 0.9238 | 0.9238 | | 0.2055 | 4.86 | 1800 | 0.1926 | 0.9250 | 0.925 | | 0.2062 | 5.41 | 2000 | 0.1902 | 0.9292 | 0.9292 | | 0.2048 | 5.95 | 2200 | 0.1900 | 0.9240 | 0.9240 | | 0.2034 | 6.49 | 2400 | 0.1868 | 0.9270 | 0.9270 | | 0.2024 | 7.03 | 2600 | 0.1869 | 0.9284 | 0.9284 | | 0.194 | 7.57 | 2800 | 0.1862 | 0.9287 | 0.9287 | | 0.2 | 8.11 | 3000 | 0.1853 | 0.9302 | 0.9302 | | 0.1959 | 8.65 | 3200 | 0.1851 | 0.9292 | 0.9292 | | 0.1885 | 9.19 | 3400 | 0.1864 | 0.9296 | 0.9296 | | 0.1888 | 9.73 | 3600 | 0.1827 | 0.9280 | 0.9280 | | 0.1944 | 10.27 | 3800 | 0.1824 | 0.9292 | 0.9292 | | 0.1895 | 10.81 | 4000 | 0.1819 | 0.9304 | 0.9304 | | 0.1917 | 11.35 | 4200 | 0.1797 | 0.9306 | 0.9306 | | 0.1854 | 11.89 | 4400 | 0.1828 | 0.9307 | 0.9307 | | 0.1873 | 12.43 | 4600 | 0.1790 | 0.9296 | 0.9296 | | 0.1861 | 12.97 | 4800 | 0.1771 | 0.9314 | 0.9314 | | 0.1823 | 13.51 | 5000 | 0.1789 | 0.9289 | 0.9289 | | 0.187 | 14.05 | 5200 | 0.1809 | 0.9280 | 0.9280 | | 0.1817 | 14.59 | 5400 | 0.1778 | 0.9323 | 0.9323 | | 0.1801 | 15.14 | 5600 | 0.1776 | 0.9316 | 0.9316 | | 0.1801 | 15.68 | 5800 | 0.1781 | 0.9304 | 0.9304 | | 0.179 | 16.22 | 6000 | 0.1787 | 0.9316 | 0.9316 | | 0.1784 | 16.76 | 6200 | 0.1779 | 0.9296 | 0.9296 | | 0.1787 | 17.3 | 6400 | 0.1792 | 0.9277 | 0.9277 | | 0.1794 | 17.84 | 6600 | 0.1755 | 0.9328 | 0.9328 | | 0.1748 | 18.38 | 6800 | 0.1776 | 0.9294 | 0.9294 | | 0.1804 | 18.92 | 7000 | 0.1763 | 0.9292 | 0.9292 | | 0.1802 | 19.46 | 7200 | 0.1765 | 0.9316 | 0.9316 | | 0.1741 | 20.0 | 7400 | 0.1755 | 0.9326 | 0.9326 | | 0.1767 | 20.54 | 7600 | 0.1752 | 0.9309 | 0.9309 | | 0.1739 | 21.08 | 7800 | 0.1747 | 0.9312 | 0.9313 | | 0.1747 | 21.62 | 8000 | 0.1748 | 0.9311 | 0.9311 | | 0.1758 | 22.16 | 8200 | 0.1758 | 0.9319 | 0.9319 | | 0.1724 | 22.7 | 8400 | 0.1738 | 0.9336 | 0.9336 | | 0.1762 | 23.24 | 8600 | 0.1753 | 0.9306 | 0.9306 | | 0.1759 | 23.78 | 8800 | 0.1744 | 0.9312 | 0.9313 | | 0.1751 | 24.32 | 9000 | 0.1756 | 0.9307 | 0.9307 | | 0.1727 | 24.86 | 9200 | 0.1742 | 0.9318 | 0.9318 | | 0.1718 | 25.41 | 9400 | 0.1766 | 0.9309 | 0.9309 | | 0.1719 | 25.95 | 9600 | 0.1750 | 0.9321 | 0.9321 | | 0.173 | 26.49 | 9800 | 0.1745 | 0.9311 | 0.9311 | | 0.1729 | 27.03 | 10000 | 0.1746 | 0.9311 | 0.9311 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f

2024-05-03T17:30:16+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_15M-L8\_f ======================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.1974 * F1 Score: 0.9209 * Accuracy: 0.9209 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - F1 Score: 0.9221 - Accuracy: 0.9221 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3384 | 0.54 | 200 | 0.2347 | 0.9054 | 0.9054 | | 0.238 | 1.08 | 400 | 0.2090 | 0.9182 | 0.9182 | | 0.2278 | 1.62 | 600 | 0.1982 | 0.9226 | 0.9226 | | 0.2116 | 2.16 | 800 | 0.1958 | 0.9222 | 0.9223 | | 0.2086 | 2.7 | 1000 | 0.1936 | 0.9238 | 0.9238 | | 0.2038 | 3.24 | 1200 | 0.1907 | 0.9248 | 0.9248 | | 0.2055 | 3.78 | 1400 | 0.1871 | 0.9264 | 0.9264 | | 0.1993 | 4.32 | 1600 | 0.1862 | 0.9258 | 0.9258 | | 0.1938 | 4.86 | 1800 | 0.1810 | 0.9292 | 0.9292 | | 0.1914 | 5.41 | 2000 | 0.1831 | 0.9319 | 0.9319 | | 0.1908 | 5.95 | 2200 | 0.1822 | 0.9272 | 0.9272 | | 0.1883 | 6.49 | 2400 | 0.1779 | 0.9301 | 0.9301 | | 0.1878 | 7.03 | 2600 | 0.1818 | 0.9321 | 0.9321 | | 0.1787 | 7.57 | 2800 | 0.1776 | 0.9321 | 0.9321 | | 0.1846 | 8.11 | 3000 | 0.1798 | 0.9304 | 0.9304 | | 0.1792 | 8.65 | 3200 | 0.1748 | 0.9321 | 0.9321 | | 0.1713 | 9.19 | 3400 | 0.1808 | 0.9311 | 0.9311 | | 0.1723 | 9.73 | 3600 | 0.1742 | 0.9307 | 0.9307 | | 0.1774 | 10.27 | 3800 | 0.1742 | 0.9311 | 0.9311 | | 0.1732 | 10.81 | 4000 | 0.1763 | 0.9346 | 0.9346 | | 0.1724 | 11.35 | 4200 | 0.1725 | 0.9345 | 0.9345 | | 0.167 | 11.89 | 4400 | 0.1760 | 0.9346 | 0.9346 | | 0.1691 | 12.43 | 4600 | 0.1716 | 0.9333 | 0.9333 | | 0.1638 | 12.97 | 4800 | 0.1699 | 0.9311 | 0.9311 | | 0.1619 | 13.51 | 5000 | 0.1736 | 0.9302 | 0.9302 | | 0.1661 | 14.05 | 5200 | 0.1766 | 0.9273 | 0.9274 | | 0.16 | 14.59 | 5400 | 0.1720 | 0.9309 | 0.9309 | | 0.1591 | 15.14 | 5600 | 0.1725 | 0.9323 | 0.9323 | | 0.1584 | 15.68 | 5800 | 0.1710 | 0.9318 | 0.9318 | | 0.1562 | 16.22 | 6000 | 0.1739 | 0.9309 | 0.9309 | | 0.1552 | 16.76 | 6200 | 0.1748 | 0.9321 | 0.9321 | | 0.1551 | 17.3 | 6400 | 0.1751 | 0.9309 | 0.9309 | | 0.1566 | 17.84 | 6600 | 0.1718 | 0.9331 | 0.9331 | | 0.1509 | 18.38 | 6800 | 0.1730 | 0.9314 | 0.9314 | | 0.1546 | 18.92 | 7000 | 0.1714 | 0.9331 | 0.9331 | | 0.1538 | 19.46 | 7200 | 0.1716 | 0.9334 | 0.9334 | | 0.15 | 20.0 | 7400 | 0.1728 | 0.9339 | 0.9340 | | 0.1513 | 20.54 | 7600 | 0.1715 | 0.9328 | 0.9328 | | 0.1485 | 21.08 | 7800 | 0.1698 | 0.9326 | 0.9326 | | 0.1484 | 21.62 | 8000 | 0.1706 | 0.9326 | 0.9326 | | 0.1494 | 22.16 | 8200 | 0.1711 | 0.9331 | 0.9331 | | 0.1448 | 22.7 | 8400 | 0.1689 | 0.9333 | 0.9333 | | 0.1468 | 23.24 | 8600 | 0.1715 | 0.9323 | 0.9323 | | 0.1478 | 23.78 | 8800 | 0.1719 | 0.9317 | 0.9318 | | 0.1448 | 24.32 | 9000 | 0.1721 | 0.9317 | 0.9318 | | 0.1454 | 24.86 | 9200 | 0.1707 | 0.9331 | 0.9331 | | 0.1432 | 25.41 | 9400 | 0.1746 | 0.9328 | 0.9328 | | 0.1437 | 25.95 | 9600 | 0.1727 | 0.9326 | 0.9326 | | 0.1448 | 26.49 | 9800 | 0.1718 | 0.9328 | 0.9328 | | 0.1434 | 27.03 | 10000 | 0.1715 | 0.9331 | 0.9331 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f

2024-05-03T17:30:56+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_15M-L32\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.1996 * F1 Score: 0.9221 * Accuracy: 0.9221 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5179 - F1 Score: 0.7401 - Accuracy: 0.7398 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.621 | 0.97 | 200 | 0.5813 | 0.7034 | 0.7014 | | 0.5829 | 1.93 | 400 | 0.5649 | 0.7244 | 0.7234 | | 0.5699 | 2.9 | 600 | 0.5881 | 0.7000 | 0.6989 | | 0.5629 | 3.86 | 800 | 0.5478 | 0.7259 | 0.7277 | | 0.5513 | 4.83 | 1000 | 0.5659 | 0.7205 | 0.7189 | | 0.5467 | 5.8 | 1200 | 0.5586 | 0.7257 | 0.7241 | | 0.5421 | 6.76 | 1400 | 0.5415 | 0.7344 | 0.7325 | | 0.54 | 7.73 | 1600 | 0.5395 | 0.7347 | 0.7328 | | 0.5343 | 8.7 | 1800 | 0.5408 | 0.7313 | 0.7295 | | 0.5335 | 9.66 | 2000 | 0.5432 | 0.7340 | 0.7322 | | 0.5333 | 10.63 | 2200 | 0.5558 | 0.7252 | 0.7241 | | 0.5269 | 11.59 | 2400 | 0.5283 | 0.7408 | 0.7392 | | 0.5308 | 12.56 | 2600 | 0.5436 | 0.7342 | 0.7325 | | 0.5281 | 13.53 | 2800 | 0.5438 | 0.7280 | 0.7265 | | 0.5271 | 14.49 | 3000 | 0.5531 | 0.7231 | 0.7222 | | 0.5225 | 15.46 | 3200 | 0.5235 | 0.7473 | 0.7461 | | 0.5232 | 16.43 | 3400 | 0.5536 | 0.7240 | 0.7231 | | 0.5238 | 17.39 | 3600 | 0.5289 | 0.7389 | 0.7371 | | 0.52 | 18.36 | 3800 | 0.5192 | 0.7531 | 0.7525 | | 0.5196 | 19.32 | 4000 | 0.5257 | 0.7443 | 0.7425 | | 0.5165 | 20.29 | 4200 | 0.5332 | 0.7413 | 0.7395 | | 0.5193 | 21.26 | 4400 | 0.5360 | 0.7372 | 0.7356 | | 0.5184 | 22.22 | 4600 | 0.5446 | 0.7270 | 0.7259 | | 0.5189 | 23.19 | 4800 | 0.5232 | 0.7500 | 0.7483 | | 0.5167 | 24.15 | 5000 | 0.5251 | 0.7461 | 0.7443 | | 0.5142 | 25.12 | 5200 | 0.5545 | 0.7270 | 0.7262 | | 0.5155 | 26.09 | 5400 | 0.5322 | 0.7387 | 0.7371 | | 0.5159 | 27.05 | 5600 | 0.5536 | 0.7217 | 0.7213 | | 0.5137 | 28.02 | 5800 | 0.5214 | 0.7500 | 0.7483 | | 0.514 | 28.99 | 6000 | 0.5382 | 0.7318 | 0.7304 | | 0.5121 | 29.95 | 6200 | 0.5395 | 0.7333 | 0.7319 | | 0.5146 | 30.92 | 6400 | 0.5213 | 0.7512 | 0.7495 | | 0.5135 | 31.88 | 6600 | 0.5305 | 0.7396 | 0.7380 | | 0.509 | 32.85 | 6800 | 0.5327 | 0.7377 | 0.7362 | | 0.5134 | 33.82 | 7000 | 0.5423 | 0.7309 | 0.7298 | | 0.51 | 34.78 | 7200 | 0.5412 | 0.7326 | 0.7313 | | 0.5122 | 35.75 | 7400 | 0.5335 | 0.7362 | 0.7346 | | 0.508 | 36.71 | 7600 | 0.5288 | 0.7417 | 0.7401 | | 0.509 | 37.68 | 7800 | 0.5311 | 0.7423 | 0.7407 | | 0.5105 | 38.65 | 8000 | 0.5237 | 0.7482 | 0.7464 | | 0.5139 | 39.61 | 8200 | 0.5312 | 0.7398 | 0.7383 | | 0.5052 | 40.58 | 8400 | 0.5363 | 0.7345 | 0.7331 | | 0.5068 | 41.55 | 8600 | 0.5293 | 0.7438 | 0.7422 | | 0.5084 | 42.51 | 8800 | 0.5338 | 0.7380 | 0.7365 | | 0.5113 | 43.48 | 9000 | 0.5397 | 0.7341 | 0.7328 | | 0.5068 | 44.44 | 9200 | 0.5338 | 0.7383 | 0.7368 | | 0.5112 | 45.41 | 9400 | 0.5303 | 0.7402 | 0.7386 | | 0.504 | 46.38 | 9600 | 0.5351 | 0.7373 | 0.7359 | | 0.5109 | 47.34 | 9800 | 0.5327 | 0.7380 | 0.7365 | | 0.5066 | 48.31 | 10000 | 0.5302 | 0.7408 | 0.7392 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f

2024-05-03T17:30:56+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_15M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.5179 * F1 Score: 0.7401 * Accuracy: 0.7398 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

amuseix/w2v-bert-2.0-bulgarian-CV17.0-FLEURS

2024-05-03T17:31:10+00:00

TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

transformers

# Uploaded model - **Developed by:** eugeniosegala - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

eugeniosegala/model

2024-05-03T17:33:07+00:00

TAGS #transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: eugeniosegala - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

text-generation

transformers

<br/><br/> 8bpw/h8 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:** ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 **prompting format** Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-8B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}

JayhC/Llama-3-Lumimaid-8B-v0.1-8bpw-h8-exl2

2024-05-03T17:33:49+00:00

TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

<br/><br/> 8bpw/h8 exl2 quantization of NeverSleep/Llama-3-Lumimaid-8B-v0.1 using default exllamav2 calibration dataset. --- ORIGINAL CARD: ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Llama3 prompting format Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our Discord Server. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-8B-v0.1. Switch: 8B - 70B - 70B-alt ## Training data used: - Aesir datasets - NoRobots - limarp - 8k ctx - toxic-dpo-v0.1-sharegpt - ToxicQAFinal - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - Squish42/bluemoon-fandom-1-1-rp-cleaned - 50% (randomly) - NobodyExistsOnTheInternet/PIPPAsharegptv2test - 5% (randomly) - cgato/SlimOrcaDedupCleaned - 5% (randomly) - Airoboros (reduced) - Capybara (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek

peft

<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5143 - F1 Score: 0.7518 - Accuracy: 0.7507 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6069 | 0.97 | 200 | 0.5639 | 0.7202 | 0.7189 | | 0.5659 | 1.93 | 400 | 0.5476 | 0.7285 | 0.7271 | | 0.5461 | 2.9 | 600 | 0.5649 | 0.7130 | 0.7123 | | 0.5404 | 3.86 | 800 | 0.5287 | 0.7451 | 0.7440 | | 0.5311 | 4.83 | 1000 | 0.5559 | 0.7271 | 0.7259 | | 0.5299 | 5.8 | 1200 | 0.5429 | 0.7335 | 0.7319 | | 0.5247 | 6.76 | 1400 | 0.5236 | 0.7494 | 0.7477 | | 0.5209 | 7.73 | 1600 | 0.5307 | 0.7500 | 0.7483 | | 0.5164 | 8.7 | 1800 | 0.5279 | 0.7429 | 0.7413 | | 0.5144 | 9.66 | 2000 | 0.5352 | 0.7374 | 0.7359 | | 0.5133 | 10.63 | 2200 | 0.5353 | 0.7373 | 0.7359 | | 0.5063 | 11.59 | 2400 | 0.5131 | 0.7591 | 0.7576 | | 0.5103 | 12.56 | 2600 | 0.5332 | 0.7430 | 0.7416 | | 0.5067 | 13.53 | 2800 | 0.5274 | 0.7448 | 0.7434 | | 0.5053 | 14.49 | 3000 | 0.5314 | 0.7414 | 0.7401 | | 0.4984 | 15.46 | 3200 | 0.5152 | 0.7564 | 0.7549 | | 0.5017 | 16.43 | 3400 | 0.5355 | 0.7398 | 0.7386 | | 0.5011 | 17.39 | 3600 | 0.5153 | 0.7557 | 0.7540 | | 0.4956 | 18.36 | 3800 | 0.5074 | 0.7642 | 0.7634 | | 0.4947 | 19.32 | 4000 | 0.5103 | 0.7619 | 0.7604 | | 0.4904 | 20.29 | 4200 | 0.5248 | 0.7575 | 0.7558 | | 0.4948 | 21.26 | 4400 | 0.5249 | 0.7508 | 0.7492 | | 0.4924 | 22.22 | 4600 | 0.5366 | 0.7369 | 0.7359 | | 0.4933 | 23.19 | 4800 | 0.5116 | 0.7598 | 0.7582 | | 0.4892 | 24.15 | 5000 | 0.5158 | 0.7530 | 0.7513 | | 0.4868 | 25.12 | 5200 | 0.5430 | 0.7402 | 0.7392 | | 0.4865 | 26.09 | 5400 | 0.5305 | 0.7469 | 0.7455 | | 0.4888 | 27.05 | 5600 | 0.5468 | 0.7348 | 0.7340 | | 0.4838 | 28.02 | 5800 | 0.5166 | 0.7548 | 0.7531 | | 0.4852 | 28.99 | 6000 | 0.5230 | 0.7511 | 0.7495 | | 0.4821 | 29.95 | 6200 | 0.5328 | 0.7448 | 0.7434 | | 0.4827 | 30.92 | 6400 | 0.5079 | 0.7651 | 0.7637 | | 0.4839 | 31.88 | 6600 | 0.5158 | 0.7536 | 0.7519 | | 0.4765 | 32.85 | 6800 | 0.5259 | 0.7498 | 0.7483 | | 0.4826 | 33.82 | 7000 | 0.5297 | 0.7448 | 0.7434 | | 0.4768 | 34.78 | 7200 | 0.5302 | 0.7472 | 0.7458 | | 0.481 | 35.75 | 7400 | 0.5245 | 0.7505 | 0.7489 | | 0.4745 | 36.71 | 7600 | 0.5234 | 0.7523 | 0.7507 | | 0.4762 | 37.68 | 7800 | 0.5197 | 0.7526 | 0.7510 | | 0.4771 | 38.65 | 8000 | 0.5158 | 0.7521 | 0.7504 | | 0.4792 | 39.61 | 8200 | 0.5203 | 0.7526 | 0.7510 | | 0.4711 | 40.58 | 8400 | 0.5316 | 0.7458 | 0.7443 | | 0.4719 | 41.55 | 8600 | 0.5230 | 0.7523 | 0.7507 | | 0.4748 | 42.51 | 8800 | 0.5263 | 0.7511 | 0.7495 | | 0.4772 | 43.48 | 9000 | 0.5299 | 0.7468 | 0.7452 | | 0.4734 | 44.44 | 9200 | 0.5273 | 0.7502 | 0.7486 | | 0.478 | 45.41 | 9400 | 0.5242 | 0.7502 | 0.7486 | | 0.4673 | 46.38 | 9600 | 0.5285 | 0.7480 | 0.7464 | | 0.4758 | 47.34 | 9800 | 0.5244 | 0.7505 | 0.7489 | | 0.4699 | 48.31 | 10000 | 0.5224 | 0.7511 | 0.7495 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f

2024-05-03T17:33:59+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_15M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.5143 * F1 Score: 0.7518 * Accuracy: 0.7507 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

  # Llama-3-Giraffe-70B-Instruct Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant! This model has an effective context length of approximately 128k. We have currently trained on ~1.5B tokens. There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in:  ## Training Methodology The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation. ### NTK-scaling The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments. ### PoSE We utilise Positional Skip-wise Training (PoSE) with the following parameters: - **Number of Chunks**: 5 - **Max position ID**: 32768 ### Data We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data). ### Hardware We train on 8xH100 GPUs with Deepspeed Zero Stage 3. ## Evaluation Methodology We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B. We evaluate with the following parameters: - **Min context length**: 2000 - **Max context length**: 128000 - **Context interval**: 4000 - **Depth interval**: 0.1 - **Num samples**: 2 - **Rnd number digits**: 7 - **Haystack dir**: PaulGrahamEssays ### Adapter Transfer We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we release the merged version here.

{"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"}

abacusai/Llama-3-Giraffe-70B-Instruct

2024-05-03T17:34:02+00:00

TAGS #transformers #safetensors #llama #text-generation #meta #llama-3 #conversational #en #arxiv-2309.10400 #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

!image/png !image/png # Llama-3-Giraffe-70B-Instruct Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant! This model has an effective context length of approximately 128k. We have currently trained on ~1.5B tokens. There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in: !image/png ## Training Methodology The methodology for training uses PoSE and dynamic-NTK interpolation. ### NTK-scaling The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments. ### PoSE We utilise Positional Skip-wise Training (PoSE) with the following parameters: - Number of Chunks: 5 - Max position ID: 32768 ### Data We use on average ~8K long samples from RedPajama. ### Hardware We train on 8xH100 GPUs with Deepspeed Zero Stage 3. ## Evaluation Methodology We use the EasyContext implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B. We evaluate with the following parameters: - Min context length: 2000 - Max context length: 128000 - Context interval: 4000 - Depth interval: 0.1 - Num samples: 2 - Rnd number digits: 7 - Haystack dir: PaulGrahamEssays ### Adapter Transfer We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we release the merged version here.

peft

<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6021 - F1 Score: 0.6656 - Accuracy: 0.6667 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6627 | 1.04 | 200 | 0.6377 | 0.5711 | 0.6331 | | 0.6293 | 2.08 | 400 | 0.6279 | 0.6475 | 0.6471 | | 0.6201 | 3.12 | 600 | 0.6184 | 0.6407 | 0.6641 | | 0.6192 | 4.17 | 800 | 0.6200 | 0.6465 | 0.6536 | | 0.6176 | 5.21 | 1000 | 0.6203 | 0.6515 | 0.6540 | | 0.6144 | 6.25 | 1200 | 0.6163 | 0.6488 | 0.6543 | | 0.609 | 7.29 | 1400 | 0.6247 | 0.6541 | 0.6533 | | 0.6101 | 8.33 | 1600 | 0.6215 | 0.6542 | 0.6543 | | 0.6095 | 9.38 | 1800 | 0.6298 | 0.6494 | 0.6471 | | 0.6088 | 10.42 | 2000 | 0.6181 | 0.6592 | 0.6598 | | 0.6097 | 11.46 | 2200 | 0.6094 | 0.6533 | 0.6628 | | 0.6028 | 12.5 | 2400 | 0.6121 | 0.6578 | 0.6621 | | 0.6016 | 13.54 | 2600 | 0.6112 | 0.6534 | 0.6611 | | 0.6037 | 14.58 | 2800 | 0.6100 | 0.6536 | 0.6605 | | 0.6058 | 15.62 | 3000 | 0.6102 | 0.6558 | 0.6621 | | 0.6011 | 16.67 | 3200 | 0.6148 | 0.6607 | 0.6621 | | 0.6004 | 17.71 | 3400 | 0.6086 | 0.6574 | 0.6644 | | 0.6031 | 18.75 | 3600 | 0.6099 | 0.6617 | 0.6660 | | 0.6016 | 19.79 | 3800 | 0.6130 | 0.6658 | 0.6680 | | 0.5948 | 20.83 | 4000 | 0.6156 | 0.6632 | 0.6637 | | 0.6 | 21.88 | 4200 | 0.6166 | 0.6623 | 0.6631 | | 0.5969 | 22.92 | 4400 | 0.6148 | 0.6644 | 0.6657 | | 0.5979 | 23.96 | 4600 | 0.6176 | 0.6650 | 0.6650 | | 0.5961 | 25.0 | 4800 | 0.6084 | 0.6649 | 0.6699 | | 0.594 | 26.04 | 5000 | 0.6150 | 0.6680 | 0.6689 | | 0.5947 | 27.08 | 5200 | 0.6137 | 0.6665 | 0.6676 | | 0.5937 | 28.12 | 5400 | 0.6101 | 0.6647 | 0.6676 | | 0.5947 | 29.17 | 5600 | 0.6156 | 0.6682 | 0.6683 | | 0.5904 | 30.21 | 5800 | 0.6164 | 0.6698 | 0.6699 | | 0.5929 | 31.25 | 6000 | 0.6136 | 0.6693 | 0.6699 | | 0.5924 | 32.29 | 6200 | 0.6135 | 0.6682 | 0.6689 | | 0.5925 | 33.33 | 6400 | 0.6170 | 0.6693 | 0.6693 | | 0.5933 | 34.38 | 6600 | 0.6090 | 0.6683 | 0.6719 | | 0.5905 | 35.42 | 6800 | 0.6095 | 0.6691 | 0.6722 | | 0.5904 | 36.46 | 7000 | 0.6083 | 0.6705 | 0.6742 | | 0.5866 | 37.5 | 7200 | 0.6134 | 0.6711 | 0.6719 | | 0.5887 | 38.54 | 7400 | 0.6110 | 0.6729 | 0.6748 | | 0.5927 | 39.58 | 7600 | 0.6105 | 0.6705 | 0.6725 | | 0.5898 | 40.62 | 7800 | 0.6198 | 0.6666 | 0.6654 | | 0.5882 | 41.67 | 8000 | 0.6124 | 0.6703 | 0.6709 | | 0.5878 | 42.71 | 8200 | 0.6088 | 0.6686 | 0.6729 | | 0.5902 | 43.75 | 8400 | 0.6109 | 0.6714 | 0.6729 | | 0.5885 | 44.79 | 8600 | 0.6156 | 0.6702 | 0.6699 | | 0.5862 | 45.83 | 8800 | 0.6122 | 0.6709 | 0.6722 | | 0.5905 | 46.88 | 9000 | 0.6144 | 0.6695 | 0.6696 | | 0.5869 | 47.92 | 9200 | 0.6138 | 0.6689 | 0.6693 | | 0.5888 | 48.96 | 9400 | 0.6124 | 0.6695 | 0.6706 | | 0.5884 | 50.0 | 9600 | 0.6128 | 0.6682 | 0.6689 | | 0.5867 | 51.04 | 9800 | 0.6131 | 0.6699 | 0.6706 | | 0.5862 | 52.08 | 10000 | 0.6128 | 0.6678 | 0.6686 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f

2024-05-03T17:34:02+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_15M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.6021 * F1 Score: 0.6656 * Accuracy: 0.6667 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

multiple-choice

transformers

<!-- 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. --> # fine_tuned_copa_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.51 - F1: 0.4857 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7257 | 1.0 | 50 | 0.6931 | 0.49 | 0.4838 | | 0.7001 | 2.0 | 100 | 0.6931 | 0.48 | 0.48 | | 0.7196 | 3.0 | 150 | 0.6931 | 0.52 | 0.4603 | | 0.6895 | 4.0 | 200 | 0.6931 | 0.5 | 0.4926 | | 0.745 | 5.0 | 250 | 0.6931 | 0.46 | 0.4244 | | 0.7102 | 6.0 | 300 | 0.6931 | 0.5 | 0.4861 | | 0.7245 | 7.0 | 350 | 0.6931 | 0.55 | 0.5391 | | 0.7283 | 8.0 | 400 | 0.6931 | 0.51 | 0.4857 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_copa_croslo", "results": []}]}

lenatr99/fine_tuned_copa_croslo

2024-05-03T17:35:12+00:00

TAGS #transformers #safetensors #bert #multiple-choice #generated_from_trainer #base_model-EMBEDDIA/crosloengual-bert #license-cc-by-4.0 #endpoints_compatible #region-us

fine\_tuned\_copa\_croslo ========================= This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6931 * Accuracy: 0.51 * F1: 0.4857 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: 0.003 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Steelskull/L3-Arcania-4x8b

{}

mradermacher/L3-Arcania-4x8b-GGUF

2024-05-03T17:36:03+00:00

TAGS #gguf #region-us

static quants of URL

peft

<!-- 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. --> # loha_fine_tuned_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6890 - Accuracy: 0.52 - F1: 0.5212 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7072 | 1.0 | 50 | 0.6896 | 0.52 | 0.5212 | | 0.6973 | 2.0 | 100 | 0.6894 | 0.53 | 0.5312 | | 0.6988 | 3.0 | 150 | 0.6892 | 0.54 | 0.5411 | | 0.7016 | 4.0 | 200 | 0.6891 | 0.53 | 0.5312 | | 0.7034 | 5.0 | 250 | 0.6890 | 0.52 | 0.5212 | | 0.6978 | 6.0 | 300 | 0.6890 | 0.51 | 0.5112 | | 0.6965 | 7.0 | 350 | 0.6890 | 0.51 | 0.5112 | | 0.6907 | 8.0 | 400 | 0.6890 | 0.52 | 0.5212 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_croslo", "results": []}]}

lenatr99/loha_fine_tuned_croslo

2024-05-03T17:36:50+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-EMBEDDIA/crosloengual-bert #license-cc-by-4.0 #region-us

loha\_fine\_tuned\_croslo ========================= This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6890 * Accuracy: 0.52 * F1: 0.5212 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: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

robotics

# Introduction This model is currently being tested, further details to be added in future for now see [robot_learning_baselines](https://github.com/peterdavidfagan/robot_learning_baselines).

{"license": "apache-2.0", "datasets": ["peterdavidfagan/transporter_networks"], "pipeline_tag": "robotics"}

peterdavidfagan/transporter_networks

2024-05-03T17:38:29+00:00

TAGS #tflite #robotics #dataset-peterdavidfagan/transporter_networks #license-apache-2.0 #region-us

# Introduction This model is currently being tested, further details to be added in future for now see robot_learning_baselines.

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": ["unsloth"]}

sparvekar/critique_lora_model

2024-05-03T17:38:51+00:00

TAGS #transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <|im_start|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<|im_end|> <|im_start|>user Ki a legerősebb szuperhős?<|im_end|> <|im_start|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<|im_end|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}

ariel-ml/PULI-LlumiX-32K-instruct-lora

2024-05-03T17:39:54+00:00

TAGS #transformers #safetensors #puli #text-generation-inference #unsloth #llama #trl #finetuned #text-generation #conversational #hu #en #dataset-boapps/szurkemarha #base_model-NYTK/PULI-LlumiX-32K #license-llama2 #endpoints_compatible #region-us

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform Lightning AI Studio L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ## Base model - Trained with OpenChatKit github - The LLaMA-2-7B-32K model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

text-generation

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/g2rr5al

2024-05-03T17:40:20+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <|im_start|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<|im_end|> <|im_start|>user Ki a legerősebb szuperhős?<|im_end|> <|im_start|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<|im_end|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}

ariel-ml/PULI-LlumiX-32K-instruct-f16

2024-05-03T17:40:22+00:00

TAGS #transformers #safetensors #llama #text-generation #puli #text-generation-inference #unsloth #trl #finetuned #conversational #custom_code #hu #en #dataset-boapps/szurkemarha #base_model-NYTK/PULI-LlumiX-32K #license-llama2 #autotrain_compatible #endpoints_compatible #region-us

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform Lightning AI Studio L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ## Base model - Trained with OpenChatKit github - The LLaMA-2-7B-32K model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

text-generation

transformers

# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Jayant9928/orpo_med_v3](https://huggingface.co/Jayant9928/orpo_med_v3) as a base. ### Models Merged The following models were included in the merge: * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Jayant9928/orpo_med_v3 parameters: density: 0.53 weight: 0.4 - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: Jayant9928/orpo_med_v3 tokenizer_source: union parameters: int8_mask: true dtype: float16 ```

{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Jayant9928/orpo_med_v3", "meta-llama/Meta-Llama-3-8B-Instruct"]}

Muhammad2003/Dmitry69

2024-05-03T17:40:29+00:00

TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2311.03099 #arxiv-2306.01708 #base_model-Jayant9928/orpo_med_v3 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the DARE TIES merge method using Jayant9928/orpo_med_v3 as a base. ### Models Merged The following models were included in the merge: * meta-llama/Meta-Llama-3-8B-Instruct ### Configuration The following YAML configuration was used to produce this model:

peft

<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5137 - F1 Score: 0.7465 - Accuracy: 0.7452 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5962 | 0.97 | 200 | 0.5550 | 0.7252 | 0.7241 | | 0.5522 | 1.93 | 400 | 0.5384 | 0.7392 | 0.7377 | | 0.5347 | 2.9 | 600 | 0.5523 | 0.7224 | 0.7213 | | 0.5295 | 3.86 | 800 | 0.5225 | 0.7510 | 0.7495 | | 0.5188 | 4.83 | 1000 | 0.5540 | 0.7280 | 0.7271 | | 0.5171 | 5.8 | 1200 | 0.5321 | 0.7383 | 0.7368 | | 0.5098 | 6.76 | 1400 | 0.5174 | 0.7512 | 0.7495 | | 0.5041 | 7.73 | 1600 | 0.5242 | 0.7454 | 0.7437 | | 0.499 | 8.7 | 1800 | 0.5289 | 0.7457 | 0.7443 | | 0.4945 | 9.66 | 2000 | 0.5280 | 0.7488 | 0.7474 | | 0.4928 | 10.63 | 2200 | 0.5247 | 0.7495 | 0.7480 | | 0.4843 | 11.59 | 2400 | 0.5053 | 0.7654 | 0.7640 | | 0.4847 | 12.56 | 2600 | 0.5261 | 0.7461 | 0.7446 | | 0.4807 | 13.53 | 2800 | 0.5256 | 0.7504 | 0.7489 | | 0.478 | 14.49 | 3000 | 0.5253 | 0.7434 | 0.7419 | | 0.4683 | 15.46 | 3200 | 0.5126 | 0.7638 | 0.7622 | | 0.4695 | 16.43 | 3400 | 0.5248 | 0.7485 | 0.7470 | | 0.4665 | 17.39 | 3600 | 0.5196 | 0.7578 | 0.7561 | | 0.4595 | 18.36 | 3800 | 0.5050 | 0.7626 | 0.7619 | | 0.4571 | 19.32 | 4000 | 0.5115 | 0.7579 | 0.7564 | | 0.4522 | 20.29 | 4200 | 0.5346 | 0.7557 | 0.7540 | | 0.4557 | 21.26 | 4400 | 0.5250 | 0.7566 | 0.7549 | | 0.449 | 22.22 | 4600 | 0.5417 | 0.7443 | 0.7431 | | 0.4484 | 23.19 | 4800 | 0.5210 | 0.7545 | 0.7528 | | 0.4437 | 24.15 | 5000 | 0.5327 | 0.7544 | 0.7528 | | 0.4398 | 25.12 | 5200 | 0.5487 | 0.7435 | 0.7425 | | 0.4388 | 26.09 | 5400 | 0.5419 | 0.7453 | 0.7440 | | 0.4372 | 27.05 | 5600 | 0.5656 | 0.7427 | 0.7416 | | 0.4307 | 28.02 | 5800 | 0.5400 | 0.7533 | 0.7516 | | 0.429 | 28.99 | 6000 | 0.5285 | 0.7539 | 0.7522 | | 0.4243 | 29.95 | 6200 | 0.5554 | 0.7452 | 0.7437 | | 0.4249 | 30.92 | 6400 | 0.5254 | 0.7546 | 0.7534 | | 0.426 | 31.88 | 6600 | 0.5293 | 0.7494 | 0.7477 | | 0.4144 | 32.85 | 6800 | 0.5486 | 0.7502 | 0.7486 | | 0.4206 | 33.82 | 7000 | 0.5444 | 0.7498 | 0.7483 | | 0.4113 | 34.78 | 7200 | 0.5544 | 0.7529 | 0.7513 | | 0.4185 | 35.75 | 7400 | 0.5436 | 0.7481 | 0.7464 | | 0.4096 | 36.71 | 7600 | 0.5489 | 0.7499 | 0.7483 | | 0.4124 | 37.68 | 7800 | 0.5416 | 0.7554 | 0.7537 | | 0.4109 | 38.65 | 8000 | 0.5439 | 0.7488 | 0.7470 | | 0.4081 | 39.61 | 8200 | 0.5420 | 0.7506 | 0.7489 | | 0.4018 | 40.58 | 8400 | 0.5606 | 0.7492 | 0.7477 | | 0.4028 | 41.55 | 8600 | 0.5520 | 0.7524 | 0.7507 | | 0.4059 | 42.51 | 8800 | 0.5511 | 0.7539 | 0.7522 | | 0.4061 | 43.48 | 9000 | 0.5581 | 0.7514 | 0.7498 | | 0.4036 | 44.44 | 9200 | 0.5532 | 0.7521 | 0.7504 | | 0.408 | 45.41 | 9400 | 0.5504 | 0.7551 | 0.7534 | | 0.3953 | 46.38 | 9600 | 0.5564 | 0.7517 | 0.7501 | | 0.4054 | 47.34 | 9800 | 0.5496 | 0.7512 | 0.7495 | | 0.3949 | 48.31 | 10000 | 0.5515 | 0.7527 | 0.7510 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f

2024-05-03T17:44:41+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_15M-L32\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.5137 * F1 Score: 0.7465 * Accuracy: 0.7452 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}

BotCuddles/gemma-2b-it-ft-mental

2024-05-03T17:44:46+00:00

TAGS #transformers #safetensors #gemma #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5973 - F1 Score: 0.6642 - Accuracy: 0.6693 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6513 | 1.04 | 200 | 0.6213 | 0.6317 | 0.6549 | | 0.6199 | 2.08 | 400 | 0.6293 | 0.6443 | 0.6426 | | 0.6123 | 3.12 | 600 | 0.6108 | 0.6493 | 0.6680 | | 0.6106 | 4.17 | 800 | 0.6176 | 0.6585 | 0.6588 | | 0.6064 | 5.21 | 1000 | 0.6064 | 0.6673 | 0.6758 | | 0.6033 | 6.25 | 1200 | 0.6056 | 0.6679 | 0.6738 | | 0.5957 | 7.29 | 1400 | 0.6180 | 0.6640 | 0.6624 | | 0.5959 | 8.33 | 1600 | 0.6186 | 0.6685 | 0.6667 | | 0.5932 | 9.38 | 1800 | 0.6305 | 0.6574 | 0.6549 | | 0.5919 | 10.42 | 2000 | 0.6082 | 0.6766 | 0.6774 | | 0.593 | 11.46 | 2200 | 0.6020 | 0.6772 | 0.6826 | | 0.5848 | 12.5 | 2400 | 0.6121 | 0.6768 | 0.6758 | | 0.5841 | 13.54 | 2600 | 0.6098 | 0.6725 | 0.6729 | | 0.5834 | 14.58 | 2800 | 0.6081 | 0.6715 | 0.6719 | | 0.5864 | 15.62 | 3000 | 0.6126 | 0.6760 | 0.6751 | | 0.5818 | 16.67 | 3200 | 0.6155 | 0.6718 | 0.6699 | | 0.5802 | 17.71 | 3400 | 0.6068 | 0.6744 | 0.6751 | | 0.5828 | 18.75 | 3600 | 0.6077 | 0.6713 | 0.6719 | | 0.5803 | 19.79 | 3800 | 0.6130 | 0.6742 | 0.6735 | | 0.5743 | 20.83 | 4000 | 0.6197 | 0.6699 | 0.6680 | | 0.5769 | 21.88 | 4200 | 0.6318 | 0.6626 | 0.6601 | | 0.5746 | 22.92 | 4400 | 0.6185 | 0.6679 | 0.6663 | | 0.5741 | 23.96 | 4600 | 0.6256 | 0.6661 | 0.6637 | | 0.5728 | 25.0 | 4800 | 0.6091 | 0.6691 | 0.6693 | | 0.5694 | 26.04 | 5000 | 0.6206 | 0.6678 | 0.6660 | | 0.5706 | 27.08 | 5200 | 0.6181 | 0.6659 | 0.6644 | | 0.5682 | 28.12 | 5400 | 0.6203 | 0.6699 | 0.6680 | | 0.5684 | 29.17 | 5600 | 0.6188 | 0.6727 | 0.6716 | | 0.5626 | 30.21 | 5800 | 0.6244 | 0.6680 | 0.6663 | | 0.5659 | 31.25 | 6000 | 0.6298 | 0.6645 | 0.6621 | | 0.5652 | 32.29 | 6200 | 0.6119 | 0.6672 | 0.6667 | | 0.565 | 33.33 | 6400 | 0.6228 | 0.6646 | 0.6628 | | 0.5636 | 34.38 | 6600 | 0.6187 | 0.6672 | 0.6663 | | 0.5624 | 35.42 | 6800 | 0.6183 | 0.6671 | 0.6660 | | 0.5631 | 36.46 | 7000 | 0.6131 | 0.6729 | 0.6729 | | 0.5575 | 37.5 | 7200 | 0.6277 | 0.6620 | 0.6601 | | 0.5588 | 38.54 | 7400 | 0.6218 | 0.6689 | 0.6680 | | 0.5624 | 39.58 | 7600 | 0.6139 | 0.6722 | 0.6722 | | 0.56 | 40.62 | 7800 | 0.6328 | 0.6586 | 0.6562 | | 0.5583 | 41.67 | 8000 | 0.6191 | 0.6650 | 0.6634 | | 0.5563 | 42.71 | 8200 | 0.6189 | 0.6708 | 0.6706 | | 0.5599 | 43.75 | 8400 | 0.6180 | 0.6674 | 0.6663 | | 0.5572 | 44.79 | 8600 | 0.6239 | 0.6643 | 0.6624 | | 0.5543 | 45.83 | 8800 | 0.6204 | 0.6676 | 0.6670 | | 0.5576 | 46.88 | 9000 | 0.6294 | 0.6597 | 0.6575 | | 0.5544 | 47.92 | 9200 | 0.6281 | 0.6599 | 0.6579 | | 0.555 | 48.96 | 9400 | 0.6271 | 0.6637 | 0.6621 | | 0.555 | 50.0 | 9600 | 0.6273 | 0.6652 | 0.6634 | | 0.5544 | 51.04 | 9800 | 0.6263 | 0.6641 | 0.6624 | | 0.5523 | 52.08 | 10000 | 0.6264 | 0.6642 | 0.6624 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f

2024-05-03T17:44:48+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_15M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.5973 * F1 Score: 0.6642 * Accuracy: 0.6693 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

golf2248/ox9od86

2024-05-03T17:44:59+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

<!-- 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. --> # sft-fsi-masked-loss This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3788 ## 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 17.1473 | 0.5333 | 1 | 18.7762 | | 17.1473 | 1.6 | 3 | 9.0847 | | 11.7791 | 2.6667 | 5 | 3.0550 | | 11.7791 | 3.7333 | 7 | 0.6706 | | 11.7791 | 4.8 | 9 | 0.6697 | | 1.4045 | 5.8667 | 11 | 0.5653 | | 1.4045 | 6.9333 | 13 | 0.4982 | | 0.6622 | 8.0 | 15 | 0.4756 | | 0.6622 | 8.5333 | 16 | 0.4777 | | 0.6622 | 9.6 | 18 | 0.4338 | | 0.5586 | 10.6667 | 20 | 0.3788 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["trl", "sft", "generated_from_trainer"], "model-index": [{"name": "sft-fsi-masked-loss", "results": []}]}

jamesoneill12/sft-fsi-masked-loss

2024-05-03T17:45:22+00:00

TAGS #transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

sft-fsi-masked-loss =================== This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.3788 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: 2e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 8 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 256 * total\_eval\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

text2text-generation

transformers

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

{"library_name": "transformers", "tags": []}

Ragab167/m2m_translation_v2

2024-05-03T17:45:31+00:00

TAGS #transformers #safetensors #m2m_100 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

cilantro9246/vlrcskl

2024-05-03T17:45:32+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

<br/><br/> 3bpw/h6 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:** ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 **prompting format** Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}

JayhC/Llama-3-Lumimaid-70B-v0.1-3bpw-h6-exl2

2024-05-03T17:45:41+00:00

TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us

<br/><br/> 3bpw/h6 exl2 quantization of NeverSleep/Llama-3-Lumimaid-70B-v0.1 using default exllamav2 calibration dataset. --- ORIGINAL CARD: ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Llama3 prompting format Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our Discord Server. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: 8B - 70B - 70B-alt ## Training data used: - Aesir datasets - NoRobots - limarp - 8k ctx - toxic-dpo-v0.1-sharegpt - ToxicQAFinal - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - Squish42/bluemoon-fandom-1-1-rp-cleaned - 50% (randomly) - NobodyExistsOnTheInternet/PIPPAsharegptv2test - 5% (randomly) - cgato/SlimOrcaDedupCleaned - 5% (randomly) - Airoboros (reduced) - Capybara (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek

peft

<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6006 - F1 Score: 0.6727 - Accuracy: 0.6748 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6452 | 1.04 | 200 | 0.6178 | 0.6311 | 0.6566 | | 0.6153 | 2.08 | 400 | 0.6301 | 0.6434 | 0.6409 | | 0.6065 | 3.12 | 600 | 0.6049 | 0.6694 | 0.6804 | | 0.603 | 4.17 | 800 | 0.6251 | 0.6584 | 0.6559 | | 0.5966 | 5.21 | 1000 | 0.6064 | 0.6686 | 0.6755 | | 0.5941 | 6.25 | 1200 | 0.6047 | 0.6722 | 0.6745 | | 0.5852 | 7.29 | 1400 | 0.6158 | 0.6688 | 0.6673 | | 0.5845 | 8.33 | 1600 | 0.6197 | 0.6646 | 0.6624 | | 0.5814 | 9.38 | 1800 | 0.6251 | 0.6585 | 0.6559 | | 0.5764 | 10.42 | 2000 | 0.6045 | 0.6777 | 0.6804 | | 0.5778 | 11.46 | 2200 | 0.6041 | 0.6711 | 0.6758 | | 0.5668 | 12.5 | 2400 | 0.6185 | 0.6726 | 0.6722 | | 0.5644 | 13.54 | 2600 | 0.6260 | 0.6671 | 0.6657 | | 0.5642 | 14.58 | 2800 | 0.6139 | 0.6665 | 0.6670 | | 0.5637 | 15.62 | 3000 | 0.6193 | 0.6636 | 0.6631 | | 0.5576 | 16.67 | 3200 | 0.6239 | 0.6590 | 0.6579 | | 0.5523 | 17.71 | 3400 | 0.6274 | 0.6560 | 0.6546 | | 0.556 | 18.75 | 3600 | 0.6327 | 0.6570 | 0.6553 | | 0.5516 | 19.79 | 3800 | 0.6394 | 0.6645 | 0.6628 | | 0.5438 | 20.83 | 4000 | 0.6292 | 0.6633 | 0.6621 | | 0.5438 | 21.88 | 4200 | 0.6535 | 0.6475 | 0.6448 | | 0.5386 | 22.92 | 4400 | 0.6413 | 0.6594 | 0.6579 | | 0.5357 | 23.96 | 4600 | 0.6465 | 0.6519 | 0.6497 | | 0.5325 | 25.0 | 4800 | 0.6459 | 0.6539 | 0.6517 | | 0.5274 | 26.04 | 5000 | 0.6459 | 0.6504 | 0.6484 | | 0.526 | 27.08 | 5200 | 0.6466 | 0.6535 | 0.6520 | | 0.523 | 28.12 | 5400 | 0.6561 | 0.6495 | 0.6471 | | 0.5191 | 29.17 | 5600 | 0.6623 | 0.6535 | 0.6514 | | 0.5115 | 30.21 | 5800 | 0.6637 | 0.6552 | 0.6533 | | 0.5137 | 31.25 | 6000 | 0.6703 | 0.6423 | 0.6396 | | 0.5119 | 32.29 | 6200 | 0.6508 | 0.6502 | 0.6487 | | 0.5088 | 33.33 | 6400 | 0.6721 | 0.6439 | 0.6413 | | 0.5057 | 34.38 | 6600 | 0.6668 | 0.6495 | 0.6491 | | 0.5043 | 35.42 | 6800 | 0.6701 | 0.6503 | 0.6481 | | 0.506 | 36.46 | 7000 | 0.6517 | 0.6510 | 0.6497 | | 0.4961 | 37.5 | 7200 | 0.6784 | 0.6473 | 0.6452 | | 0.4929 | 38.54 | 7400 | 0.6843 | 0.6489 | 0.6471 | | 0.4942 | 39.58 | 7600 | 0.6631 | 0.6505 | 0.6510 | | 0.4938 | 40.62 | 7800 | 0.6954 | 0.6413 | 0.6386 | | 0.4898 | 41.67 | 8000 | 0.6708 | 0.6492 | 0.6474 | | 0.4866 | 42.71 | 8200 | 0.6798 | 0.6518 | 0.6504 | | 0.4901 | 43.75 | 8400 | 0.6709 | 0.6427 | 0.6413 | | 0.4866 | 44.79 | 8600 | 0.6799 | 0.6513 | 0.6494 | | 0.4819 | 45.83 | 8800 | 0.6798 | 0.6502 | 0.6494 | | 0.4847 | 46.88 | 9000 | 0.6948 | 0.6396 | 0.6370 | | 0.4809 | 47.92 | 9200 | 0.6960 | 0.6417 | 0.6393 | | 0.4816 | 48.96 | 9400 | 0.6919 | 0.6486 | 0.6468 | | 0.482 | 50.0 | 9600 | 0.6903 | 0.6467 | 0.6445 | | 0.4792 | 51.04 | 9800 | 0.6937 | 0.6468 | 0.6445 | | 0.4762 | 52.08 | 10000 | 0.6930 | 0.6458 | 0.6435 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f

2024-05-03T17:45:43+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_15M-L32\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.6006 * F1 Score: 0.6727 * Accuracy: 0.6748 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4994 - F1 Score: 0.7632 - Accuracy: 0.7625 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6178 | 1.15 | 200 | 0.5812 | 0.7080 | 0.7074 | | 0.5678 | 2.3 | 400 | 0.6382 | 0.6436 | 0.6582 | | 0.5459 | 3.45 | 600 | 0.5881 | 0.7031 | 0.7071 | | 0.5401 | 4.6 | 800 | 0.5820 | 0.7005 | 0.7046 | | 0.5342 | 5.75 | 1000 | 0.5525 | 0.7202 | 0.7200 | | 0.5258 | 6.9 | 1200 | 0.5593 | 0.7173 | 0.7179 | | 0.5225 | 8.05 | 1400 | 0.5496 | 0.7250 | 0.7247 | | 0.5197 | 9.2 | 1600 | 0.5916 | 0.6860 | 0.6923 | | 0.5155 | 10.34 | 1800 | 0.5553 | 0.7197 | 0.7200 | | 0.5146 | 11.49 | 2000 | 0.5560 | 0.7191 | 0.7200 | | 0.508 | 12.64 | 2200 | 0.5824 | 0.7011 | 0.7053 | | 0.5132 | 13.79 | 2400 | 0.5530 | 0.7193 | 0.7211 | | 0.506 | 14.94 | 2600 | 0.5556 | 0.7127 | 0.7143 | | 0.504 | 16.09 | 2800 | 0.5451 | 0.7312 | 0.7316 | | 0.503 | 17.24 | 3000 | 0.5652 | 0.7205 | 0.7222 | | 0.4994 | 18.39 | 3200 | 0.5591 | 0.7246 | 0.7262 | | 0.5011 | 19.54 | 3400 | 0.5456 | 0.7289 | 0.7298 | | 0.497 | 20.69 | 3600 | 0.5430 | 0.7267 | 0.7269 | | 0.4967 | 21.84 | 3800 | 0.5407 | 0.7314 | 0.7319 | | 0.4947 | 22.99 | 4000 | 0.5471 | 0.7285 | 0.7290 | | 0.4959 | 24.14 | 4200 | 0.5297 | 0.7354 | 0.7352 | | 0.4894 | 25.29 | 4400 | 0.5519 | 0.7314 | 0.7319 | | 0.4965 | 26.44 | 4600 | 0.5460 | 0.7324 | 0.7326 | | 0.4902 | 27.59 | 4800 | 0.5525 | 0.7269 | 0.7280 | | 0.487 | 28.74 | 5000 | 0.5480 | 0.7240 | 0.7251 | | 0.4945 | 29.89 | 5200 | 0.5410 | 0.7337 | 0.7341 | | 0.4869 | 31.03 | 5400 | 0.5507 | 0.7291 | 0.7301 | | 0.4896 | 32.18 | 5600 | 0.5256 | 0.7396 | 0.7391 | | 0.4832 | 33.33 | 5800 | 0.5439 | 0.7342 | 0.7344 | | 0.4921 | 34.48 | 6000 | 0.5405 | 0.7330 | 0.7337 | | 0.4814 | 35.63 | 6200 | 0.5309 | 0.7376 | 0.7373 | | 0.4888 | 36.78 | 6400 | 0.5390 | 0.7330 | 0.7334 | | 0.4837 | 37.93 | 6600 | 0.5416 | 0.7329 | 0.7330 | | 0.4815 | 39.08 | 6800 | 0.5345 | 0.7384 | 0.7384 | | 0.4833 | 40.23 | 7000 | 0.5349 | 0.7385 | 0.7384 | | 0.486 | 41.38 | 7200 | 0.5310 | 0.7382 | 0.7380 | | 0.483 | 42.53 | 7400 | 0.5359 | 0.7330 | 0.7334 | | 0.4805 | 43.68 | 7600 | 0.5332 | 0.7385 | 0.7384 | | 0.4801 | 44.83 | 7800 | 0.5450 | 0.7309 | 0.7316 | | 0.4821 | 45.98 | 8000 | 0.5359 | 0.7349 | 0.7352 | | 0.4806 | 47.13 | 8200 | 0.5407 | 0.7325 | 0.7330 | | 0.4819 | 48.28 | 8400 | 0.5387 | 0.7352 | 0.7355 | | 0.4829 | 49.43 | 8600 | 0.5323 | 0.7389 | 0.7388 | | 0.4819 | 50.57 | 8800 | 0.5356 | 0.7366 | 0.7366 | | 0.48 | 51.72 | 9000 | 0.5423 | 0.7321 | 0.7326 | | 0.4766 | 52.87 | 9200 | 0.5446 | 0.7321 | 0.7326 | | 0.4815 | 54.02 | 9400 | 0.5420 | 0.7328 | 0.7334 | | 0.48 | 55.17 | 9600 | 0.5405 | 0.7329 | 0.7334 | | 0.476 | 56.32 | 9800 | 0.5379 | 0.7349 | 0.7352 | | 0.4808 | 57.47 | 10000 | 0.5386 | 0.7342 | 0.7344 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f

2024-05-03T17:45:55+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K9ac-seqsight\_4096\_512\_15M-L1\_f =============================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K9ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4994 * F1 Score: 0.7632 * Accuracy: 0.7625 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4829 - F1 Score: 0.7825 - Accuracy: 0.7819 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5985 | 1.15 | 200 | 0.5713 | 0.7153 | 0.7154 | | 0.5469 | 2.3 | 400 | 0.6351 | 0.6395 | 0.6564 | | 0.5227 | 3.45 | 600 | 0.5475 | 0.7292 | 0.7294 | | 0.5141 | 4.6 | 800 | 0.5387 | 0.7369 | 0.7373 | | 0.5053 | 5.75 | 1000 | 0.5242 | 0.7497 | 0.7492 | | 0.4998 | 6.9 | 1200 | 0.5305 | 0.7395 | 0.7391 | | 0.4963 | 8.05 | 1400 | 0.5248 | 0.7393 | 0.7388 | | 0.4931 | 9.2 | 1600 | 0.5473 | 0.7262 | 0.7283 | | 0.4876 | 10.34 | 1800 | 0.5194 | 0.7439 | 0.7434 | | 0.4872 | 11.49 | 2000 | 0.5172 | 0.7510 | 0.7506 | | 0.4807 | 12.64 | 2200 | 0.5475 | 0.7303 | 0.7319 | | 0.4855 | 13.79 | 2400 | 0.5089 | 0.7587 | 0.7582 | | 0.4776 | 14.94 | 2600 | 0.5157 | 0.7514 | 0.7510 | | 0.4752 | 16.09 | 2800 | 0.5177 | 0.7500 | 0.7499 | | 0.4758 | 17.24 | 3000 | 0.5201 | 0.7531 | 0.7528 | | 0.4699 | 18.39 | 3200 | 0.5240 | 0.7504 | 0.7503 | | 0.4728 | 19.54 | 3400 | 0.5102 | 0.7498 | 0.7496 | | 0.4662 | 20.69 | 3600 | 0.5063 | 0.7545 | 0.7542 | | 0.4668 | 21.84 | 3800 | 0.5230 | 0.7458 | 0.7460 | | 0.4627 | 22.99 | 4000 | 0.5297 | 0.7412 | 0.7416 | | 0.4655 | 24.14 | 4200 | 0.5121 | 0.7515 | 0.7510 | | 0.4565 | 25.29 | 4400 | 0.5336 | 0.7506 | 0.7506 | | 0.463 | 26.44 | 4600 | 0.5167 | 0.7540 | 0.7535 | | 0.4583 | 27.59 | 4800 | 0.5223 | 0.7470 | 0.7474 | | 0.4553 | 28.74 | 5000 | 0.5166 | 0.7515 | 0.7513 | | 0.4595 | 29.89 | 5200 | 0.5159 | 0.7546 | 0.7542 | | 0.4532 | 31.03 | 5400 | 0.5204 | 0.7508 | 0.7506 | | 0.4546 | 32.18 | 5600 | 0.5063 | 0.7537 | 0.7531 | | 0.4474 | 33.33 | 5800 | 0.5128 | 0.7562 | 0.7557 | | 0.4565 | 34.48 | 6000 | 0.5174 | 0.7511 | 0.7506 | | 0.4419 | 35.63 | 6200 | 0.5137 | 0.7540 | 0.7535 | | 0.4492 | 36.78 | 6400 | 0.5112 | 0.7576 | 0.7571 | | 0.4456 | 37.93 | 6600 | 0.5413 | 0.7403 | 0.7402 | | 0.4434 | 39.08 | 6800 | 0.5180 | 0.7519 | 0.7513 | | 0.4448 | 40.23 | 7000 | 0.5249 | 0.7538 | 0.7535 | | 0.4468 | 41.38 | 7200 | 0.5210 | 0.7503 | 0.7499 | | 0.444 | 42.53 | 7400 | 0.5156 | 0.7479 | 0.7474 | | 0.4406 | 43.68 | 7600 | 0.5162 | 0.7490 | 0.7485 | | 0.4386 | 44.83 | 7800 | 0.5258 | 0.7495 | 0.7492 | | 0.443 | 45.98 | 8000 | 0.5153 | 0.7486 | 0.7481 | | 0.4409 | 47.13 | 8200 | 0.5243 | 0.7488 | 0.7485 | | 0.4412 | 48.28 | 8400 | 0.5204 | 0.7493 | 0.7488 | | 0.4385 | 49.43 | 8600 | 0.5198 | 0.7501 | 0.7496 | | 0.4406 | 50.57 | 8800 | 0.5227 | 0.7521 | 0.7517 | | 0.4391 | 51.72 | 9000 | 0.5283 | 0.7511 | 0.7510 | | 0.4376 | 52.87 | 9200 | 0.5288 | 0.7484 | 0.7481 | | 0.4383 | 54.02 | 9400 | 0.5270 | 0.7479 | 0.7478 | | 0.4378 | 55.17 | 9600 | 0.5240 | 0.7488 | 0.7485 | | 0.4332 | 56.32 | 9800 | 0.5228 | 0.7514 | 0.7510 | | 0.4382 | 57.47 | 10000 | 0.5228 | 0.7503 | 0.7499 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f

2024-05-03T17:46:02+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K9ac-seqsight\_4096\_512\_15M-L8\_f =============================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K9ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4829 * F1 Score: 0.7825 * Accuracy: 0.7819 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4683 - F1 Score: 0.7846 - Accuracy: 0.7841 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5805 | 1.15 | 200 | 0.5699 | 0.7096 | 0.7121 | | 0.5311 | 2.3 | 400 | 0.6196 | 0.6574 | 0.6711 | | 0.5054 | 3.45 | 600 | 0.5329 | 0.7385 | 0.7384 | | 0.4987 | 4.6 | 800 | 0.5223 | 0.7407 | 0.7406 | | 0.4901 | 5.75 | 1000 | 0.5191 | 0.7511 | 0.7506 | | 0.4858 | 6.9 | 1200 | 0.5215 | 0.7479 | 0.7478 | | 0.4814 | 8.05 | 1400 | 0.5293 | 0.7434 | 0.7431 | | 0.4752 | 9.2 | 1600 | 0.5324 | 0.7414 | 0.7427 | | 0.4677 | 10.34 | 1800 | 0.5228 | 0.7472 | 0.7467 | | 0.4686 | 11.49 | 2000 | 0.5185 | 0.7565 | 0.7560 | | 0.4585 | 12.64 | 2200 | 0.5343 | 0.7408 | 0.7409 | | 0.4611 | 13.79 | 2400 | 0.5132 | 0.7546 | 0.7542 | | 0.4551 | 14.94 | 2600 | 0.5177 | 0.7486 | 0.7481 | | 0.45 | 16.09 | 2800 | 0.5290 | 0.7467 | 0.7470 | | 0.4485 | 17.24 | 3000 | 0.5097 | 0.7583 | 0.7578 | | 0.4414 | 18.39 | 3200 | 0.5293 | 0.7483 | 0.7481 | | 0.4412 | 19.54 | 3400 | 0.5122 | 0.7461 | 0.7456 | | 0.4354 | 20.69 | 3600 | 0.5108 | 0.7502 | 0.7499 | | 0.4326 | 21.84 | 3800 | 0.5305 | 0.7444 | 0.7445 | | 0.4262 | 22.99 | 4000 | 0.5570 | 0.7396 | 0.7406 | | 0.4284 | 24.14 | 4200 | 0.5263 | 0.7511 | 0.7506 | | 0.4186 | 25.29 | 4400 | 0.5468 | 0.7512 | 0.7510 | | 0.4232 | 26.44 | 4600 | 0.5302 | 0.7490 | 0.7485 | | 0.4159 | 27.59 | 4800 | 0.5412 | 0.7507 | 0.7506 | | 0.4109 | 28.74 | 5000 | 0.5274 | 0.7464 | 0.7460 | | 0.4147 | 29.89 | 5200 | 0.5354 | 0.7479 | 0.7481 | | 0.4047 | 31.03 | 5400 | 0.5491 | 0.7428 | 0.7427 | | 0.4047 | 32.18 | 5600 | 0.5310 | 0.7433 | 0.7427 | | 0.3938 | 33.33 | 5800 | 0.5478 | 0.7511 | 0.7506 | | 0.4018 | 34.48 | 6000 | 0.5339 | 0.7508 | 0.7503 | | 0.3872 | 35.63 | 6200 | 0.5474 | 0.7439 | 0.7434 | | 0.3911 | 36.78 | 6400 | 0.5366 | 0.7428 | 0.7424 | | 0.3877 | 37.93 | 6600 | 0.5748 | 0.7417 | 0.7413 | | 0.3853 | 39.08 | 6800 | 0.5557 | 0.7392 | 0.7388 | | 0.3846 | 40.23 | 7000 | 0.5654 | 0.7439 | 0.7434 | | 0.3872 | 41.38 | 7200 | 0.5705 | 0.7375 | 0.7373 | | 0.3829 | 42.53 | 7400 | 0.5605 | 0.7393 | 0.7388 | | 0.3754 | 43.68 | 7600 | 0.5542 | 0.7450 | 0.7445 | | 0.3755 | 44.83 | 7800 | 0.5678 | 0.7403 | 0.7398 | | 0.3758 | 45.98 | 8000 | 0.5571 | 0.7418 | 0.7413 | | 0.3735 | 47.13 | 8200 | 0.5867 | 0.7398 | 0.7395 | | 0.3728 | 48.28 | 8400 | 0.5711 | 0.7382 | 0.7377 | | 0.371 | 49.43 | 8600 | 0.5742 | 0.7407 | 0.7402 | | 0.3695 | 50.57 | 8800 | 0.5821 | 0.7402 | 0.7398 | | 0.368 | 51.72 | 9000 | 0.5897 | 0.7393 | 0.7391 | | 0.3675 | 52.87 | 9200 | 0.5823 | 0.7362 | 0.7359 | | 0.3668 | 54.02 | 9400 | 0.5857 | 0.7365 | 0.7362 | | 0.3671 | 55.17 | 9600 | 0.5799 | 0.7396 | 0.7391 | | 0.3637 | 56.32 | 9800 | 0.5779 | 0.7410 | 0.7406 | | 0.3655 | 57.47 | 10000 | 0.5769 | 0.7406 | 0.7402 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f

2024-05-03T17:46:07+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K9ac-seqsight\_4096\_512\_15M-L32\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K9ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4683 * F1 Score: 0.7846 * Accuracy: 0.7841 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # prompt_fine_tuned_CB_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1636 - Accuracy: 0.3182 - F1: 0.1536 ## 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "prompt_fine_tuned_CB_bert", "results": []}]}

lenatr99/prompt_fine_tuned_CB_bert

2024-05-03T17:48:19+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #region-us

# prompt_fine_tuned_CB_bert This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1636 - Accuracy: 0.3182 - F1: 0.1536 ## 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

<!-- 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. --> # Phi0503B1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0800 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5697 | 0.09 | 10 | 0.7185 | | 0.345 | 0.18 | 20 | 0.1655 | | 0.1552 | 0.27 | 30 | 0.1343 | | 0.1345 | 0.36 | 40 | 0.1175 | | 0.121 | 0.45 | 50 | 0.1152 | | 0.1088 | 0.54 | 60 | 0.0861 | | 0.0923 | 0.63 | 70 | 0.0942 | | 0.0773 | 0.73 | 80 | 0.0681 | | 0.0606 | 0.82 | 90 | 0.0686 | | 0.0647 | 0.91 | 100 | 0.0624 | | 0.062 | 1.0 | 110 | 0.0663 | | 0.0434 | 1.09 | 120 | 0.0687 | | 0.042 | 1.18 | 130 | 0.0675 | | 0.0503 | 1.27 | 140 | 0.0681 | | 0.0445 | 1.36 | 150 | 0.0654 | | 0.0511 | 1.45 | 160 | 0.0593 | | 0.0462 | 1.54 | 170 | 0.0687 | | 0.0498 | 1.63 | 180 | 0.0651 | | 0.0448 | 1.72 | 190 | 0.0640 | | 0.043 | 1.81 | 200 | 0.0636 | | 0.04 | 1.9 | 210 | 0.0617 | | 0.043 | 1.99 | 220 | 0.0613 | | 0.0226 | 2.08 | 230 | 0.0657 | | 0.0165 | 2.18 | 240 | 0.0788 | | 0.011 | 2.27 | 250 | 0.0943 | | 0.0097 | 2.36 | 260 | 0.0946 | | 0.0167 | 2.45 | 270 | 0.0864 | | 0.0105 | 2.54 | 280 | 0.0827 | | 0.0118 | 2.63 | 290 | 0.0819 | | 0.0156 | 2.72 | 300 | 0.0802 | | 0.0137 | 2.81 | 310 | 0.0800 | | 0.013 | 2.9 | 320 | 0.0800 | | 0.0098 | 2.99 | 330 | 0.0800 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B1", "results": []}]}

Litzy619/Phi0503B1

2024-05-03T17:49:24+00:00

TAGS #safetensors #generated_from_trainer #base_model-microsoft/Phi-3-mini-4k-instruct #license-mit #region-us

Phi0503B1 ========= This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0800 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: 0.0003 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.0.dev0 * Pytorch 2.1.2+cu121 * Datasets 2.14.6 * Tokenizers 0.14.1

<!-- 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. --> # Phi0503B2 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0690 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.4837 | 0.09 | 10 | 5.4342 | | 5.4537 | 0.18 | 20 | 5.2266 | | 4.774 | 0.27 | 30 | 3.6419 | | 2.4745 | 0.36 | 40 | 1.0488 | | 0.5621 | 0.45 | 50 | 0.2015 | | 0.1739 | 0.54 | 60 | 0.1465 | | 0.1373 | 0.63 | 70 | 0.1350 | | 0.1328 | 0.73 | 80 | 0.1258 | | 0.1091 | 0.82 | 90 | 0.1152 | | 0.1142 | 0.91 | 100 | 0.0968 | | 0.0918 | 1.0 | 110 | 0.1021 | | 0.0773 | 1.09 | 120 | 0.0807 | | 0.0711 | 1.18 | 130 | 0.0793 | | 0.0751 | 1.27 | 140 | 0.0661 | | 0.06 | 1.36 | 150 | 0.0651 | | 0.0647 | 1.45 | 160 | 0.0658 | | 0.0577 | 1.54 | 170 | 0.0657 | | 0.0575 | 1.63 | 180 | 0.0644 | | 0.0534 | 1.72 | 190 | 0.0661 | | 0.0594 | 1.81 | 200 | 0.0622 | | 0.0473 | 1.9 | 210 | 0.0628 | | 0.0522 | 1.99 | 220 | 0.0643 | | 0.0402 | 2.08 | 230 | 0.0644 | | 0.0436 | 2.18 | 240 | 0.0674 | | 0.0343 | 2.27 | 250 | 0.0708 | | 0.0358 | 2.36 | 260 | 0.0724 | | 0.0411 | 2.45 | 270 | 0.0720 | | 0.0359 | 2.54 | 280 | 0.0706 | | 0.0366 | 2.63 | 290 | 0.0702 | | 0.0397 | 2.72 | 300 | 0.0697 | | 0.044 | 2.81 | 310 | 0.0692 | | 0.0415 | 2.9 | 320 | 0.0688 | | 0.037 | 2.99 | 330 | 0.0690 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B2", "results": []}]}

Litzy619/Phi0503B2

2024-05-03T17:49:39+00:00

TAGS #safetensors #generated_from_trainer #base_model-microsoft/Phi-3-mini-4k-instruct #license-mit #region-us

Phi0503B2 ========= This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0690 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: 0.0003 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.0.dev0 * Pytorch 2.1.2+cu121 * Datasets 2.14.6 * Tokenizers 0.14.1

peft

<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6104 - F1 Score: 0.6754 - Accuracy: 0.6755 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6748 | 0.87 | 200 | 0.6571 | 0.6173 | 0.6188 | | 0.6536 | 1.74 | 400 | 0.6488 | 0.6324 | 0.6323 | | 0.6479 | 2.61 | 600 | 0.6438 | 0.6414 | 0.6410 | | 0.6365 | 3.48 | 800 | 0.6347 | 0.6385 | 0.6397 | | 0.6346 | 4.35 | 1000 | 0.6307 | 0.6387 | 0.6416 | | 0.6286 | 5.22 | 1200 | 0.6395 | 0.6339 | 0.6391 | | 0.6242 | 6.09 | 1400 | 0.6266 | 0.6497 | 0.6511 | | 0.6186 | 6.96 | 1600 | 0.6242 | 0.6600 | 0.6606 | | 0.6171 | 7.83 | 1800 | 0.6186 | 0.6623 | 0.6630 | | 0.6159 | 8.7 | 2000 | 0.6171 | 0.6646 | 0.6644 | | 0.6094 | 9.57 | 2200 | 0.6146 | 0.6613 | 0.6611 | | 0.6144 | 10.43 | 2400 | 0.6139 | 0.6648 | 0.6647 | | 0.6105 | 11.3 | 2600 | 0.6175 | 0.6571 | 0.6584 | | 0.6118 | 12.17 | 2800 | 0.6119 | 0.6676 | 0.6674 | | 0.6086 | 13.04 | 3000 | 0.6103 | 0.6679 | 0.6677 | | 0.6053 | 13.91 | 3200 | 0.6114 | 0.6620 | 0.6625 | | 0.6039 | 14.78 | 3400 | 0.6115 | 0.6615 | 0.6628 | | 0.606 | 15.65 | 3600 | 0.6125 | 0.6653 | 0.6660 | | 0.6002 | 16.52 | 3800 | 0.6121 | 0.6665 | 0.6668 | | 0.6016 | 17.39 | 4000 | 0.6084 | 0.6693 | 0.6696 | | 0.603 | 18.26 | 4200 | 0.6086 | 0.6690 | 0.6690 | | 0.597 | 19.13 | 4400 | 0.6072 | 0.6692 | 0.6693 | | 0.5983 | 20.0 | 4600 | 0.6074 | 0.6661 | 0.6666 | | 0.5986 | 20.87 | 4800 | 0.6091 | 0.6645 | 0.6649 | | 0.5976 | 21.74 | 5000 | 0.6116 | 0.6619 | 0.6630 | | 0.5976 | 22.61 | 5200 | 0.6068 | 0.6666 | 0.6677 | | 0.5978 | 23.48 | 5400 | 0.6129 | 0.6573 | 0.6611 | | 0.5943 | 24.35 | 5600 | 0.6047 | 0.6673 | 0.6674 | | 0.5966 | 25.22 | 5800 | 0.6116 | 0.6578 | 0.6617 | | 0.5934 | 26.09 | 6000 | 0.6113 | 0.6585 | 0.6614 | | 0.5951 | 26.96 | 6200 | 0.6116 | 0.6622 | 0.6652 | | 0.5948 | 27.83 | 6400 | 0.6180 | 0.6534 | 0.6592 | | 0.5914 | 28.7 | 6600 | 0.6068 | 0.6609 | 0.6628 | | 0.5915 | 29.57 | 6800 | 0.6048 | 0.6677 | 0.6690 | | 0.5893 | 30.43 | 7000 | 0.6109 | 0.6600 | 0.6633 | | 0.5974 | 31.3 | 7200 | 0.6085 | 0.6625 | 0.6652 | | 0.5923 | 32.17 | 7400 | 0.6108 | 0.6596 | 0.6639 | | 0.5891 | 33.04 | 7600 | 0.6036 | 0.6659 | 0.6671 | | 0.5919 | 33.91 | 7800 | 0.6048 | 0.6618 | 0.6633 | | 0.5906 | 34.78 | 8000 | 0.6055 | 0.6651 | 0.6666 | | 0.5927 | 35.65 | 8200 | 0.6027 | 0.6657 | 0.6668 | | 0.5891 | 36.52 | 8400 | 0.6069 | 0.6614 | 0.6639 | | 0.5908 | 37.39 | 8600 | 0.6063 | 0.6635 | 0.6655 | | 0.5857 | 38.26 | 8800 | 0.6095 | 0.6630 | 0.6660 | | 0.5921 | 39.13 | 9000 | 0.6070 | 0.6622 | 0.6649 | | 0.5895 | 40.0 | 9200 | 0.6047 | 0.6643 | 0.6660 | | 0.5884 | 40.87 | 9400 | 0.6029 | 0.6672 | 0.6679 | | 0.5909 | 41.74 | 9600 | 0.6040 | 0.6656 | 0.6668 | | 0.5906 | 42.61 | 9800 | 0.6042 | 0.6650 | 0.6666 | | 0.5892 | 43.48 | 10000 | 0.6047 | 0.6640 | 0.6658 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f

2024-05-03T17:51:35+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me3-seqsight\_4096\_512\_15M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me3 dataset. It achieves the following results on the evaluation set: * Loss: 0.6104 * F1 Score: 0.6754 * Accuracy: 0.6755 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

transformers

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

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_5e-05

2024-05-03T17:53:04+00:00

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

# LlamaJarvis-7B LlamaJarvis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) ## 🧩 Configuration ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.6 weight: 0.5 - model: mlabonne/OrpoLlama-3-8B parameters: density: 0.55 weight: 0.05 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/LlamaJarvis-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B"]}

automerger/LlamaJarvis-7B

2024-05-03T17:53:49+00:00

TAGS #merge #mergekit #lazymergekit #automerger #base_model-NousResearch/Meta-Llama-3-8B-Instruct #base_model-mlabonne/OrpoLlama-3-8B #license-apache-2.0 #region-us

# LlamaJarvis-7B LlamaJarvis-7B is an automated merge created by Maxime Labonne using the following configuration. * NousResearch/Meta-Llama-3-8B-Instruct * mlabonne/OrpoLlama-3-8B ## Configuration ## Usage

peft

<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6053 - F1 Score: 0.6811 - Accuracy: 0.6823 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6682 | 0.87 | 200 | 0.6471 | 0.6349 | 0.6359 | | 0.6393 | 1.74 | 400 | 0.6363 | 0.6456 | 0.6457 | | 0.6281 | 2.61 | 600 | 0.6212 | 0.6612 | 0.6609 | | 0.6181 | 3.48 | 800 | 0.6148 | 0.6642 | 0.6641 | | 0.6148 | 4.35 | 1000 | 0.6139 | 0.6601 | 0.6603 | | 0.6098 | 5.22 | 1200 | 0.6154 | 0.6526 | 0.6552 | | 0.6042 | 6.09 | 1400 | 0.6202 | 0.6480 | 0.6527 | | 0.5989 | 6.96 | 1600 | 0.6141 | 0.6615 | 0.6639 | | 0.5962 | 7.83 | 1800 | 0.6078 | 0.6712 | 0.6709 | | 0.5953 | 8.7 | 2000 | 0.6030 | 0.6731 | 0.6731 | | 0.5864 | 9.57 | 2200 | 0.5979 | 0.6767 | 0.6766 | | 0.5919 | 10.43 | 2400 | 0.6012 | 0.6721 | 0.6723 | | 0.5862 | 11.3 | 2600 | 0.6009 | 0.6692 | 0.6715 | | 0.5893 | 12.17 | 2800 | 0.5981 | 0.6709 | 0.6717 | | 0.5824 | 13.04 | 3000 | 0.5966 | 0.6752 | 0.6758 | | 0.5807 | 13.91 | 3200 | 0.5975 | 0.6735 | 0.6747 | | 0.5772 | 14.78 | 3400 | 0.6008 | 0.6742 | 0.6766 | | 0.5799 | 15.65 | 3600 | 0.6016 | 0.6730 | 0.6758 | | 0.5746 | 16.52 | 3800 | 0.5983 | 0.6759 | 0.6764 | | 0.5731 | 17.39 | 4000 | 0.5999 | 0.6770 | 0.6777 | | 0.5756 | 18.26 | 4200 | 0.5986 | 0.6797 | 0.6815 | | 0.5684 | 19.13 | 4400 | 0.5978 | 0.6775 | 0.6780 | | 0.5707 | 20.0 | 4600 | 0.5995 | 0.6755 | 0.6769 | | 0.5702 | 20.87 | 4800 | 0.5974 | 0.6778 | 0.6791 | | 0.5675 | 21.74 | 5000 | 0.6075 | 0.6707 | 0.6720 | | 0.569 | 22.61 | 5200 | 0.5955 | 0.6776 | 0.6785 | | 0.5645 | 23.48 | 5400 | 0.6137 | 0.6672 | 0.6723 | | 0.5628 | 24.35 | 5600 | 0.6011 | 0.6756 | 0.6769 | | 0.5664 | 25.22 | 5800 | 0.6027 | 0.6728 | 0.6764 | | 0.5609 | 26.09 | 6000 | 0.6073 | 0.6746 | 0.6772 | | 0.5618 | 26.96 | 6200 | 0.6067 | 0.6739 | 0.6769 | | 0.5603 | 27.83 | 6400 | 0.6151 | 0.6679 | 0.6728 | | 0.5578 | 28.7 | 6600 | 0.5997 | 0.6778 | 0.6796 | | 0.559 | 29.57 | 6800 | 0.5980 | 0.6795 | 0.6807 | | 0.5551 | 30.43 | 7000 | 0.6067 | 0.6740 | 0.6772 | | 0.5636 | 31.3 | 7200 | 0.6002 | 0.6794 | 0.6810 | | 0.5549 | 32.17 | 7400 | 0.6016 | 0.6790 | 0.6807 | | 0.5543 | 33.04 | 7600 | 0.5994 | 0.6770 | 0.6783 | | 0.5558 | 33.91 | 7800 | 0.5993 | 0.6776 | 0.6793 | | 0.5546 | 34.78 | 8000 | 0.6022 | 0.6781 | 0.6793 | | 0.5567 | 35.65 | 8200 | 0.5980 | 0.6793 | 0.6807 | | 0.553 | 36.52 | 8400 | 0.6025 | 0.6756 | 0.6783 | | 0.5553 | 37.39 | 8600 | 0.6016 | 0.6774 | 0.6788 | | 0.5478 | 38.26 | 8800 | 0.6096 | 0.6733 | 0.6764 | | 0.5536 | 39.13 | 9000 | 0.6045 | 0.6756 | 0.6777 | | 0.5508 | 40.0 | 9200 | 0.6035 | 0.6800 | 0.6818 | | 0.5521 | 40.87 | 9400 | 0.6018 | 0.6760 | 0.6769 | | 0.5512 | 41.74 | 9600 | 0.6028 | 0.6758 | 0.6772 | | 0.552 | 42.61 | 9800 | 0.6021 | 0.6789 | 0.6802 | | 0.5521 | 43.48 | 10000 | 0.6031 | 0.6783 | 0.6799 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f

2024-05-03T17:54:40+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me3-seqsight\_4096\_512\_15M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me3 dataset. It achieves the following results on the evaluation set: * Loss: 0.6053 * F1 Score: 0.6811 * Accuracy: 0.6823 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

peft

<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6214 - F1 Score: 0.6871 - Accuracy: 0.6872 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6604 | 0.87 | 200 | 0.6356 | 0.6419 | 0.6446 | | 0.6283 | 1.74 | 400 | 0.6323 | 0.6420 | 0.6451 | | 0.6159 | 2.61 | 600 | 0.6089 | 0.6698 | 0.6696 | | 0.6064 | 3.48 | 800 | 0.6044 | 0.6734 | 0.6731 | | 0.6011 | 4.35 | 1000 | 0.6067 | 0.6681 | 0.6679 | | 0.5945 | 5.22 | 1200 | 0.6015 | 0.6739 | 0.6742 | | 0.5889 | 6.09 | 1400 | 0.6102 | 0.6588 | 0.6630 | | 0.5839 | 6.96 | 1600 | 0.6055 | 0.6744 | 0.6764 | | 0.5777 | 7.83 | 1800 | 0.6038 | 0.6778 | 0.6777 | | 0.5768 | 8.7 | 2000 | 0.6082 | 0.6716 | 0.6717 | | 0.5664 | 9.57 | 2200 | 0.5990 | 0.6784 | 0.6785 | | 0.5693 | 10.43 | 2400 | 0.6050 | 0.6708 | 0.6726 | | 0.5635 | 11.3 | 2600 | 0.5996 | 0.6714 | 0.675 | | 0.566 | 12.17 | 2800 | 0.5940 | 0.6735 | 0.6747 | | 0.556 | 13.04 | 3000 | 0.5968 | 0.6770 | 0.6780 | | 0.553 | 13.91 | 3200 | 0.6026 | 0.6703 | 0.6720 | | 0.5486 | 14.78 | 3400 | 0.6150 | 0.6675 | 0.6709 | | 0.5497 | 15.65 | 3600 | 0.6032 | 0.6709 | 0.6731 | | 0.5432 | 16.52 | 3800 | 0.6059 | 0.6764 | 0.6766 | | 0.5393 | 17.39 | 4000 | 0.6131 | 0.6752 | 0.6772 | | 0.5427 | 18.26 | 4200 | 0.6093 | 0.6747 | 0.6785 | | 0.5304 | 19.13 | 4400 | 0.6131 | 0.6716 | 0.6739 | | 0.5329 | 20.0 | 4600 | 0.6077 | 0.6777 | 0.6793 | | 0.531 | 20.87 | 4800 | 0.6070 | 0.6769 | 0.6783 | | 0.5239 | 21.74 | 5000 | 0.6174 | 0.6708 | 0.6723 | | 0.5272 | 22.61 | 5200 | 0.6096 | 0.6799 | 0.6813 | | 0.5188 | 23.48 | 5400 | 0.6364 | 0.6696 | 0.6731 | | 0.5177 | 24.35 | 5600 | 0.6255 | 0.6697 | 0.6736 | | 0.5185 | 25.22 | 5800 | 0.6251 | 0.6740 | 0.6777 | | 0.513 | 26.09 | 6000 | 0.6339 | 0.6707 | 0.6742 | | 0.5119 | 26.96 | 6200 | 0.6245 | 0.6742 | 0.6777 | | 0.5078 | 27.83 | 6400 | 0.6367 | 0.6723 | 0.6766 | | 0.504 | 28.7 | 6600 | 0.6171 | 0.6765 | 0.6772 | | 0.5056 | 29.57 | 6800 | 0.6165 | 0.6755 | 0.6769 | | 0.5021 | 30.43 | 7000 | 0.6280 | 0.6777 | 0.6804 | | 0.5093 | 31.3 | 7200 | 0.6212 | 0.6818 | 0.6826 | | 0.4991 | 32.17 | 7400 | 0.6257 | 0.6770 | 0.6783 | | 0.4968 | 33.04 | 7600 | 0.6238 | 0.6776 | 0.6791 | | 0.4957 | 33.91 | 7800 | 0.6232 | 0.6764 | 0.6785 | | 0.4945 | 34.78 | 8000 | 0.6249 | 0.6765 | 0.6780 | | 0.4986 | 35.65 | 8200 | 0.6241 | 0.6784 | 0.6802 | | 0.4907 | 36.52 | 8400 | 0.6303 | 0.6738 | 0.6761 | | 0.495 | 37.39 | 8600 | 0.6312 | 0.6758 | 0.6769 | | 0.4868 | 38.26 | 8800 | 0.6352 | 0.6774 | 0.6793 | | 0.4894 | 39.13 | 9000 | 0.6343 | 0.6773 | 0.6791 | | 0.4875 | 40.0 | 9200 | 0.6298 | 0.6787 | 0.6802 | | 0.4871 | 40.87 | 9400 | 0.6313 | 0.6760 | 0.6769 | | 0.4861 | 41.74 | 9600 | 0.6330 | 0.6773 | 0.6791 | | 0.4892 | 42.61 | 9800 | 0.6306 | 0.6777 | 0.6791 | | 0.4891 | 43.48 | 10000 | 0.6317 | 0.6775 | 0.6791 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f

2024-05-03T17:55:10+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_EMP\_H3K4me3-seqsight\_4096\_512\_15M-L32\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me3 dataset. It achieves the following results on the evaluation set: * Loss: 0.6214 * F1 Score: 0.6871 * Accuracy: 0.6872 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: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

LongDHo/finetuned-gemma-2b

2024-05-03T17:55:44+00:00

TAGS #transformers #safetensors #gemma #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # cosmosDPO_CodeTest2 This model is a fine-tuned version of [ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5356 - Rewards/chosen: -1.3639 - Rewards/rejected: -3.6411 - Rewards/accuracies: 0.2640 - Rewards/margins: 2.2772 - Logps/rejected: -477.7171 - Logps/chosen: -224.9044 - Logits/rejected: -4.1447 - Logits/chosen: -3.7114 ## 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: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6915 | 0.0524 | 8 | 0.6855 | -0.0225 | -0.0387 | 0.2171 | 0.0162 | -117.4774 | -90.7611 | -2.7135 | -2.4688 | | 0.6639 | 0.1047 | 16 | 0.6480 | -0.2509 | -0.4015 | 0.2189 | 0.1506 | -153.7584 | -113.6010 | -3.2208 | -2.9343 | | 0.6251 | 0.1571 | 24 | 0.6436 | -0.7453 | -1.1570 | 0.2217 | 0.4117 | -229.3032 | -163.0413 | -3.8702 | -3.5589 | | 0.6238 | 0.2095 | 32 | 0.6047 | -0.7237 | -1.2597 | 0.2355 | 0.5360 | -239.5777 | -160.8856 | -3.7913 | -3.4555 | | 0.5586 | 0.2619 | 40 | 0.5789 | -1.0590 | -1.9755 | 0.2474 | 0.9164 | -311.1551 | -194.4169 | -3.9560 | -3.5940 | | 0.5389 | 0.3142 | 48 | 0.5577 | -1.0922 | -2.3486 | 0.2548 | 1.2564 | -348.4677 | -197.7312 | -3.9027 | -3.5251 | | 0.5102 | 0.3666 | 56 | 0.5606 | -1.4904 | -3.3229 | 0.2548 | 1.8325 | -445.8979 | -237.5522 | -4.0088 | -3.6310 | | 0.5506 | 0.4190 | 64 | 0.5529 | -1.4084 | -3.4076 | 0.2585 | 1.9992 | -454.3663 | -229.3532 | -3.9314 | -3.5543 | | 0.5696 | 0.4714 | 72 | 0.5365 | -0.7411 | -2.1788 | 0.2621 | 1.4377 | -331.4860 | -162.6252 | -3.6733 | -3.2798 | | 0.5265 | 0.5237 | 80 | 0.5355 | -0.8770 | -2.4950 | 0.2612 | 1.6180 | -363.1028 | -176.2112 | -3.7304 | -3.3452 | | 0.5199 | 0.5761 | 88 | 0.5482 | -1.5559 | -3.7745 | 0.2585 | 2.2186 | -491.0597 | -244.1054 | -3.9633 | -3.5958 | | 0.5163 | 0.6285 | 96 | 0.5464 | -1.5899 | -3.8545 | 0.2594 | 2.2646 | -499.0518 | -247.5011 | -4.0472 | -3.6688 | | 0.5421 | 0.6809 | 104 | 0.5408 | -1.4973 | -3.8002 | 0.2631 | 2.3029 | -493.6231 | -238.2402 | -4.1221 | -3.7151 | | 0.5416 | 0.7332 | 112 | 0.5356 | -1.2811 | -3.4299 | 0.2640 | 2.1488 | -456.5994 | -216.6231 | -4.0861 | -3.6611 | | 0.4967 | 0.7856 | 120 | 0.5347 | -1.2626 | -3.4278 | 0.2640 | 2.1653 | -456.3912 | -214.7687 | -4.1048 | -3.6705 | | 0.4783 | 0.8380 | 128 | 0.5345 | -1.2666 | -3.4477 | 0.2640 | 2.1811 | -458.3748 | -215.1744 | -4.1066 | -3.6704 | | 0.508 | 0.8903 | 136 | 0.5352 | -1.3287 | -3.5746 | 0.2640 | 2.2459 | -471.0667 | -221.3868 | -4.1311 | -3.6966 | | 0.5417 | 0.9427 | 144 | 0.5356 | -1.3619 | -3.6366 | 0.2640 | 2.2746 | -477.2621 | -224.7045 | -4.1435 | -3.7103 | | 0.5414 | 0.9951 | 152 | 0.5356 | -1.3639 | -3.6411 | 0.2640 | 2.2772 | -477.7171 | -224.9044 | -4.1447 | -3.7114 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "model-index": [{"name": "cosmosDPO_CodeTest2", "results": []}]}

meguzn/cosmosDPO_CodeTest2

2024-05-03T17:55:54+00:00

TAGS #peft #tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1 #license-mit #region-us

cosmosDPO\_CodeTest2 ==================== This model is a fine-tuned version of ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5356 * Rewards/chosen: -1.3639 * Rewards/rejected: -3.6411 * Rewards/accuracies: 0.2640 * Rewards/margins: 2.2772 * Logps/rejected: -477.7171 * Logps/chosen: -224.9044 * Logits/rejected: -4.1447 * Logits/chosen: -3.7114 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: 5e-06 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.10.0 * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

transformers

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

{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}

MohamedSaeed-dev/gemma7b-unsloth

2024-05-03T17:56:25+00:00

TAGS #transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

text-generation

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}

MohamedSaeed-dev/phi-unsloth

2024-05-03T17:56:38+00:00

TAGS #transformers #pytorch #mistral #text-generation #unsloth #trl #sft #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

peft

<!-- 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. --> # loha_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.3182 - F1: 0.1536 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.9874 | 3.5714 | 50 | 1.1351 | 0.3182 | 0.1591 | | 0.8665 | 7.1429 | 100 | 1.1589 | 0.3182 | 0.1536 | | 0.8359 | 10.7143 | 150 | 1.1890 | 0.3182 | 0.1536 | | 0.7662 | 14.2857 | 200 | 1.2116 | 0.3182 | 0.1536 | | 0.769 | 17.8571 | 250 | 1.2287 | 0.3182 | 0.1536 | | 0.7534 | 21.4286 | 300 | 1.2380 | 0.3182 | 0.1536 | | 0.7359 | 25.0 | 350 | 1.2421 | 0.3182 | 0.1536 | | 0.7449 | 28.5714 | 400 | 1.2436 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_cb_croslo", "results": []}]}

lenatr99/loha_fine_tuned_cb_croslo

2024-05-03T17:56:45+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-EMBEDDIA/crosloengual-bert #license-cc-by-4.0 #region-us

loha\_fine\_tuned\_cb\_croslo ============================= This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2436 * Accuracy: 0.3182 * F1: 0.1536 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: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # lora_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3172 - Accuracy: 0.3182 - F1: 0.1536 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 1.0956 | 3.5714 | 50 | 1.1024 | 0.3636 | 0.3027 | | 0.8669 | 7.1429 | 100 | 1.1540 | 0.3182 | 0.1536 | | 0.7634 | 10.7143 | 150 | 1.2351 | 0.3182 | 0.1536 | | 0.7 | 14.2857 | 200 | 1.2885 | 0.3182 | 0.1536 | | 0.6951 | 17.8571 | 250 | 1.3121 | 0.3182 | 0.1536 | | 0.7047 | 21.4286 | 300 | 1.3145 | 0.3182 | 0.1536 | | 0.6769 | 25.0 | 350 | 1.3154 | 0.3182 | 0.1536 | | 0.6886 | 28.5714 | 400 | 1.3172 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "lora_fine_tuned_cb_croslo", "results": []}]}

lenatr99/lora_fine_tuned_cb_croslo

2024-05-03T17:56:45+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-EMBEDDIA/crosloengual-bert #license-cc-by-4.0 #region-us

lora\_fine\_tuned\_cb\_croslo ============================= This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3172 * Accuracy: 0.3182 * F1: 0.1536 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: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

peft

<!-- 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. --> # prompt_fine_tuned_CB_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2046 - Accuracy: 0.3182 - F1: 0.1536 ## 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 1.0278 | 0.4545 | 50 | 1.1158 | 0.3182 | 0.2306 | | 0.9865 | 0.9091 | 100 | 1.1195 | 0.3636 | 0.2430 | | 0.8601 | 1.3636 | 150 | 1.1357 | 0.3182 | 0.1536 | | 0.8769 | 1.8182 | 200 | 1.1595 | 0.3182 | 0.1536 | | 0.9026 | 2.2727 | 250 | 1.1733 | 0.3182 | 0.1536 | | 0.8002 | 2.7273 | 300 | 1.1885 | 0.3182 | 0.1536 | | 0.8093 | 3.1818 | 350 | 1.1996 | 0.3182 | 0.1536 | | 0.7259 | 3.6364 | 400 | 1.2046 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "prompt_fine_tuned_CB_croslo", "results": []}]}

lenatr99/prompt_fine_tuned_CB_croslo

2024-05-03T17:57:06+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-EMBEDDIA/crosloengual-bert #license-cc-by-4.0 #region-us

prompt\_fine\_tuned\_CB\_croslo =============================== This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2046 * Accuracy: 0.3182 * F1: 0.1536 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: 2e-05 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

transformers

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

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_0.0002

2024-05-03T17:59:42+00:00

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact