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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_mouse_4-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_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.6110 - F1 Score: 0.6554 - Accuracy: 0.6559 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.683 | 1.69 | 200 | 0.6566 | 0.6034 | 0.6033 | | 0.6508 | 3.39 | 400 | 0.6407 | 0.6230 | 0.6235 | | 0.6239 | 5.08 | 600 | 0.6308 | 0.6494 | 0.6495 | | 0.6163 | 6.78 | 800 | 0.6259 | 0.6523 | 0.6527 | | 0.6116 | 8.47 | 1000 | 0.6196 | 0.6535 | 0.6537 | | 0.6067 | 10.17 | 1200 | 0.6199 | 0.6473 | 0.6500 | | 0.6023 | 11.86 | 1400 | 0.6083 | 0.6664 | 0.6665 | | 0.5984 | 13.56 | 1600 | 0.6065 | 0.6617 | 0.6617 | | 0.5946 | 15.25 | 1800 | 0.6030 | 0.6704 | 0.6707 | | 0.5924 | 16.95 | 2000 | 0.6005 | 0.6664 | 0.6676 | | 0.5883 | 18.64 | 2200 | 0.6036 | 0.6649 | 0.6670 | | 0.5884 | 20.34 | 2400 | 0.5927 | 0.6783 | 0.6782 | | 0.5858 | 22.03 | 2600 | 0.6051 | 0.6606 | 0.6649 | | 0.5826 | 23.73 | 2800 | 0.5936 | 0.6682 | 0.6697 | | 0.5795 | 25.42 | 3000 | 0.5884 | 0.6846 | 0.6845 | | 0.582 | 27.12 | 3200 | 0.6029 | 0.6611 | 0.6670 | | 0.5777 | 28.81 | 3400 | 0.5910 | 0.6725 | 0.6739 | | 0.5738 | 30.51 | 3600 | 0.5898 | 0.6738 | 0.6755 | | 0.5762 | 32.2 | 3800 | 0.5907 | 0.6773 | 0.6792 | | 0.5723 | 33.9 | 4000 | 0.5926 | 0.6742 | 0.6771 | | 0.5749 | 35.59 | 4200 | 0.5941 | 0.6714 | 0.6750 | | 0.5725 | 37.29 | 4400 | 0.5861 | 0.6829 | 0.6840 | | 0.5697 | 38.98 | 4600 | 0.5849 | 0.6865 | 0.6872 | | 0.5704 | 40.68 | 4800 | 0.5867 | 0.6790 | 0.6808 | | 0.5636 | 42.37 | 5000 | 0.5876 | 0.6862 | 0.6872 | | 0.5688 | 44.07 | 5200 | 0.5832 | 0.6948 | 0.6952 | | 0.5672 | 45.76 | 5400 | 0.5889 | 0.6780 | 0.6808 | | 0.5659 | 47.46 | 5600 | 0.5863 | 0.6885 | 0.6888 | | 0.5679 | 49.15 | 5800 | 0.5958 | 0.6677 | 0.6723 | | 0.5659 | 50.85 | 6000 | 0.5818 | 0.6840 | 0.6851 | | 0.5643 | 52.54 | 6200 | 0.5843 | 0.6858 | 0.6872 | | 0.5642 | 54.24 | 6400 | 0.5827 | 0.6878 | 0.6888 | | 0.5631 | 55.93 | 6600 | 0.5810 | 0.6931 | 0.6936 | | 0.5644 | 57.63 | 6800 | 0.5784 | 0.7031 | 0.7031 | | 0.5635 | 59.32 | 7000 | 0.5866 | 0.6773 | 0.6798 | | 0.5596 | 61.02 | 7200 | 0.5803 | 0.6990 | 0.6994 | | 0.5629 | 62.71 | 7400 | 0.5813 | 0.6911 | 0.6920 | | 0.5617 | 64.41 | 7600 | 0.5839 | 0.6920 | 0.6930 | | 0.5618 | 66.1 | 7800 | 0.5828 | 0.6932 | 0.6941 | | 0.5576 | 67.8 | 8000 | 0.5818 | 0.6989 | 0.6994 | | 0.5608 | 69.49 | 8200 | 0.5811 | 0.6956 | 0.6962 | | 0.5562 | 71.19 | 8400 | 0.5827 | 0.7005 | 0.7010 | | 0.5598 | 72.88 | 8600 | 0.5803 | 0.6982 | 0.6984 | | 0.5576 | 74.58 | 8800 | 0.5847 | 0.6898 | 0.6909 | | 0.5572 | 76.27 | 9000 | 0.5833 | 0.6945 | 0.6952 | | 0.5579 | 77.97 | 9200 | 0.5821 | 0.6950 | 0.6957 | | 0.5579 | 79.66 | 9400 | 0.5813 | 0.7012 | 0.7015 | | 0.5586 | 81.36 | 9600 | 0.5823 | 0.6946 | 0.6952 | | 0.5566 | 83.05 | 9800 | 0.5814 | 0.7006 | 0.7010 | | 0.5552 | 84.75 | 10000 | 0.5825 | 0.6934 | 0.6941 | ### 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_mouse_4-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_15M-L1_f

2024-05-03T18:52:52+00:00

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_mouse_4-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_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.6101 - F1 Score: 0.6606 - Accuracy: 0.6606 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6674 | 1.69 | 200 | 0.6381 | 0.6401 | 0.6399 | | 0.6256 | 3.39 | 400 | 0.6245 | 0.6486 | 0.6495 | | 0.607 | 5.08 | 600 | 0.6118 | 0.6598 | 0.6606 | | 0.5989 | 6.78 | 800 | 0.6010 | 0.6679 | 0.6691 | | 0.5915 | 8.47 | 1000 | 0.5868 | 0.6823 | 0.6824 | | 0.583 | 10.17 | 1200 | 0.5847 | 0.6824 | 0.6840 | | 0.5796 | 11.86 | 1400 | 0.5738 | 0.6963 | 0.6962 | | 0.5732 | 13.56 | 1600 | 0.5765 | 0.6930 | 0.6930 | | 0.5673 | 15.25 | 1800 | 0.5703 | 0.6951 | 0.6952 | | 0.564 | 16.95 | 2000 | 0.5757 | 0.6873 | 0.6909 | | 0.5596 | 18.64 | 2200 | 0.5713 | 0.6978 | 0.6984 | | 0.5596 | 20.34 | 2400 | 0.5668 | 0.7117 | 0.7116 | | 0.5554 | 22.03 | 2600 | 0.5946 | 0.6729 | 0.6792 | | 0.5523 | 23.73 | 2800 | 0.5647 | 0.6984 | 0.6989 | | 0.5483 | 25.42 | 3000 | 0.5646 | 0.6993 | 0.6994 | | 0.549 | 27.12 | 3200 | 0.5756 | 0.6921 | 0.6941 | | 0.5452 | 28.81 | 3400 | 0.5690 | 0.7002 | 0.7005 | | 0.541 | 30.51 | 3600 | 0.5648 | 0.7075 | 0.7074 | | 0.5424 | 32.2 | 3800 | 0.5669 | 0.7040 | 0.7042 | | 0.5385 | 33.9 | 4000 | 0.5669 | 0.7035 | 0.7042 | | 0.538 | 35.59 | 4200 | 0.5762 | 0.6918 | 0.6941 | | 0.535 | 37.29 | 4400 | 0.5687 | 0.7004 | 0.7010 | | 0.5322 | 38.98 | 4600 | 0.5692 | 0.6997 | 0.6999 | | 0.5319 | 40.68 | 4800 | 0.5766 | 0.6927 | 0.6946 | | 0.5234 | 42.37 | 5000 | 0.5726 | 0.7060 | 0.7063 | | 0.5279 | 44.07 | 5200 | 0.5664 | 0.7096 | 0.7095 | | 0.5257 | 45.76 | 5400 | 0.5639 | 0.7023 | 0.7026 | | 0.5228 | 47.46 | 5600 | 0.5722 | 0.7006 | 0.7005 | | 0.525 | 49.15 | 5800 | 0.5789 | 0.6988 | 0.6999 | | 0.5217 | 50.85 | 6000 | 0.5644 | 0.7024 | 0.7026 | | 0.5195 | 52.54 | 6200 | 0.5624 | 0.7032 | 0.7031 | | 0.5179 | 54.24 | 6400 | 0.5679 | 0.7021 | 0.7026 | | 0.516 | 55.93 | 6600 | 0.5662 | 0.6996 | 0.6999 | | 0.5151 | 57.63 | 6800 | 0.5637 | 0.6995 | 0.6994 | | 0.5146 | 59.32 | 7000 | 0.5714 | 0.6994 | 0.6999 | | 0.5122 | 61.02 | 7200 | 0.5674 | 0.7024 | 0.7026 | | 0.5114 | 62.71 | 7400 | 0.5661 | 0.7042 | 0.7042 | | 0.5141 | 64.41 | 7600 | 0.5717 | 0.7016 | 0.7021 | | 0.5115 | 66.1 | 7800 | 0.5735 | 0.7047 | 0.7053 | | 0.5046 | 67.8 | 8000 | 0.5721 | 0.7021 | 0.7021 | | 0.5073 | 69.49 | 8200 | 0.5651 | 0.7016 | 0.7015 | | 0.5053 | 71.19 | 8400 | 0.5697 | 0.7041 | 0.7042 | | 0.5056 | 72.88 | 8600 | 0.5703 | 0.7010 | 0.7010 | | 0.5026 | 74.58 | 8800 | 0.5760 | 0.7033 | 0.7037 | | 0.5047 | 76.27 | 9000 | 0.5747 | 0.7009 | 0.7010 | | 0.5052 | 77.97 | 9200 | 0.5714 | 0.7051 | 0.7053 | | 0.506 | 79.66 | 9400 | 0.5708 | 0.7042 | 0.7042 | | 0.5056 | 81.36 | 9600 | 0.5719 | 0.7040 | 0.7042 | | 0.504 | 83.05 | 9800 | 0.5709 | 0.7026 | 0.7026 | | 0.5006 | 84.75 | 10000 | 0.5719 | 0.7019 | 0.7021 | ### 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_mouse_4-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_15M-L8_f

2024-05-03T18:53:05+00:00

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": []}

golf2248/9i661kg

2024-05-03T18:53:15+00:00

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_mouse_4-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_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.6857 - F1 Score: 0.6840 - Accuracy: 0.6840 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6544 | 1.69 | 200 | 0.6273 | 0.6539 | 0.6548 | | 0.6133 | 3.39 | 400 | 0.6103 | 0.6595 | 0.6633 | | 0.592 | 5.08 | 600 | 0.5848 | 0.6849 | 0.6851 | | 0.5788 | 6.78 | 800 | 0.5756 | 0.6915 | 0.6914 | | 0.5698 | 8.47 | 1000 | 0.5724 | 0.6988 | 0.6994 | | 0.5576 | 10.17 | 1200 | 0.5673 | 0.7050 | 0.7058 | | 0.5497 | 11.86 | 1400 | 0.5583 | 0.7080 | 0.7079 | | 0.5383 | 13.56 | 1600 | 0.5708 | 0.6974 | 0.6984 | | 0.5296 | 15.25 | 1800 | 0.5646 | 0.7053 | 0.7053 | | 0.5215 | 16.95 | 2000 | 0.5670 | 0.6980 | 0.6999 | | 0.5131 | 18.64 | 2200 | 0.5732 | 0.7014 | 0.7026 | | 0.5082 | 20.34 | 2400 | 0.5704 | 0.7107 | 0.7111 | | 0.4993 | 22.03 | 2600 | 0.5817 | 0.6977 | 0.6994 | | 0.4907 | 23.73 | 2800 | 0.5626 | 0.7087 | 0.7095 | | 0.4849 | 25.42 | 3000 | 0.5776 | 0.7162 | 0.7164 | | 0.4811 | 27.12 | 3200 | 0.5932 | 0.7007 | 0.7021 | | 0.475 | 28.81 | 3400 | 0.5822 | 0.7147 | 0.7148 | | 0.462 | 30.51 | 3600 | 0.5907 | 0.7125 | 0.7127 | | 0.463 | 32.2 | 3800 | 0.6006 | 0.7152 | 0.7153 | | 0.4569 | 33.9 | 4000 | 0.5908 | 0.7160 | 0.7159 | | 0.4507 | 35.59 | 4200 | 0.6157 | 0.6987 | 0.7005 | | 0.4443 | 37.29 | 4400 | 0.6111 | 0.7048 | 0.7053 | | 0.4404 | 38.98 | 4600 | 0.6148 | 0.7122 | 0.7122 | | 0.436 | 40.68 | 4800 | 0.6259 | 0.7101 | 0.7106 | | 0.4232 | 42.37 | 5000 | 0.6250 | 0.7147 | 0.7148 | | 0.4249 | 44.07 | 5200 | 0.6233 | 0.7070 | 0.7069 | | 0.421 | 45.76 | 5400 | 0.6165 | 0.7136 | 0.7143 | | 0.4161 | 47.46 | 5600 | 0.6374 | 0.7165 | 0.7164 | | 0.4157 | 49.15 | 5800 | 0.6503 | 0.7111 | 0.7111 | | 0.4082 | 50.85 | 6000 | 0.6373 | 0.7192 | 0.7191 | | 0.4085 | 52.54 | 6200 | 0.6399 | 0.7153 | 0.7153 | | 0.3995 | 54.24 | 6400 | 0.6552 | 0.7049 | 0.7053 | | 0.3972 | 55.93 | 6600 | 0.6393 | 0.7080 | 0.7079 | | 0.3917 | 57.63 | 6800 | 0.6566 | 0.7165 | 0.7164 | | 0.3943 | 59.32 | 7000 | 0.6516 | 0.7131 | 0.7132 | | 0.3912 | 61.02 | 7200 | 0.6507 | 0.7111 | 0.7111 | | 0.3865 | 62.71 | 7400 | 0.6577 | 0.7079 | 0.7079 | | 0.3877 | 64.41 | 7600 | 0.6608 | 0.7123 | 0.7127 | | 0.3805 | 66.1 | 7800 | 0.6760 | 0.7120 | 0.7122 | | 0.3721 | 67.8 | 8000 | 0.6708 | 0.7086 | 0.7084 | | 0.3792 | 69.49 | 8200 | 0.6642 | 0.7091 | 0.7090 | | 0.3775 | 71.19 | 8400 | 0.6657 | 0.7107 | 0.7106 | | 0.3761 | 72.88 | 8600 | 0.6629 | 0.7096 | 0.7095 | | 0.3703 | 74.58 | 8800 | 0.6837 | 0.7126 | 0.7127 | | 0.3693 | 76.27 | 9000 | 0.6859 | 0.7061 | 0.7063 | | 0.3669 | 77.97 | 9200 | 0.6852 | 0.7084 | 0.7084 | | 0.3738 | 79.66 | 9400 | 0.6796 | 0.7074 | 0.7074 | | 0.3689 | 81.36 | 9600 | 0.6798 | 0.7074 | 0.7074 | | 0.3635 | 83.05 | 9800 | 0.6820 | 0.7080 | 0.7079 | | 0.3644 | 84.75 | 10000 | 0.6825 | 0.7079 | 0.7079 | ### 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_mouse_4-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_15M-L32_f

2024-05-03T18:53:55+00:00

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": []}

fmshahata/phi-moe-switch_2exp

2024-05-03T18:54:16+00:00

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_mouse_3-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_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5011 - F1 Score: 0.7866 - Accuracy: 0.7866 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6541 | 13.33 | 200 | 0.5477 | 0.7214 | 0.7238 | | 0.5726 | 26.67 | 400 | 0.4790 | 0.7776 | 0.7782 | | 0.519 | 40.0 | 600 | 0.4452 | 0.8067 | 0.8075 | | 0.4727 | 53.33 | 800 | 0.4042 | 0.8324 | 0.8326 | | 0.4311 | 66.67 | 1000 | 0.3913 | 0.8617 | 0.8619 | | 0.3989 | 80.0 | 1200 | 0.3878 | 0.8452 | 0.8452 | | 0.3732 | 93.33 | 1400 | 0.3857 | 0.8535 | 0.8536 | | 0.3492 | 106.67 | 1600 | 0.3827 | 0.8490 | 0.8494 | | 0.3338 | 120.0 | 1800 | 0.3922 | 0.8575 | 0.8577 | | 0.3175 | 133.33 | 2000 | 0.3908 | 0.8493 | 0.8494 | | 0.3048 | 146.67 | 2200 | 0.4073 | 0.8575 | 0.8577 | | 0.2949 | 160.0 | 2400 | 0.4128 | 0.8450 | 0.8452 | | 0.2834 | 173.33 | 2600 | 0.4232 | 0.8619 | 0.8619 | | 0.2721 | 186.67 | 2800 | 0.4289 | 0.8452 | 0.8452 | | 0.2632 | 200.0 | 3000 | 0.4327 | 0.8449 | 0.8452 | | 0.2587 | 213.33 | 3200 | 0.4534 | 0.8452 | 0.8452 | | 0.2484 | 226.67 | 3400 | 0.4390 | 0.8534 | 0.8536 | | 0.2457 | 240.0 | 3600 | 0.4784 | 0.8326 | 0.8326 | | 0.2434 | 253.33 | 3800 | 0.4525 | 0.8452 | 0.8452 | | 0.2357 | 266.67 | 4000 | 0.4488 | 0.8368 | 0.8368 | | 0.2277 | 280.0 | 4200 | 0.4695 | 0.8408 | 0.8410 | | 0.2331 | 293.33 | 4400 | 0.4660 | 0.8367 | 0.8368 | | 0.2232 | 306.67 | 4600 | 0.4873 | 0.8405 | 0.8410 | | 0.2194 | 320.0 | 4800 | 0.4744 | 0.8365 | 0.8368 | | 0.216 | 333.33 | 5000 | 0.4685 | 0.8282 | 0.8285 | | 0.213 | 346.67 | 5200 | 0.4692 | 0.8324 | 0.8326 | | 0.2118 | 360.0 | 5400 | 0.4635 | 0.8325 | 0.8326 | | 0.2128 | 373.33 | 5600 | 0.4588 | 0.8282 | 0.8285 | | 0.2054 | 386.67 | 5800 | 0.4728 | 0.8324 | 0.8326 | | 0.2001 | 400.0 | 6000 | 0.4711 | 0.8282 | 0.8285 | | 0.1999 | 413.33 | 6200 | 0.4887 | 0.8325 | 0.8326 | | 0.1954 | 426.67 | 6400 | 0.4876 | 0.8281 | 0.8285 | | 0.1946 | 440.0 | 6600 | 0.4965 | 0.8240 | 0.8243 | | 0.1897 | 453.33 | 6800 | 0.4993 | 0.8240 | 0.8243 | | 0.1918 | 466.67 | 7000 | 0.5031 | 0.8199 | 0.8201 | | 0.191 | 480.0 | 7200 | 0.5048 | 0.8240 | 0.8243 | | 0.1854 | 493.33 | 7400 | 0.4900 | 0.8282 | 0.8285 | | 0.1861 | 506.67 | 7600 | 0.4985 | 0.8200 | 0.8201 | | 0.1798 | 520.0 | 7800 | 0.5088 | 0.8241 | 0.8243 | | 0.1871 | 533.33 | 8000 | 0.4990 | 0.8282 | 0.8285 | | 0.184 | 546.67 | 8200 | 0.5025 | 0.8365 | 0.8368 | | 0.1864 | 560.0 | 8400 | 0.5094 | 0.8282 | 0.8285 | | 0.18 | 573.33 | 8600 | 0.5180 | 0.8240 | 0.8243 | | 0.1826 | 586.67 | 8800 | 0.5049 | 0.8241 | 0.8243 | | 0.1822 | 600.0 | 9000 | 0.5036 | 0.8241 | 0.8243 | | 0.1745 | 613.33 | 9200 | 0.5176 | 0.8282 | 0.8285 | | 0.1757 | 626.67 | 9400 | 0.5141 | 0.8241 | 0.8243 | | 0.1785 | 640.0 | 9600 | 0.5104 | 0.8241 | 0.8243 | | 0.1697 | 653.33 | 9800 | 0.5116 | 0.8241 | 0.8243 | | 0.1809 | 666.67 | 10000 | 0.5110 | 0.8241 | 0.8243 | ### 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_mouse_3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_15M-L1_f

2024-05-03T18:54:17+00:00

{}

Zefirkefir/sn25-5-3

2024-05-03T18:54:30+00:00

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_mouse_3-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_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.9891 - F1 Score: 0.8281 - Accuracy: 0.8285 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6046 | 13.33 | 200 | 0.4456 | 0.7863 | 0.7866 | | 0.4398 | 26.67 | 400 | 0.3750 | 0.8479 | 0.8494 | | 0.3513 | 40.0 | 600 | 0.3555 | 0.8701 | 0.8703 | | 0.2989 | 53.33 | 800 | 0.3583 | 0.8743 | 0.8745 | | 0.2647 | 66.67 | 1000 | 0.3853 | 0.8570 | 0.8577 | | 0.2338 | 80.0 | 1200 | 0.3965 | 0.8529 | 0.8536 | | 0.2133 | 93.33 | 1400 | 0.3871 | 0.8490 | 0.8494 | | 0.184 | 106.67 | 1600 | 0.4219 | 0.8529 | 0.8536 | | 0.172 | 120.0 | 1800 | 0.4005 | 0.8618 | 0.8619 | | 0.1484 | 133.33 | 2000 | 0.4358 | 0.8661 | 0.8661 | | 0.1381 | 146.67 | 2200 | 0.4182 | 0.8660 | 0.8661 | | 0.1287 | 160.0 | 2400 | 0.4432 | 0.8744 | 0.8745 | | 0.1179 | 173.33 | 2600 | 0.4558 | 0.8703 | 0.8703 | | 0.112 | 186.67 | 2800 | 0.4523 | 0.8448 | 0.8452 | | 0.1042 | 200.0 | 3000 | 0.4517 | 0.8742 | 0.8745 | | 0.094 | 213.33 | 3200 | 0.4399 | 0.8659 | 0.8661 | | 0.0905 | 226.67 | 3400 | 0.4493 | 0.8619 | 0.8619 | | 0.0864 | 240.0 | 3600 | 0.4652 | 0.8493 | 0.8494 | | 0.0823 | 253.33 | 3800 | 0.4940 | 0.8577 | 0.8577 | | 0.0777 | 266.67 | 4000 | 0.5251 | 0.8703 | 0.8703 | | 0.0731 | 280.0 | 4200 | 0.5398 | 0.8619 | 0.8619 | | 0.0718 | 293.33 | 4400 | 0.5079 | 0.8577 | 0.8577 | | 0.0649 | 306.67 | 4600 | 0.5619 | 0.8661 | 0.8661 | | 0.064 | 320.0 | 4800 | 0.5438 | 0.8451 | 0.8452 | | 0.0629 | 333.33 | 5000 | 0.5502 | 0.8536 | 0.8536 | | 0.0588 | 346.67 | 5200 | 0.5091 | 0.8661 | 0.8661 | | 0.0575 | 360.0 | 5400 | 0.5668 | 0.8492 | 0.8494 | | 0.0562 | 373.33 | 5600 | 0.5382 | 0.8826 | 0.8828 | | 0.0532 | 386.67 | 5800 | 0.5470 | 0.8618 | 0.8619 | | 0.0517 | 400.0 | 6000 | 0.5525 | 0.8536 | 0.8536 | | 0.054 | 413.33 | 6200 | 0.5554 | 0.8494 | 0.8494 | | 0.0497 | 426.67 | 6400 | 0.6015 | 0.8577 | 0.8577 | | 0.0509 | 440.0 | 6600 | 0.5405 | 0.8618 | 0.8619 | | 0.0461 | 453.33 | 6800 | 0.5920 | 0.8660 | 0.8661 | | 0.0466 | 466.67 | 7000 | 0.5824 | 0.8744 | 0.8745 | | 0.0429 | 480.0 | 7200 | 0.6150 | 0.8744 | 0.8745 | | 0.0416 | 493.33 | 7400 | 0.5984 | 0.8577 | 0.8577 | | 0.0401 | 506.67 | 7600 | 0.6160 | 0.8702 | 0.8703 | | 0.0433 | 520.0 | 7800 | 0.6118 | 0.8576 | 0.8577 | | 0.0412 | 533.33 | 8000 | 0.5844 | 0.8661 | 0.8661 | | 0.0381 | 546.67 | 8200 | 0.6168 | 0.8576 | 0.8577 | | 0.0425 | 560.0 | 8400 | 0.5694 | 0.8703 | 0.8703 | | 0.0376 | 573.33 | 8600 | 0.5914 | 0.8786 | 0.8787 | | 0.0389 | 586.67 | 8800 | 0.5895 | 0.8786 | 0.8787 | | 0.0391 | 600.0 | 9000 | 0.5927 | 0.8745 | 0.8745 | | 0.0392 | 613.33 | 9200 | 0.6015 | 0.8619 | 0.8619 | | 0.0374 | 626.67 | 9400 | 0.6041 | 0.8745 | 0.8745 | | 0.0406 | 640.0 | 9600 | 0.6066 | 0.8660 | 0.8661 | | 0.037 | 653.33 | 9800 | 0.5993 | 0.8661 | 0.8661 | | 0.0386 | 666.67 | 10000 | 0.5986 | 0.8703 | 0.8703 | ### 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_mouse_3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_15M-L8_f

2024-05-03T18:55:01+00:00

{"license": "apache-2.0"}

MoFarouk/Mestests

2024-05-03T18:56:26+00:00

{}

zoe145768586678/odyssey-test-9

2024-05-03T18:56:27+00:00

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_mouse_2-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_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.2779 - F1 Score: 0.8780 - Accuracy: 0.8780 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4865 | 9.52 | 200 | 0.3900 | 0.7925 | 0.7927 | | 0.3502 | 19.05 | 400 | 0.3748 | 0.8132 | 0.8140 | | 0.3093 | 28.57 | 600 | 0.3435 | 0.8445 | 0.8445 | | 0.2862 | 38.1 | 800 | 0.3369 | 0.8596 | 0.8598 | | 0.2678 | 47.62 | 1000 | 0.3191 | 0.8628 | 0.8628 | | 0.2521 | 57.14 | 1200 | 0.3183 | 0.8811 | 0.8811 | | 0.2367 | 66.67 | 1400 | 0.3310 | 0.8871 | 0.8872 | | 0.2323 | 76.19 | 1600 | 0.3365 | 0.8750 | 0.875 | | 0.2212 | 85.71 | 1800 | 0.3334 | 0.8750 | 0.875 | | 0.2104 | 95.24 | 2000 | 0.3484 | 0.8902 | 0.8902 | | 0.208 | 104.76 | 2200 | 0.3370 | 0.8779 | 0.8780 | | 0.2009 | 114.29 | 2400 | 0.3393 | 0.8902 | 0.8902 | | 0.1977 | 123.81 | 2600 | 0.3537 | 0.8810 | 0.8811 | | 0.1943 | 133.33 | 2800 | 0.3527 | 0.8780 | 0.8780 | | 0.1876 | 142.86 | 3000 | 0.3524 | 0.8780 | 0.8780 | | 0.182 | 152.38 | 3200 | 0.3624 | 0.8779 | 0.8780 | | 0.1797 | 161.9 | 3400 | 0.3658 | 0.8780 | 0.8780 | | 0.1735 | 171.43 | 3600 | 0.3764 | 0.8658 | 0.8659 | | 0.1693 | 180.95 | 3800 | 0.3777 | 0.8780 | 0.8780 | | 0.1655 | 190.48 | 4000 | 0.3893 | 0.8719 | 0.8720 | | 0.1637 | 200.0 | 4200 | 0.3935 | 0.8628 | 0.8628 | | 0.1606 | 209.52 | 4400 | 0.3768 | 0.8719 | 0.8720 | | 0.1592 | 219.05 | 4600 | 0.3964 | 0.8719 | 0.8720 | | 0.1559 | 228.57 | 4800 | 0.4098 | 0.8658 | 0.8659 | | 0.1559 | 238.1 | 5000 | 0.4274 | 0.8567 | 0.8567 | | 0.153 | 247.62 | 5200 | 0.4074 | 0.8689 | 0.8689 | | 0.1479 | 257.14 | 5400 | 0.4058 | 0.8689 | 0.8689 | | 0.1458 | 266.67 | 5600 | 0.4290 | 0.8628 | 0.8628 | | 0.1473 | 276.19 | 5800 | 0.4177 | 0.8567 | 0.8567 | | 0.1406 | 285.71 | 6000 | 0.4082 | 0.8719 | 0.8720 | | 0.1417 | 295.24 | 6200 | 0.4173 | 0.8628 | 0.8628 | | 0.1401 | 304.76 | 6400 | 0.4135 | 0.8567 | 0.8567 | | 0.1396 | 314.29 | 6600 | 0.4015 | 0.8750 | 0.875 | | 0.1398 | 323.81 | 6800 | 0.4065 | 0.8567 | 0.8567 | | 0.1351 | 333.33 | 7000 | 0.4180 | 0.8659 | 0.8659 | | 0.1369 | 342.86 | 7200 | 0.4041 | 0.8659 | 0.8659 | | 0.1295 | 352.38 | 7400 | 0.4232 | 0.8689 | 0.8689 | | 0.1362 | 361.9 | 7600 | 0.4126 | 0.8597 | 0.8598 | | 0.1354 | 371.43 | 7800 | 0.4204 | 0.8689 | 0.8689 | | 0.1271 | 380.95 | 8000 | 0.4247 | 0.8658 | 0.8659 | | 0.1326 | 390.48 | 8200 | 0.4123 | 0.8658 | 0.8659 | | 0.1288 | 400.0 | 8400 | 0.4256 | 0.8628 | 0.8628 | | 0.128 | 409.52 | 8600 | 0.4215 | 0.8750 | 0.875 | | 0.1237 | 419.05 | 8800 | 0.4332 | 0.8597 | 0.8598 | | 0.1299 | 428.57 | 9000 | 0.4228 | 0.8597 | 0.8598 | | 0.1278 | 438.1 | 9200 | 0.4213 | 0.8689 | 0.8689 | | 0.1271 | 447.62 | 9400 | 0.4287 | 0.8597 | 0.8598 | | 0.1241 | 457.14 | 9600 | 0.4240 | 0.8689 | 0.8689 | | 0.1241 | 466.67 | 9800 | 0.4266 | 0.8689 | 0.8689 | | 0.124 | 476.19 | 10000 | 0.4254 | 0.8689 | 0.8689 | ### 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_mouse_2-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_15M-L1_f

2024-05-03T18:56:34+00:00

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_mouse_3-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_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset. It achieves the following results on the evaluation set: - Loss: 1.3687 - F1 Score: 0.8368 - Accuracy: 0.8368 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5528 | 13.33 | 200 | 0.3800 | 0.8360 | 0.8368 | | 0.3385 | 26.67 | 400 | 0.3713 | 0.8654 | 0.8661 | | 0.2527 | 40.0 | 600 | 0.3684 | 0.8619 | 0.8619 | | 0.1912 | 53.33 | 800 | 0.4153 | 0.8535 | 0.8536 | | 0.1505 | 66.67 | 1000 | 0.4190 | 0.8614 | 0.8619 | | 0.1168 | 80.0 | 1200 | 0.4873 | 0.8698 | 0.8703 | | 0.0996 | 93.33 | 1400 | 0.4635 | 0.8536 | 0.8536 | | 0.0851 | 106.67 | 1600 | 0.5159 | 0.8529 | 0.8536 | | 0.0699 | 120.0 | 1800 | 0.5508 | 0.8784 | 0.8787 | | 0.0595 | 133.33 | 2000 | 0.5934 | 0.8574 | 0.8577 | | 0.0505 | 146.67 | 2200 | 0.6054 | 0.8576 | 0.8577 | | 0.0504 | 160.0 | 2400 | 0.5825 | 0.8701 | 0.8703 | | 0.0411 | 173.33 | 2600 | 0.6155 | 0.8619 | 0.8619 | | 0.0388 | 186.67 | 2800 | 0.6479 | 0.8661 | 0.8661 | | 0.0359 | 200.0 | 3000 | 0.6592 | 0.8615 | 0.8619 | | 0.0327 | 213.33 | 3200 | 0.7244 | 0.8702 | 0.8703 | | 0.0315 | 226.67 | 3400 | 0.6512 | 0.8577 | 0.8577 | | 0.0306 | 240.0 | 3600 | 0.6895 | 0.8576 | 0.8577 | | 0.029 | 253.33 | 3800 | 0.7618 | 0.8577 | 0.8577 | | 0.0271 | 266.67 | 4000 | 0.7633 | 0.8534 | 0.8536 | | 0.022 | 280.0 | 4200 | 0.7738 | 0.8492 | 0.8494 | | 0.0238 | 293.33 | 4400 | 0.7606 | 0.8618 | 0.8619 | | 0.0195 | 306.67 | 4600 | 0.7799 | 0.8659 | 0.8661 | | 0.0208 | 320.0 | 4800 | 0.7812 | 0.8617 | 0.8619 | | 0.0182 | 333.33 | 5000 | 0.7868 | 0.8744 | 0.8745 | | 0.0203 | 346.67 | 5200 | 0.8271 | 0.8739 | 0.8745 | | 0.0178 | 360.0 | 5400 | 0.7416 | 0.8703 | 0.8703 | | 0.016 | 373.33 | 5600 | 0.7589 | 0.8744 | 0.8745 | | 0.0178 | 386.67 | 5800 | 0.7254 | 0.8702 | 0.8703 | | 0.016 | 400.0 | 6000 | 0.7839 | 0.8619 | 0.8619 | | 0.0136 | 413.33 | 6200 | 0.8584 | 0.8619 | 0.8619 | | 0.0143 | 426.67 | 6400 | 0.8470 | 0.8783 | 0.8787 | | 0.0135 | 440.0 | 6600 | 0.8244 | 0.8744 | 0.8745 | | 0.0149 | 453.33 | 6800 | 0.7704 | 0.8786 | 0.8787 | | 0.0146 | 466.67 | 7000 | 0.8063 | 0.8744 | 0.8745 | | 0.0116 | 480.0 | 7200 | 0.8048 | 0.8619 | 0.8619 | | 0.01 | 493.33 | 7400 | 0.8597 | 0.8744 | 0.8745 | | 0.0113 | 506.67 | 7600 | 0.8415 | 0.8660 | 0.8661 | | 0.0096 | 520.0 | 7800 | 0.8698 | 0.8786 | 0.8787 | | 0.011 | 533.33 | 8000 | 0.8537 | 0.8619 | 0.8619 | | 0.0095 | 546.67 | 8200 | 0.8248 | 0.8786 | 0.8787 | | 0.0099 | 560.0 | 8400 | 0.8428 | 0.8701 | 0.8703 | | 0.0093 | 573.33 | 8600 | 0.8373 | 0.8619 | 0.8619 | | 0.0099 | 586.67 | 8800 | 0.8226 | 0.8619 | 0.8619 | | 0.0075 | 600.0 | 9000 | 0.8746 | 0.8744 | 0.8745 | | 0.0087 | 613.33 | 9200 | 0.8475 | 0.8661 | 0.8661 | | 0.0083 | 626.67 | 9400 | 0.8725 | 0.8661 | 0.8661 | | 0.0101 | 640.0 | 9600 | 0.8741 | 0.8619 | 0.8619 | | 0.007 | 653.33 | 9800 | 0.8736 | 0.8702 | 0.8703 | | 0.0107 | 666.67 | 10000 | 0.8653 | 0.8661 | 0.8661 | ### 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_mouse_3-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_15M-L32_f

2024-05-03T18:56:34+00:00

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_mouse_2-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_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6301 - F1 Score: 0.8780 - Accuracy: 0.8780 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4196 | 9.52 | 200 | 0.3453 | 0.8411 | 0.8415 | | 0.2843 | 19.05 | 400 | 0.3329 | 0.8658 | 0.8659 | | 0.2436 | 28.57 | 600 | 0.3090 | 0.8840 | 0.8841 | | 0.217 | 38.1 | 800 | 0.3200 | 0.8841 | 0.8841 | | 0.196 | 47.62 | 1000 | 0.3318 | 0.8872 | 0.8872 | | 0.1761 | 57.14 | 1200 | 0.3589 | 0.8658 | 0.8659 | | 0.1625 | 66.67 | 1400 | 0.3791 | 0.8720 | 0.8720 | | 0.1532 | 76.19 | 1600 | 0.4121 | 0.8627 | 0.8628 | | 0.1401 | 85.71 | 1800 | 0.3999 | 0.8658 | 0.8659 | | 0.1265 | 95.24 | 2000 | 0.4633 | 0.8749 | 0.875 | | 0.1219 | 104.76 | 2200 | 0.4277 | 0.8689 | 0.8689 | | 0.1168 | 114.29 | 2400 | 0.4125 | 0.8659 | 0.8659 | | 0.1082 | 123.81 | 2600 | 0.4537 | 0.8689 | 0.8689 | | 0.0991 | 133.33 | 2800 | 0.4396 | 0.8780 | 0.8780 | | 0.0917 | 142.86 | 3000 | 0.4864 | 0.8749 | 0.875 | | 0.0843 | 152.38 | 3200 | 0.4842 | 0.8720 | 0.8720 | | 0.0858 | 161.9 | 3400 | 0.4928 | 0.8628 | 0.8628 | | 0.0744 | 171.43 | 3600 | 0.5215 | 0.8750 | 0.875 | | 0.0724 | 180.95 | 3800 | 0.5353 | 0.8658 | 0.8659 | | 0.0697 | 190.48 | 4000 | 0.5285 | 0.8841 | 0.8841 | | 0.0643 | 200.0 | 4200 | 0.5673 | 0.8780 | 0.8780 | | 0.0653 | 209.52 | 4400 | 0.5322 | 0.8841 | 0.8841 | | 0.0614 | 219.05 | 4600 | 0.5746 | 0.8750 | 0.875 | | 0.0534 | 228.57 | 4800 | 0.6451 | 0.8719 | 0.8720 | | 0.0571 | 238.1 | 5000 | 0.6393 | 0.8746 | 0.875 | | 0.0535 | 247.62 | 5200 | 0.5712 | 0.8841 | 0.8841 | | 0.0496 | 257.14 | 5400 | 0.6100 | 0.8779 | 0.8780 | | 0.0464 | 266.67 | 5600 | 0.6278 | 0.8871 | 0.8872 | | 0.0495 | 276.19 | 5800 | 0.6104 | 0.8840 | 0.8841 | | 0.0446 | 285.71 | 6000 | 0.6431 | 0.8779 | 0.8780 | | 0.0449 | 295.24 | 6200 | 0.6230 | 0.8841 | 0.8841 | | 0.0427 | 304.76 | 6400 | 0.6259 | 0.8750 | 0.875 | | 0.0434 | 314.29 | 6600 | 0.6362 | 0.8810 | 0.8811 | | 0.0426 | 323.81 | 6800 | 0.6241 | 0.8840 | 0.8841 | | 0.0403 | 333.33 | 7000 | 0.6379 | 0.8871 | 0.8872 | | 0.0391 | 342.86 | 7200 | 0.6461 | 0.8780 | 0.8780 | | 0.0377 | 352.38 | 7400 | 0.6628 | 0.8841 | 0.8841 | | 0.0372 | 361.9 | 7600 | 0.6478 | 0.8901 | 0.8902 | | 0.0347 | 371.43 | 7800 | 0.6833 | 0.8870 | 0.8872 | | 0.0342 | 380.95 | 8000 | 0.6775 | 0.8871 | 0.8872 | | 0.0345 | 390.48 | 8200 | 0.6877 | 0.8870 | 0.8872 | | 0.0341 | 400.0 | 8400 | 0.6865 | 0.8932 | 0.8933 | | 0.0353 | 409.52 | 8600 | 0.6739 | 0.8870 | 0.8872 | | 0.0325 | 419.05 | 8800 | 0.6810 | 0.8870 | 0.8872 | | 0.0341 | 428.57 | 9000 | 0.6819 | 0.8870 | 0.8872 | | 0.0315 | 438.1 | 9200 | 0.6780 | 0.8870 | 0.8872 | | 0.0305 | 447.62 | 9400 | 0.6929 | 0.8870 | 0.8872 | | 0.029 | 457.14 | 9600 | 0.6948 | 0.8870 | 0.8872 | | 0.0291 | 466.67 | 9800 | 0.6944 | 0.8870 | 0.8872 | | 0.0317 | 476.19 | 10000 | 0.6863 | 0.8901 | 0.8902 | ### 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_mouse_2-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_15M-L8_f

2024-05-03T18:57:20+00:00

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/bt5ia0i

2024-05-03T18:57:35+00:00

{}

karanpreetlm10/falcon-7b-sharded-bf16-finetuned-mental-health-conversational

2024-05-03T18:57:41+00:00

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_mouse_2-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_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5027 - F1 Score: 0.8750 - Accuracy: 0.875 ## 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 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3806 | 9.52 | 200 | 0.3269 | 0.8597 | 0.8598 | | 0.2415 | 19.05 | 400 | 0.3152 | 0.8658 | 0.8659 | | 0.1924 | 28.57 | 600 | 0.3159 | 0.8811 | 0.8811 | | 0.16 | 38.1 | 800 | 0.3525 | 0.8689 | 0.8689 | | 0.1335 | 47.62 | 1000 | 0.4108 | 0.8780 | 0.8780 | | 0.1085 | 57.14 | 1200 | 0.4464 | 0.8749 | 0.875 | | 0.0881 | 66.67 | 1400 | 0.4792 | 0.8689 | 0.8689 | | 0.073 | 76.19 | 1600 | 0.5455 | 0.8780 | 0.8780 | | 0.0586 | 85.71 | 1800 | 0.5630 | 0.8811 | 0.8811 | | 0.049 | 95.24 | 2000 | 0.6062 | 0.8869 | 0.8872 | | 0.0428 | 104.76 | 2200 | 0.5581 | 0.8994 | 0.8994 | | 0.0415 | 114.29 | 2400 | 0.5765 | 0.8871 | 0.8872 | | 0.0343 | 123.81 | 2600 | 0.5885 | 0.8902 | 0.8902 | | 0.0314 | 133.33 | 2800 | 0.6035 | 0.8872 | 0.8872 | | 0.0289 | 142.86 | 3000 | 0.6384 | 0.8840 | 0.8841 | | 0.0236 | 152.38 | 3200 | 0.7343 | 0.8871 | 0.8872 | | 0.0282 | 161.9 | 3400 | 0.7517 | 0.8748 | 0.875 | | 0.0214 | 171.43 | 3600 | 0.7033 | 0.8931 | 0.8933 | | 0.0207 | 180.95 | 3800 | 0.7171 | 0.8839 | 0.8841 | | 0.0199 | 190.48 | 4000 | 0.7869 | 0.8840 | 0.8841 | | 0.0173 | 200.0 | 4200 | 0.8030 | 0.8841 | 0.8841 | | 0.0178 | 209.52 | 4400 | 0.7712 | 0.8808 | 0.8811 | | 0.0174 | 219.05 | 4600 | 0.6971 | 0.8963 | 0.8963 | | 0.0138 | 228.57 | 4800 | 0.8396 | 0.8658 | 0.8659 | | 0.0138 | 238.1 | 5000 | 0.7916 | 0.8808 | 0.8811 | | 0.0128 | 247.62 | 5200 | 0.7458 | 0.8963 | 0.8963 | | 0.0127 | 257.14 | 5400 | 0.8319 | 0.8901 | 0.8902 | | 0.0109 | 266.67 | 5600 | 0.8372 | 0.8870 | 0.8872 | | 0.0111 | 276.19 | 5800 | 0.8510 | 0.8901 | 0.8902 | | 0.0112 | 285.71 | 6000 | 0.8220 | 0.8870 | 0.8872 | | 0.0097 | 295.24 | 6200 | 0.8300 | 0.8901 | 0.8902 | | 0.0102 | 304.76 | 6400 | 0.8657 | 0.8870 | 0.8872 | | 0.01 | 314.29 | 6600 | 0.8303 | 0.8870 | 0.8872 | | 0.0088 | 323.81 | 6800 | 0.8713 | 0.8809 | 0.8811 | | 0.0087 | 333.33 | 7000 | 0.8464 | 0.8901 | 0.8902 | | 0.0084 | 342.86 | 7200 | 0.8823 | 0.8932 | 0.8933 | | 0.007 | 352.38 | 7400 | 0.9236 | 0.8840 | 0.8841 | | 0.0088 | 361.9 | 7600 | 0.8623 | 0.8870 | 0.8872 | | 0.0065 | 371.43 | 7800 | 0.8455 | 0.8932 | 0.8933 | | 0.0068 | 380.95 | 8000 | 0.8949 | 0.8963 | 0.8963 | | 0.0084 | 390.48 | 8200 | 0.8279 | 0.8962 | 0.8963 | | 0.0063 | 400.0 | 8400 | 0.8768 | 0.8901 | 0.8902 | | 0.0057 | 409.52 | 8600 | 0.9100 | 0.8901 | 0.8902 | | 0.0069 | 419.05 | 8800 | 0.8906 | 0.8932 | 0.8933 | | 0.0065 | 428.57 | 9000 | 0.9110 | 0.8840 | 0.8841 | | 0.0069 | 438.1 | 9200 | 0.8759 | 0.8901 | 0.8902 | | 0.0053 | 447.62 | 9400 | 0.9003 | 0.8932 | 0.8933 | | 0.0046 | 457.14 | 9600 | 0.9264 | 0.8901 | 0.8902 | | 0.0046 | 466.67 | 9800 | 0.9214 | 0.8963 | 0.8963 | | 0.0051 | 476.19 | 10000 | 0.9170 | 0.8963 | 0.8963 | ### 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_mouse_2-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_15M-L32_f

2024-05-03T18:57:46+00:00

transformers

# Uploaded model - **Developed by:** yeetech19 - **License:** apache-2.0 - **Finetuned from model :** zhichen/Llama3-Chinese 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": "zhichen/Llama3-Chinese"}

yeetech19/lora_adapter_v2

2024-05-03T18:57:46+00:00

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_0.01_8_5e-05

2024-05-03T18:58:16+00:00

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_splice_reconstructed-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_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset. It achieves the following results on the evaluation set: - Loss: 0.4189 - F1 Score: 0.8268 - Accuracy: 0.8262 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.9723 | 0.7 | 200 | 0.9473 | 0.4075 | 0.5644 | | 0.9335 | 1.4 | 400 | 0.8961 | 0.5071 | 0.5804 | | 0.7745 | 2.1 | 600 | 0.6515 | 0.7094 | 0.7100 | | 0.6342 | 2.8 | 800 | 0.5971 | 0.7378 | 0.7376 | | 0.601 | 3.5 | 1000 | 0.5694 | 0.7518 | 0.7508 | | 0.5767 | 4.2 | 1200 | 0.5445 | 0.7677 | 0.7674 | | 0.5527 | 4.9 | 1400 | 0.5276 | 0.7729 | 0.7722 | | 0.5385 | 5.59 | 1600 | 0.5263 | 0.7760 | 0.7751 | | 0.5382 | 6.29 | 1800 | 0.5080 | 0.7836 | 0.7830 | | 0.5148 | 6.99 | 2000 | 0.5005 | 0.7827 | 0.7821 | | 0.5161 | 7.69 | 2200 | 0.4859 | 0.7865 | 0.7872 | | 0.5107 | 8.39 | 2400 | 0.4937 | 0.7897 | 0.7889 | | 0.5047 | 9.09 | 2600 | 0.4884 | 0.7899 | 0.7891 | | 0.4975 | 9.79 | 2800 | 0.4804 | 0.7944 | 0.7937 | | 0.4949 | 10.49 | 3000 | 0.4809 | 0.7965 | 0.7957 | | 0.4894 | 11.19 | 3200 | 0.4876 | 0.7936 | 0.7926 | | 0.4921 | 11.89 | 3400 | 0.4676 | 0.7984 | 0.7979 | | 0.4804 | 12.59 | 3600 | 0.4771 | 0.7979 | 0.7970 | | 0.4785 | 13.29 | 3800 | 0.4690 | 0.8005 | 0.7996 | | 0.4801 | 13.99 | 4000 | 0.4731 | 0.8043 | 0.8034 | | 0.4741 | 14.69 | 4200 | 0.4591 | 0.8068 | 0.8062 | | 0.4781 | 15.38 | 4400 | 0.4695 | 0.8008 | 0.7999 | | 0.4698 | 16.08 | 4600 | 0.4857 | 0.7940 | 0.7931 | | 0.4668 | 16.78 | 4800 | 0.4646 | 0.8041 | 0.8032 | | 0.4631 | 17.48 | 5000 | 0.4707 | 0.8016 | 0.8005 | | 0.4611 | 18.18 | 5200 | 0.4489 | 0.8111 | 0.8106 | | 0.4625 | 18.88 | 5400 | 0.4618 | 0.8052 | 0.8043 | | 0.4656 | 19.58 | 5600 | 0.4474 | 0.8121 | 0.8115 | | 0.4619 | 20.28 | 5800 | 0.4488 | 0.8120 | 0.8113 | | 0.4579 | 20.98 | 6000 | 0.4470 | 0.8124 | 0.8117 | | 0.4604 | 21.68 | 6200 | 0.4558 | 0.8100 | 0.8091 | | 0.4515 | 22.38 | 6400 | 0.4484 | 0.8118 | 0.8110 | | 0.4469 | 23.08 | 6600 | 0.4473 | 0.8136 | 0.8128 | | 0.4531 | 23.78 | 6800 | 0.4492 | 0.8103 | 0.8095 | | 0.4489 | 24.48 | 7000 | 0.4544 | 0.8104 | 0.8095 | | 0.45 | 25.17 | 7200 | 0.4502 | 0.8119 | 0.8110 | | 0.448 | 25.87 | 7400 | 0.4499 | 0.8141 | 0.8132 | | 0.448 | 26.57 | 7600 | 0.4580 | 0.8096 | 0.8086 | | 0.4459 | 27.27 | 7800 | 0.4528 | 0.8128 | 0.8119 | | 0.4497 | 27.97 | 8000 | 0.4429 | 0.8158 | 0.8150 | | 0.4456 | 28.67 | 8200 | 0.4539 | 0.8115 | 0.8106 | | 0.4408 | 29.37 | 8400 | 0.4443 | 0.8146 | 0.8137 | | 0.4427 | 30.07 | 8600 | 0.4370 | 0.8185 | 0.8178 | | 0.4435 | 30.77 | 8800 | 0.4456 | 0.8146 | 0.8137 | | 0.4419 | 31.47 | 9000 | 0.4465 | 0.8130 | 0.8121 | | 0.4429 | 32.17 | 9200 | 0.4433 | 0.8152 | 0.8143 | | 0.4408 | 32.87 | 9400 | 0.4452 | 0.8141 | 0.8132 | | 0.4474 | 33.57 | 9600 | 0.4457 | 0.8146 | 0.8137 | | 0.4383 | 34.27 | 9800 | 0.4440 | 0.8156 | 0.8148 | | 0.4384 | 34.97 | 10000 | 0.4441 | 0.8150 | 0.8141 | ### 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_splice_reconstructed-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_4096_512_15M-L1_f

2024-05-03T18:59:52+00:00

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_splice_reconstructed-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_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset. It achieves the following results on the evaluation set: - Loss: 0.3625 - F1 Score: 0.8558 - Accuracy: 0.8551 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.9617 | 0.7 | 200 | 0.9080 | 0.4586 | 0.5623 | | 0.7438 | 1.4 | 400 | 0.5836 | 0.7380 | 0.7378 | | 0.5658 | 2.1 | 600 | 0.5211 | 0.7703 | 0.7696 | | 0.5131 | 2.8 | 800 | 0.4952 | 0.7834 | 0.7826 | | 0.4935 | 3.5 | 1000 | 0.4709 | 0.7979 | 0.7972 | | 0.4825 | 4.2 | 1200 | 0.4707 | 0.7963 | 0.7950 | | 0.4655 | 4.9 | 1400 | 0.4551 | 0.8074 | 0.8064 | | 0.4545 | 5.59 | 1600 | 0.4599 | 0.8087 | 0.8084 | | 0.4506 | 6.29 | 1800 | 0.4387 | 0.8145 | 0.8135 | | 0.4324 | 6.99 | 2000 | 0.4328 | 0.8196 | 0.8185 | | 0.4262 | 7.69 | 2200 | 0.4177 | 0.8231 | 0.8224 | | 0.4265 | 8.39 | 2400 | 0.4268 | 0.8242 | 0.8233 | | 0.4156 | 9.09 | 2600 | 0.4281 | 0.8247 | 0.8235 | | 0.4113 | 9.79 | 2800 | 0.4151 | 0.8262 | 0.8253 | | 0.4078 | 10.49 | 3000 | 0.4062 | 0.8322 | 0.8314 | | 0.4013 | 11.19 | 3200 | 0.4180 | 0.8284 | 0.8273 | | 0.4036 | 11.89 | 3400 | 0.4114 | 0.8299 | 0.8290 | | 0.3911 | 12.59 | 3600 | 0.4182 | 0.8307 | 0.8299 | | 0.3886 | 13.29 | 3800 | 0.4032 | 0.8337 | 0.8327 | | 0.3908 | 13.99 | 4000 | 0.4155 | 0.8286 | 0.8275 | | 0.3805 | 14.69 | 4200 | 0.3981 | 0.8373 | 0.8365 | | 0.3858 | 15.38 | 4400 | 0.3991 | 0.8406 | 0.8398 | | 0.3773 | 16.08 | 4600 | 0.4261 | 0.8269 | 0.8260 | | 0.3766 | 16.78 | 4800 | 0.3980 | 0.8395 | 0.8384 | | 0.3702 | 17.48 | 5000 | 0.4159 | 0.8308 | 0.8297 | | 0.3686 | 18.18 | 5200 | 0.3865 | 0.8447 | 0.8439 | | 0.3686 | 18.88 | 5400 | 0.3927 | 0.8438 | 0.8428 | | 0.3676 | 19.58 | 5600 | 0.3750 | 0.8499 | 0.8492 | | 0.3677 | 20.28 | 5800 | 0.3936 | 0.8416 | 0.8406 | | 0.361 | 20.98 | 6000 | 0.3824 | 0.8480 | 0.8472 | | 0.3623 | 21.68 | 6200 | 0.3847 | 0.8465 | 0.8457 | | 0.357 | 22.38 | 6400 | 0.3775 | 0.8509 | 0.8501 | | 0.3523 | 23.08 | 6600 | 0.3842 | 0.8468 | 0.8459 | | 0.3569 | 23.78 | 6800 | 0.3878 | 0.8453 | 0.8444 | | 0.3522 | 24.48 | 7000 | 0.3946 | 0.8433 | 0.8424 | | 0.3545 | 25.17 | 7200 | 0.3804 | 0.8480 | 0.8470 | | 0.3494 | 25.87 | 7400 | 0.3975 | 0.8419 | 0.8409 | | 0.3513 | 26.57 | 7600 | 0.4007 | 0.8381 | 0.8371 | | 0.3486 | 27.27 | 7800 | 0.3907 | 0.8449 | 0.8439 | | 0.3518 | 27.97 | 8000 | 0.3822 | 0.8488 | 0.8479 | | 0.3521 | 28.67 | 8200 | 0.3912 | 0.8424 | 0.8415 | | 0.3421 | 29.37 | 8400 | 0.3821 | 0.8490 | 0.8481 | | 0.342 | 30.07 | 8600 | 0.3762 | 0.8538 | 0.8529 | | 0.343 | 30.77 | 8800 | 0.3842 | 0.8462 | 0.8452 | | 0.3424 | 31.47 | 9000 | 0.3890 | 0.8444 | 0.8435 | | 0.3462 | 32.17 | 9200 | 0.3835 | 0.8477 | 0.8468 | | 0.34 | 32.87 | 9400 | 0.3844 | 0.8461 | 0.8452 | | 0.3485 | 33.57 | 9600 | 0.3851 | 0.8451 | 0.8441 | | 0.3398 | 34.27 | 9800 | 0.3819 | 0.8485 | 0.8477 | | 0.3388 | 34.97 | 10000 | 0.3827 | 0.8479 | 0.8470 | ### 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_splice_reconstructed-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_4096_512_15M-L8_f

2024-05-03T18:59:54+00:00

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": []}

janakipanneerselvam/SegFormer_Sunlit_nvidia_mit-b5_Almonds

2024-05-03T19:00:07+00:00

{}

bobbyw/deberta-v3-xsmall_v1_no_entities

2024-05-03T19:00:29+00:00

<!-- 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_splice_reconstructed-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_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset. It achieves the following results on the evaluation set: - Loss: 0.3278 - F1 Score: 0.8773 - Accuracy: 0.8768 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.9437 | 0.7 | 200 | 0.7875 | 0.5777 | 0.6344 | | 0.5833 | 1.4 | 400 | 0.5029 | 0.7792 | 0.7784 | | 0.5022 | 2.1 | 600 | 0.4673 | 0.7985 | 0.7977 | | 0.4574 | 2.8 | 800 | 0.4595 | 0.8028 | 0.8018 | | 0.443 | 3.5 | 1000 | 0.4219 | 0.8222 | 0.8216 | | 0.4304 | 4.2 | 1200 | 0.4373 | 0.8187 | 0.8176 | | 0.4107 | 4.9 | 1400 | 0.4053 | 0.8347 | 0.8338 | | 0.3993 | 5.59 | 1600 | 0.4180 | 0.8263 | 0.8260 | | 0.3898 | 6.29 | 1800 | 0.3822 | 0.8464 | 0.8457 | | 0.3746 | 6.99 | 2000 | 0.3862 | 0.8463 | 0.8455 | | 0.3682 | 7.69 | 2200 | 0.3810 | 0.8463 | 0.8455 | | 0.3631 | 8.39 | 2400 | 0.3731 | 0.8503 | 0.8496 | | 0.3535 | 9.09 | 2600 | 0.3850 | 0.8482 | 0.8472 | | 0.349 | 9.79 | 2800 | 0.3807 | 0.8468 | 0.8459 | | 0.3446 | 10.49 | 3000 | 0.3597 | 0.8566 | 0.8560 | | 0.3349 | 11.19 | 3200 | 0.3978 | 0.8461 | 0.8450 | | 0.3372 | 11.89 | 3400 | 0.3640 | 0.8539 | 0.8531 | | 0.3255 | 12.59 | 3600 | 0.3699 | 0.8528 | 0.8520 | | 0.3194 | 13.29 | 3800 | 0.3573 | 0.8605 | 0.8597 | | 0.3222 | 13.99 | 4000 | 0.3641 | 0.8592 | 0.8584 | | 0.3105 | 14.69 | 4200 | 0.3593 | 0.8586 | 0.8577 | | 0.3154 | 15.38 | 4400 | 0.3643 | 0.8591 | 0.8584 | | 0.3092 | 16.08 | 4600 | 0.3741 | 0.8559 | 0.8551 | | 0.3048 | 16.78 | 4800 | 0.3464 | 0.8668 | 0.8661 | | 0.2974 | 17.48 | 5000 | 0.3718 | 0.8606 | 0.8597 | | 0.296 | 18.18 | 5200 | 0.3517 | 0.8654 | 0.8648 | | 0.2966 | 18.88 | 5400 | 0.3492 | 0.8677 | 0.8669 | | 0.2939 | 19.58 | 5600 | 0.3418 | 0.8728 | 0.8722 | | 0.2937 | 20.28 | 5800 | 0.3436 | 0.8730 | 0.8724 | | 0.2906 | 20.98 | 6000 | 0.3409 | 0.8705 | 0.8698 | | 0.2868 | 21.68 | 6200 | 0.3430 | 0.8715 | 0.8709 | | 0.286 | 22.38 | 6400 | 0.3433 | 0.8735 | 0.8729 | | 0.279 | 23.08 | 6600 | 0.3495 | 0.8727 | 0.8720 | | 0.2834 | 23.78 | 6800 | 0.3377 | 0.8739 | 0.8733 | | 0.2785 | 24.48 | 7000 | 0.3545 | 0.8652 | 0.8645 | | 0.2772 | 25.17 | 7200 | 0.3397 | 0.8737 | 0.8731 | | 0.2737 | 25.87 | 7400 | 0.3486 | 0.8709 | 0.8702 | | 0.2735 | 26.57 | 7600 | 0.3572 | 0.8671 | 0.8663 | | 0.2734 | 27.27 | 7800 | 0.3480 | 0.8720 | 0.8713 | | 0.2741 | 27.97 | 8000 | 0.3442 | 0.8720 | 0.8713 | | 0.2712 | 28.67 | 8200 | 0.3485 | 0.8712 | 0.8705 | | 0.2625 | 29.37 | 8400 | 0.3386 | 0.8763 | 0.8757 | | 0.2649 | 30.07 | 8600 | 0.3352 | 0.8765 | 0.8759 | | 0.2664 | 30.77 | 8800 | 0.3495 | 0.8721 | 0.8713 | | 0.2623 | 31.47 | 9000 | 0.3537 | 0.8694 | 0.8687 | | 0.2672 | 32.17 | 9200 | 0.3429 | 0.8731 | 0.8724 | | 0.2611 | 32.87 | 9400 | 0.3470 | 0.8724 | 0.8718 | | 0.2676 | 33.57 | 9600 | 0.3451 | 0.8720 | 0.8713 | | 0.26 | 34.27 | 9800 | 0.3423 | 0.8742 | 0.8735 | | 0.2596 | 34.97 | 10000 | 0.3429 | 0.8731 | 0.8724 | ### 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_splice_reconstructed-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_4096_512_15M-L32_f

2024-05-03T19:00:45+00:00

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_0-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_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.3955 - F1 Score: 0.8171 - Accuracy: 0.818 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6058 | 0.79 | 200 | 0.5094 | 0.7695 | 0.771 | | 0.5094 | 1.58 | 400 | 0.4971 | 0.7632 | 0.764 | | 0.4956 | 2.37 | 600 | 0.4886 | 0.7586 | 0.76 | | 0.485 | 3.16 | 800 | 0.4799 | 0.7769 | 0.777 | | 0.4785 | 3.95 | 1000 | 0.4762 | 0.7733 | 0.774 | | 0.4771 | 4.74 | 1200 | 0.4786 | 0.7689 | 0.769 | | 0.4732 | 5.53 | 1400 | 0.4720 | 0.7728 | 0.773 | | 0.4654 | 6.32 | 1600 | 0.4690 | 0.7730 | 0.773 | | 0.469 | 7.11 | 1800 | 0.4772 | 0.7660 | 0.766 | | 0.4692 | 7.91 | 2000 | 0.4683 | 0.7710 | 0.771 | | 0.4631 | 8.7 | 2200 | 0.4663 | 0.7738 | 0.774 | | 0.4615 | 9.49 | 2400 | 0.4637 | 0.7783 | 0.779 | | 0.4635 | 10.28 | 2600 | 0.4755 | 0.7719 | 0.772 | | 0.4589 | 11.07 | 2800 | 0.4693 | 0.7731 | 0.773 | | 0.4594 | 11.86 | 3000 | 0.4667 | 0.7801 | 0.78 | | 0.4585 | 12.65 | 3200 | 0.4688 | 0.7760 | 0.776 | | 0.4564 | 13.44 | 3400 | 0.4634 | 0.7767 | 0.777 | | 0.4584 | 14.23 | 3600 | 0.4613 | 0.7789 | 0.779 | | 0.455 | 15.02 | 3800 | 0.4658 | 0.7781 | 0.778 | | 0.4552 | 15.81 | 4000 | 0.4622 | 0.7751 | 0.775 | | 0.4538 | 16.6 | 4200 | 0.4630 | 0.776 | 0.776 | | 0.454 | 17.39 | 4400 | 0.4675 | 0.7770 | 0.777 | | 0.4536 | 18.18 | 4600 | 0.4652 | 0.7761 | 0.776 | | 0.4515 | 18.97 | 4800 | 0.4699 | 0.7749 | 0.775 | | 0.4563 | 19.76 | 5000 | 0.4599 | 0.7760 | 0.776 | | 0.4504 | 20.55 | 5200 | 0.4584 | 0.7817 | 0.782 | | 0.4508 | 21.34 | 5400 | 0.4602 | 0.7771 | 0.777 | | 0.4466 | 22.13 | 5600 | 0.4644 | 0.7761 | 0.776 | | 0.4526 | 22.92 | 5800 | 0.4616 | 0.7751 | 0.775 | | 0.447 | 23.72 | 6000 | 0.4645 | 0.7771 | 0.777 | | 0.4493 | 24.51 | 6200 | 0.4586 | 0.7759 | 0.776 | | 0.4531 | 25.3 | 6400 | 0.4613 | 0.7751 | 0.775 | | 0.446 | 26.09 | 6600 | 0.4614 | 0.7761 | 0.776 | | 0.4476 | 26.88 | 6800 | 0.4654 | 0.7790 | 0.779 | | 0.4507 | 27.67 | 7000 | 0.4589 | 0.7770 | 0.777 | | 0.4472 | 28.46 | 7200 | 0.4572 | 0.7797 | 0.78 | | 0.4487 | 29.25 | 7400 | 0.4597 | 0.7780 | 0.778 | | 0.4471 | 30.04 | 7600 | 0.4599 | 0.774 | 0.774 | | 0.4475 | 30.83 | 7800 | 0.4593 | 0.7780 | 0.778 | | 0.448 | 31.62 | 8000 | 0.4577 | 0.7739 | 0.774 | | 0.4473 | 32.41 | 8200 | 0.4599 | 0.7771 | 0.777 | | 0.4404 | 33.2 | 8400 | 0.4623 | 0.7771 | 0.777 | | 0.4506 | 33.99 | 8600 | 0.4597 | 0.7750 | 0.775 | | 0.4495 | 34.78 | 8800 | 0.4573 | 0.7788 | 0.779 | | 0.4412 | 35.57 | 9000 | 0.4611 | 0.7791 | 0.779 | | 0.4468 | 36.36 | 9200 | 0.4622 | 0.7780 | 0.778 | | 0.4493 | 37.15 | 9400 | 0.4617 | 0.7780 | 0.778 | | 0.4464 | 37.94 | 9600 | 0.4598 | 0.7781 | 0.778 | | 0.4471 | 38.74 | 9800 | 0.4606 | 0.7781 | 0.778 | | 0.4465 | 39.53 | 10000 | 0.4601 | 0.7791 | 0.779 | ### 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_tf_0-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_0-seqsight_4096_512_15M-L1_f

2024-05-03T19:00:55+00:00

{}

bobbyw/deberta-v3-large_v1_no_entities

2024-05-03T19:01:25+00:00

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_0-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_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.3820 - F1 Score: 0.8253 - Accuracy: 0.826 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.564 | 0.79 | 200 | 0.4938 | 0.7628 | 0.764 | | 0.4852 | 1.58 | 400 | 0.4781 | 0.7675 | 0.768 | | 0.4814 | 2.37 | 600 | 0.4737 | 0.7699 | 0.771 | | 0.4714 | 3.16 | 800 | 0.4691 | 0.7730 | 0.773 | | 0.4662 | 3.95 | 1000 | 0.4684 | 0.7778 | 0.779 | | 0.4652 | 4.74 | 1200 | 0.4719 | 0.7720 | 0.772 | | 0.4602 | 5.53 | 1400 | 0.4762 | 0.7750 | 0.775 | | 0.4531 | 6.32 | 1600 | 0.4656 | 0.7771 | 0.777 | | 0.4566 | 7.11 | 1800 | 0.4750 | 0.7699 | 0.77 | | 0.4543 | 7.91 | 2000 | 0.4682 | 0.7759 | 0.776 | | 0.4493 | 8.7 | 2200 | 0.4629 | 0.7758 | 0.776 | | 0.4463 | 9.49 | 2400 | 0.4588 | 0.7816 | 0.782 | | 0.4487 | 10.28 | 2600 | 0.4744 | 0.7758 | 0.776 | | 0.443 | 11.07 | 2800 | 0.4710 | 0.7716 | 0.772 | | 0.4431 | 11.86 | 3000 | 0.4668 | 0.7739 | 0.774 | | 0.44 | 12.65 | 3200 | 0.4705 | 0.7729 | 0.773 | | 0.4395 | 13.44 | 3400 | 0.4618 | 0.7819 | 0.782 | | 0.4389 | 14.23 | 3600 | 0.4595 | 0.7800 | 0.78 | | 0.4367 | 15.02 | 3800 | 0.4655 | 0.7810 | 0.781 | | 0.4365 | 15.81 | 4000 | 0.4595 | 0.7760 | 0.776 | | 0.4339 | 16.6 | 4200 | 0.4603 | 0.7829 | 0.783 | | 0.4332 | 17.39 | 4400 | 0.4745 | 0.7678 | 0.768 | | 0.4336 | 18.18 | 4600 | 0.4630 | 0.7731 | 0.773 | | 0.4308 | 18.97 | 4800 | 0.4697 | 0.7710 | 0.771 | | 0.4343 | 19.76 | 5000 | 0.4621 | 0.7751 | 0.775 | | 0.4306 | 20.55 | 5200 | 0.4580 | 0.7829 | 0.783 | | 0.4279 | 21.34 | 5400 | 0.4651 | 0.7760 | 0.776 | | 0.4256 | 22.13 | 5600 | 0.4676 | 0.7791 | 0.779 | | 0.4295 | 22.92 | 5800 | 0.4676 | 0.7681 | 0.768 | | 0.4238 | 23.72 | 6000 | 0.4683 | 0.7741 | 0.774 | | 0.426 | 24.51 | 6200 | 0.4588 | 0.7770 | 0.777 | | 0.4284 | 25.3 | 6400 | 0.4627 | 0.7761 | 0.776 | | 0.4231 | 26.09 | 6600 | 0.4634 | 0.7810 | 0.781 | | 0.4252 | 26.88 | 6800 | 0.4691 | 0.7720 | 0.772 | | 0.4266 | 27.67 | 7000 | 0.4623 | 0.7790 | 0.779 | | 0.4242 | 28.46 | 7200 | 0.4589 | 0.7808 | 0.781 | | 0.424 | 29.25 | 7400 | 0.4646 | 0.7751 | 0.775 | | 0.4238 | 30.04 | 7600 | 0.4670 | 0.7741 | 0.774 | | 0.4219 | 30.83 | 7800 | 0.4646 | 0.7760 | 0.776 | | 0.4235 | 31.62 | 8000 | 0.4620 | 0.7829 | 0.783 | | 0.4231 | 32.41 | 8200 | 0.4656 | 0.7741 | 0.774 | | 0.4158 | 33.2 | 8400 | 0.4681 | 0.7731 | 0.773 | | 0.4244 | 33.99 | 8600 | 0.4629 | 0.7780 | 0.778 | | 0.4233 | 34.78 | 8800 | 0.4610 | 0.7770 | 0.777 | | 0.4153 | 35.57 | 9000 | 0.4647 | 0.7771 | 0.777 | | 0.4203 | 36.36 | 9200 | 0.4694 | 0.7730 | 0.773 | | 0.4236 | 37.15 | 9400 | 0.4657 | 0.7790 | 0.779 | | 0.4194 | 37.94 | 9600 | 0.4650 | 0.7731 | 0.773 | | 0.4205 | 38.74 | 9800 | 0.4657 | 0.7731 | 0.773 | | 0.4202 | 39.53 | 10000 | 0.4650 | 0.7741 | 0.774 | ### 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_tf_0-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_0-seqsight_4096_512_15M-L8_f

2024-05-03T19:01:36+00:00

{}

bobbyw/deberta-v3-large_v1_no_entities_with_context

2024-05-03T19:02:31+00:00

text-generation

transformers

# mlx-community/codegemma-1.1-7b-it-4bit This model was converted to MLX format from [`google/codegemma-1.1-7b-it`]() using mlx-lm version **0.12.0**. Refer to the [original model card](https://huggingface.co/google/codegemma-1.1-7b-it) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/codegemma-1.1-7b-it-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

{"license": "gemma", "library_name": "transformers", "tags": ["mlx"], "extra_gated_heading": "Access CodeGemma on Hugging Face", "extra_gated_prompt": "To access CodeGemma on Hugging Face, you\u2019re required to review and agree to Google\u2019s usage license. To do this, please ensure you\u2019re logged-in to Hugging Face and click below. Requests are processed immediately.", "extra_gated_button_content": "Acknowledge license", "pipeline_tag": "text-generation", "widget": [{"text": "<start_of_turn>user Write a Python function to calculate the nth fibonacci number.<end_of_turn> <start_of_turn>model\n"}], "inference": {"parameters": {"max_new_tokens": 200}}, "license_link": "https://ai.google.dev/gemma/terms"}

mlx-community/codegemma-1.1-7b-it-4bit

2024-05-03T19:02:41+00:00

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_0-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_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.3822 - F1 Score: 0.8231 - Accuracy: 0.824 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5442 | 0.79 | 200 | 0.4868 | 0.7689 | 0.77 | | 0.4786 | 1.58 | 400 | 0.4719 | 0.7698 | 0.77 | | 0.474 | 2.37 | 600 | 0.4703 | 0.7744 | 0.775 | | 0.462 | 3.16 | 800 | 0.4675 | 0.7790 | 0.779 | | 0.4553 | 3.95 | 1000 | 0.4637 | 0.7710 | 0.772 | | 0.4525 | 4.74 | 1200 | 0.4660 | 0.7771 | 0.777 | | 0.4466 | 5.53 | 1400 | 0.4745 | 0.7746 | 0.775 | | 0.4392 | 6.32 | 1600 | 0.4622 | 0.7780 | 0.778 | | 0.443 | 7.11 | 1800 | 0.4753 | 0.7676 | 0.768 | | 0.4393 | 7.91 | 2000 | 0.4669 | 0.7759 | 0.776 | | 0.4334 | 8.7 | 2200 | 0.4666 | 0.7811 | 0.781 | | 0.431 | 9.49 | 2400 | 0.4610 | 0.7738 | 0.774 | | 0.4338 | 10.28 | 2600 | 0.4822 | 0.7694 | 0.77 | | 0.4258 | 11.07 | 2800 | 0.4797 | 0.7674 | 0.768 | | 0.4259 | 11.86 | 3000 | 0.4702 | 0.7729 | 0.773 | | 0.4227 | 12.65 | 3200 | 0.4685 | 0.7850 | 0.785 | | 0.4211 | 13.44 | 3400 | 0.4679 | 0.7760 | 0.776 | | 0.4194 | 14.23 | 3600 | 0.4713 | 0.7650 | 0.765 | | 0.4184 | 15.02 | 3800 | 0.4779 | 0.7768 | 0.777 | | 0.4151 | 15.81 | 4000 | 0.4566 | 0.7780 | 0.778 | | 0.4124 | 16.6 | 4200 | 0.4664 | 0.7790 | 0.779 | | 0.4129 | 17.39 | 4400 | 0.4694 | 0.7828 | 0.783 | | 0.4114 | 18.18 | 4600 | 0.4705 | 0.7790 | 0.779 | | 0.4084 | 18.97 | 4800 | 0.4758 | 0.7790 | 0.779 | | 0.41 | 19.76 | 5000 | 0.4642 | 0.7761 | 0.776 | | 0.4056 | 20.55 | 5200 | 0.4634 | 0.7860 | 0.786 | | 0.4043 | 21.34 | 5400 | 0.4719 | 0.7770 | 0.777 | | 0.4016 | 22.13 | 5600 | 0.4722 | 0.7861 | 0.786 | | 0.4019 | 22.92 | 5800 | 0.4778 | 0.7780 | 0.778 | | 0.3985 | 23.72 | 6000 | 0.4769 | 0.7809 | 0.781 | | 0.3981 | 24.51 | 6200 | 0.4672 | 0.7841 | 0.784 | | 0.4013 | 25.3 | 6400 | 0.4766 | 0.7770 | 0.777 | | 0.3957 | 26.09 | 6600 | 0.4738 | 0.7720 | 0.772 | | 0.3963 | 26.88 | 6800 | 0.4767 | 0.7771 | 0.777 | | 0.3971 | 27.67 | 7000 | 0.4753 | 0.7811 | 0.781 | | 0.3938 | 28.46 | 7200 | 0.4698 | 0.7810 | 0.781 | | 0.3937 | 29.25 | 7400 | 0.4782 | 0.7870 | 0.787 | | 0.3945 | 30.04 | 7600 | 0.4739 | 0.782 | 0.782 | | 0.3906 | 30.83 | 7800 | 0.4763 | 0.7761 | 0.776 | | 0.3911 | 31.62 | 8000 | 0.4681 | 0.7881 | 0.788 | | 0.3912 | 32.41 | 8200 | 0.4775 | 0.7800 | 0.78 | | 0.3821 | 33.2 | 8400 | 0.4849 | 0.7760 | 0.776 | | 0.3923 | 33.99 | 8600 | 0.4778 | 0.7820 | 0.782 | | 0.3908 | 34.78 | 8800 | 0.4751 | 0.7831 | 0.783 | | 0.383 | 35.57 | 9000 | 0.4811 | 0.7790 | 0.779 | | 0.3862 | 36.36 | 9200 | 0.4889 | 0.7779 | 0.778 | | 0.3904 | 37.15 | 9400 | 0.4837 | 0.7769 | 0.777 | | 0.3859 | 37.94 | 9600 | 0.4804 | 0.7780 | 0.778 | | 0.3847 | 38.74 | 9800 | 0.4822 | 0.7810 | 0.781 | | 0.3863 | 39.53 | 10000 | 0.4812 | 0.7810 | 0.781 | ### 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_tf_0-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_0-seqsight_4096_512_15M-L32_f

2024-05-03T19:03:05+00:00

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_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_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset. It achieves the following results on the evaluation set: - Loss: 0.3572 - F1 Score: 0.8444 - Accuracy: 0.845 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6122 | 0.83 | 200 | 0.5520 | 0.7117 | 0.713 | | 0.5255 | 1.67 | 400 | 0.5377 | 0.7199 | 0.72 | | 0.5074 | 2.5 | 600 | 0.5309 | 0.7250 | 0.725 | | 0.4984 | 3.33 | 800 | 0.5262 | 0.7406 | 0.741 | | 0.4977 | 4.17 | 1000 | 0.5190 | 0.7370 | 0.737 | | 0.4934 | 5.0 | 1200 | 0.5188 | 0.7430 | 0.743 | | 0.4875 | 5.83 | 1400 | 0.5129 | 0.7470 | 0.747 | | 0.4909 | 6.67 | 1600 | 0.5163 | 0.7456 | 0.746 | | 0.4879 | 7.5 | 1800 | 0.5117 | 0.7440 | 0.744 | | 0.4839 | 8.33 | 2000 | 0.5103 | 0.7409 | 0.741 | | 0.483 | 9.17 | 2200 | 0.5086 | 0.7478 | 0.748 | | 0.4839 | 10.0 | 2400 | 0.5117 | 0.7427 | 0.743 | | 0.483 | 10.83 | 2600 | 0.5129 | 0.7423 | 0.743 | | 0.4826 | 11.67 | 2800 | 0.5140 | 0.7481 | 0.749 | | 0.4732 | 12.5 | 3000 | 0.5130 | 0.7490 | 0.75 | | 0.487 | 13.33 | 3200 | 0.5049 | 0.7467 | 0.747 | | 0.4763 | 14.17 | 3400 | 0.5032 | 0.75 | 0.75 | | 0.4759 | 15.0 | 3600 | 0.5158 | 0.7463 | 0.748 | | 0.4765 | 15.83 | 3800 | 0.5033 | 0.7539 | 0.754 | | 0.4795 | 16.67 | 4000 | 0.5095 | 0.7493 | 0.75 | | 0.4729 | 17.5 | 4200 | 0.5042 | 0.7514 | 0.752 | | 0.472 | 18.33 | 4400 | 0.5125 | 0.7456 | 0.746 | | 0.4789 | 19.17 | 4600 | 0.5084 | 0.7490 | 0.75 | | 0.4773 | 20.0 | 4800 | 0.5030 | 0.7508 | 0.751 | | 0.4735 | 20.83 | 5000 | 0.5052 | 0.7548 | 0.755 | | 0.4731 | 21.67 | 5200 | 0.5012 | 0.7499 | 0.75 | | 0.4711 | 22.5 | 5400 | 0.5037 | 0.7479 | 0.748 | | 0.4721 | 23.33 | 5600 | 0.5040 | 0.7514 | 0.752 | | 0.4697 | 24.17 | 5800 | 0.5083 | 0.7523 | 0.753 | | 0.4732 | 25.0 | 6000 | 0.5022 | 0.7509 | 0.751 | | 0.4703 | 25.83 | 6200 | 0.5019 | 0.7506 | 0.751 | | 0.4715 | 26.67 | 6400 | 0.5005 | 0.7480 | 0.748 | | 0.4741 | 27.5 | 6600 | 0.5019 | 0.7518 | 0.752 | | 0.4664 | 28.33 | 6800 | 0.5025 | 0.7537 | 0.754 | | 0.4697 | 29.17 | 7000 | 0.5011 | 0.7547 | 0.755 | | 0.47 | 30.0 | 7200 | 0.5004 | 0.7537 | 0.754 | | 0.4698 | 30.83 | 7400 | 0.5049 | 0.7468 | 0.748 | | 0.4684 | 31.67 | 7600 | 0.4991 | 0.7547 | 0.755 | | 0.4707 | 32.5 | 7800 | 0.4990 | 0.7537 | 0.754 | | 0.4647 | 33.33 | 8000 | 0.5003 | 0.7527 | 0.753 | | 0.4716 | 34.17 | 8200 | 0.4997 | 0.7547 | 0.755 | | 0.4642 | 35.0 | 8400 | 0.4997 | 0.7509 | 0.751 | | 0.4682 | 35.83 | 8600 | 0.4997 | 0.7557 | 0.756 | | 0.4682 | 36.67 | 8800 | 0.4991 | 0.7577 | 0.758 | | 0.4641 | 37.5 | 9000 | 0.5043 | 0.7472 | 0.748 | | 0.4681 | 38.33 | 9200 | 0.5000 | 0.7508 | 0.751 | | 0.4682 | 39.17 | 9400 | 0.5003 | 0.7527 | 0.753 | | 0.4676 | 40.0 | 9600 | 0.5008 | 0.7545 | 0.755 | | 0.467 | 40.83 | 9800 | 0.4999 | 0.7508 | 0.751 | | 0.4663 | 41.67 | 10000 | 0.5000 | 0.7517 | 0.752 | ### 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_tf_1-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_1-seqsight_4096_512_15M-L1_f

2024-05-03T19:03:06+00:00

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_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_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset. It achieves the following results on the evaluation set: - Loss: 0.3564 - F1 Score: 0.8497 - Accuracy: 0.85 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5795 | 0.83 | 200 | 0.5363 | 0.7282 | 0.729 | | 0.5027 | 1.67 | 400 | 0.5199 | 0.7328 | 0.733 | | 0.4941 | 2.5 | 600 | 0.5148 | 0.7388 | 0.739 | | 0.4863 | 3.33 | 800 | 0.5092 | 0.7449 | 0.745 | | 0.4866 | 4.17 | 1000 | 0.5069 | 0.7498 | 0.75 | | 0.4815 | 5.0 | 1200 | 0.5068 | 0.7549 | 0.755 | | 0.4743 | 5.83 | 1400 | 0.5016 | 0.7547 | 0.755 | | 0.4779 | 6.67 | 1600 | 0.5069 | 0.7473 | 0.748 | | 0.4747 | 7.5 | 1800 | 0.5009 | 0.7590 | 0.759 | | 0.4702 | 8.33 | 2000 | 0.4978 | 0.7560 | 0.756 | | 0.4687 | 9.17 | 2200 | 0.4952 | 0.7559 | 0.756 | | 0.4681 | 10.0 | 2400 | 0.5077 | 0.7407 | 0.742 | | 0.4676 | 10.83 | 2600 | 0.5064 | 0.7450 | 0.747 | | 0.4655 | 11.67 | 2800 | 0.5004 | 0.7529 | 0.754 | | 0.4556 | 12.5 | 3000 | 0.5048 | 0.7503 | 0.752 | | 0.4698 | 13.33 | 3200 | 0.4985 | 0.7533 | 0.754 | | 0.4578 | 14.17 | 3400 | 0.4922 | 0.7499 | 0.75 | | 0.4565 | 15.0 | 3600 | 0.5020 | 0.7496 | 0.751 | | 0.455 | 15.83 | 3800 | 0.4908 | 0.7665 | 0.767 | | 0.4587 | 16.67 | 4000 | 0.4998 | 0.7512 | 0.752 | | 0.4521 | 17.5 | 4200 | 0.4939 | 0.7563 | 0.757 | | 0.4506 | 18.33 | 4400 | 0.4958 | 0.7626 | 0.763 | | 0.4572 | 19.17 | 4600 | 0.4979 | 0.7538 | 0.755 | | 0.4545 | 20.0 | 4800 | 0.4925 | 0.7598 | 0.76 | | 0.4517 | 20.83 | 5000 | 0.4961 | 0.7578 | 0.758 | | 0.449 | 21.67 | 5200 | 0.4891 | 0.7588 | 0.759 | | 0.4475 | 22.5 | 5400 | 0.4911 | 0.7658 | 0.766 | | 0.4504 | 23.33 | 5600 | 0.4953 | 0.7520 | 0.753 | | 0.4481 | 24.17 | 5800 | 0.4969 | 0.7571 | 0.758 | | 0.4497 | 25.0 | 6000 | 0.4905 | 0.7618 | 0.762 | | 0.4456 | 25.83 | 6200 | 0.4922 | 0.7574 | 0.758 | | 0.447 | 26.67 | 6400 | 0.4876 | 0.7590 | 0.759 | | 0.4495 | 27.5 | 6600 | 0.4930 | 0.7579 | 0.758 | | 0.4409 | 28.33 | 6800 | 0.4908 | 0.7520 | 0.752 | | 0.4444 | 29.17 | 7000 | 0.4898 | 0.7568 | 0.757 | | 0.4448 | 30.0 | 7200 | 0.4911 | 0.7507 | 0.751 | | 0.4457 | 30.83 | 7400 | 0.4959 | 0.7586 | 0.76 | | 0.4421 | 31.67 | 7600 | 0.4910 | 0.7578 | 0.758 | | 0.4467 | 32.5 | 7800 | 0.4899 | 0.7528 | 0.753 | | 0.4392 | 33.33 | 8000 | 0.4893 | 0.7559 | 0.756 | | 0.4449 | 34.17 | 8200 | 0.4920 | 0.7607 | 0.761 | | 0.4386 | 35.0 | 8400 | 0.4902 | 0.7549 | 0.755 | | 0.4414 | 35.83 | 8600 | 0.4890 | 0.7588 | 0.759 | | 0.4424 | 36.67 | 8800 | 0.4900 | 0.7587 | 0.759 | | 0.4385 | 37.5 | 9000 | 0.4957 | 0.7608 | 0.762 | | 0.4414 | 38.33 | 9200 | 0.4900 | 0.7528 | 0.753 | | 0.4421 | 39.17 | 9400 | 0.4894 | 0.7587 | 0.759 | | 0.4408 | 40.0 | 9600 | 0.4911 | 0.7585 | 0.759 | | 0.4398 | 40.83 | 9800 | 0.4891 | 0.7569 | 0.757 | | 0.4397 | 41.67 | 10000 | 0.4895 | 0.7578 | 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_4096_512_15M", "model-index": [{"name": "GUE_tf_1-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_1-seqsight_4096_512_15M-L8_f

2024-05-03T19:03:42+00:00

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": []}

ShakhzoDavronov/wav2vec2-base-asr-uz

2024-05-03T19:03:53+00:00

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_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_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset. It achieves the following results on the evaluation set: - Loss: 0.3672 - F1 Score: 0.8397 - Accuracy: 0.84 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5598 | 0.83 | 200 | 0.5336 | 0.7225 | 0.725 | | 0.497 | 1.67 | 400 | 0.5164 | 0.7355 | 0.736 | | 0.488 | 2.5 | 600 | 0.5110 | 0.7427 | 0.743 | | 0.4796 | 3.33 | 800 | 0.5018 | 0.7480 | 0.748 | | 0.4781 | 4.17 | 1000 | 0.4984 | 0.7543 | 0.755 | | 0.4707 | 5.0 | 1200 | 0.4983 | 0.7524 | 0.753 | | 0.4633 | 5.83 | 1400 | 0.4924 | 0.7587 | 0.759 | | 0.4651 | 6.67 | 1600 | 0.4974 | 0.7575 | 0.758 | | 0.461 | 7.5 | 1800 | 0.4896 | 0.7530 | 0.753 | | 0.4546 | 8.33 | 2000 | 0.4871 | 0.7520 | 0.752 | | 0.4557 | 9.17 | 2200 | 0.4878 | 0.7580 | 0.758 | | 0.4528 | 10.0 | 2400 | 0.5009 | 0.7536 | 0.755 | | 0.4518 | 10.83 | 2600 | 0.4983 | 0.7643 | 0.766 | | 0.448 | 11.67 | 2800 | 0.4872 | 0.7632 | 0.764 | | 0.4383 | 12.5 | 3000 | 0.4980 | 0.7536 | 0.756 | | 0.45 | 13.33 | 3200 | 0.4950 | 0.7545 | 0.755 | | 0.4399 | 14.17 | 3400 | 0.4871 | 0.7568 | 0.757 | | 0.4376 | 15.0 | 3600 | 0.4921 | 0.7626 | 0.764 | | 0.4335 | 15.83 | 3800 | 0.4854 | 0.7635 | 0.764 | | 0.4385 | 16.67 | 4000 | 0.4883 | 0.7687 | 0.769 | | 0.4312 | 17.5 | 4200 | 0.4865 | 0.7705 | 0.771 | | 0.4283 | 18.33 | 4400 | 0.4901 | 0.7687 | 0.769 | | 0.4335 | 19.17 | 4600 | 0.4903 | 0.7618 | 0.763 | | 0.4289 | 20.0 | 4800 | 0.4864 | 0.7658 | 0.766 | | 0.4278 | 20.83 | 5000 | 0.4913 | 0.7576 | 0.758 | | 0.4233 | 21.67 | 5200 | 0.4827 | 0.7589 | 0.759 | | 0.4206 | 22.5 | 5400 | 0.4899 | 0.7586 | 0.759 | | 0.423 | 23.33 | 5600 | 0.4898 | 0.7641 | 0.765 | | 0.4215 | 24.17 | 5800 | 0.4892 | 0.7665 | 0.767 | | 0.4221 | 25.0 | 6000 | 0.4869 | 0.7660 | 0.766 | | 0.4154 | 25.83 | 6200 | 0.4917 | 0.7571 | 0.758 | | 0.4184 | 26.67 | 6400 | 0.4817 | 0.7679 | 0.768 | | 0.4207 | 27.5 | 6600 | 0.4918 | 0.7620 | 0.762 | | 0.4123 | 28.33 | 6800 | 0.4898 | 0.7630 | 0.763 | | 0.4144 | 29.17 | 7000 | 0.4853 | 0.7689 | 0.769 | | 0.4137 | 30.0 | 7200 | 0.4866 | 0.7579 | 0.758 | | 0.4125 | 30.83 | 7400 | 0.4911 | 0.7602 | 0.761 | | 0.41 | 31.67 | 7600 | 0.4887 | 0.7699 | 0.77 | | 0.4125 | 32.5 | 7800 | 0.4877 | 0.7599 | 0.76 | | 0.4056 | 33.33 | 8000 | 0.4875 | 0.7638 | 0.764 | | 0.4108 | 34.17 | 8200 | 0.4858 | 0.7658 | 0.766 | | 0.4069 | 35.0 | 8400 | 0.4888 | 0.7670 | 0.767 | | 0.4083 | 35.83 | 8600 | 0.4846 | 0.7739 | 0.774 | | 0.4073 | 36.67 | 8800 | 0.4859 | 0.7697 | 0.77 | | 0.4035 | 37.5 | 9000 | 0.4917 | 0.7625 | 0.763 | | 0.4053 | 38.33 | 9200 | 0.4890 | 0.7679 | 0.768 | | 0.4067 | 39.17 | 9400 | 0.4876 | 0.7699 | 0.77 | | 0.4053 | 40.0 | 9600 | 0.4891 | 0.7657 | 0.766 | | 0.4026 | 40.83 | 9800 | 0.4878 | 0.7679 | 0.768 | | 0.4014 | 41.67 | 10000 | 0.4883 | 0.7678 | 0.768 | ### 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_tf_1-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_1-seqsight_4096_512_15M-L32_f

2024-05-03T19:04:23+00:00

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_0.01_8_0.0002

2024-05-03T19:04:42+00:00

{"license": "mit"}

AntNLP/TinyLlama-NoPE-1.1B

2024-05-03T19:04:59+00:00

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. --> # donut_synDB_base This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2303 ## 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: 6e-05 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 24 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9617 | 0.45 | 24 | 1.3561 | | 1.1972 | 0.68 | 36 | 0.6411 | | 0.7063 | 0.91 | 48 | 0.4467 | | 0.5082 | 1.13 | 60 | 0.3735 | | 0.3655 | 1.36 | 72 | 0.3117 | | 0.2861 | 1.58 | 84 | 0.2874 | | 0.274 | 1.81 | 96 | 0.2492 | | 0.2308 | 2.04 | 108 | 0.2510 | | 0.1745 | 2.26 | 120 | 0.2350 | | 0.1594 | 2.49 | 132 | 0.2334 | | 0.1471 | 2.72 | 144 | 0.2414 | | 0.1777 | 2.94 | 156 | 0.2159 | | 0.1401 | 3.17 | 168 | 0.2359 | | 0.1008 | 3.4 | 180 | 0.2389 | | 0.1038 | 3.62 | 192 | 0.2346 | | 0.1251 | 3.85 | 204 | 0.2303 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut_synDB_base", "results": []}]}

Donut01/donut_synDB_base

2024-05-03T19:05:18+00:00

{}

varox34/med-llava-llama

2024-05-03T19:06:14+00:00

{}

magicllava/vit_layout

2024-05-03T19:06:25+00:00

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_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_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3786 - F1 Score: 0.8268 - Accuracy: 0.828 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6103 | 1.34 | 200 | 0.5528 | 0.7220 | 0.722 | | 0.5121 | 2.68 | 400 | 0.5260 | 0.7171 | 0.719 | | 0.4937 | 4.03 | 600 | 0.5191 | 0.7198 | 0.722 | | 0.4848 | 5.37 | 800 | 0.5120 | 0.7330 | 0.733 | | 0.4759 | 6.71 | 1000 | 0.5128 | 0.7358 | 0.737 | | 0.4736 | 8.05 | 1200 | 0.5054 | 0.7410 | 0.741 | | 0.4722 | 9.4 | 1400 | 0.5011 | 0.7393 | 0.74 | | 0.468 | 10.74 | 1600 | 0.5104 | 0.7402 | 0.743 | | 0.4641 | 12.08 | 1800 | 0.5051 | 0.7509 | 0.752 | | 0.4627 | 13.42 | 2000 | 0.4937 | 0.7517 | 0.752 | | 0.4553 | 14.77 | 2200 | 0.4941 | 0.7610 | 0.761 | | 0.4575 | 16.11 | 2400 | 0.4892 | 0.7505 | 0.751 | | 0.4516 | 17.45 | 2600 | 0.4884 | 0.7609 | 0.761 | | 0.45 | 18.79 | 2800 | 0.4898 | 0.7610 | 0.761 | | 0.4505 | 20.13 | 3000 | 0.4834 | 0.7610 | 0.761 | | 0.4453 | 21.48 | 3200 | 0.4831 | 0.7608 | 0.761 | | 0.4413 | 22.82 | 3400 | 0.4865 | 0.7655 | 0.766 | | 0.4411 | 24.16 | 3600 | 0.4814 | 0.7609 | 0.761 | | 0.442 | 25.5 | 3800 | 0.4816 | 0.7613 | 0.762 | | 0.4367 | 26.85 | 4000 | 0.4785 | 0.7627 | 0.763 | | 0.438 | 28.19 | 4200 | 0.4775 | 0.7619 | 0.762 | | 0.4334 | 29.53 | 4400 | 0.4820 | 0.7619 | 0.762 | | 0.4363 | 30.87 | 4600 | 0.4756 | 0.7588 | 0.759 | | 0.4359 | 32.21 | 4800 | 0.4749 | 0.7685 | 0.769 | | 0.4317 | 33.56 | 5000 | 0.4782 | 0.7655 | 0.766 | | 0.4349 | 34.9 | 5200 | 0.4752 | 0.7629 | 0.763 | | 0.4349 | 36.24 | 5400 | 0.4750 | 0.7648 | 0.765 | | 0.4273 | 37.58 | 5600 | 0.4740 | 0.7697 | 0.77 | | 0.4297 | 38.93 | 5800 | 0.4709 | 0.7610 | 0.761 | | 0.4299 | 40.27 | 6000 | 0.4738 | 0.7678 | 0.768 | | 0.4262 | 41.61 | 6200 | 0.4744 | 0.7746 | 0.775 | | 0.4297 | 42.95 | 6400 | 0.4697 | 0.7679 | 0.768 | | 0.4307 | 44.3 | 6600 | 0.4700 | 0.7737 | 0.774 | | 0.4229 | 45.64 | 6800 | 0.4703 | 0.7649 | 0.765 | | 0.4306 | 46.98 | 7000 | 0.4694 | 0.7659 | 0.766 | | 0.4281 | 48.32 | 7200 | 0.4675 | 0.7698 | 0.77 | | 0.4223 | 49.66 | 7400 | 0.4683 | 0.7640 | 0.764 | | 0.4251 | 51.01 | 7600 | 0.4663 | 0.7728 | 0.773 | | 0.4226 | 52.35 | 7800 | 0.4696 | 0.7737 | 0.774 | | 0.4212 | 53.69 | 8000 | 0.4690 | 0.7669 | 0.767 | | 0.425 | 55.03 | 8200 | 0.4684 | 0.7650 | 0.765 | | 0.4201 | 56.38 | 8400 | 0.4693 | 0.7688 | 0.769 | | 0.4243 | 57.72 | 8600 | 0.4693 | 0.7775 | 0.778 | | 0.4226 | 59.06 | 8800 | 0.4673 | 0.7699 | 0.77 | | 0.4219 | 60.4 | 9000 | 0.4665 | 0.7688 | 0.769 | | 0.4213 | 61.74 | 9200 | 0.4676 | 0.7727 | 0.773 | | 0.4234 | 63.09 | 9400 | 0.4664 | 0.7708 | 0.771 | | 0.4237 | 64.43 | 9600 | 0.4666 | 0.7727 | 0.773 | | 0.4181 | 65.77 | 9800 | 0.4672 | 0.7727 | 0.773 | | 0.4216 | 67.11 | 10000 | 0.4668 | 0.7708 | 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_4096_512_15M", "model-index": [{"name": "GUE_tf_4-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_4096_512_15M-L1_f

2024-05-03T19:07:37+00:00

text-classification

transformers

{}

bobbyw/deberta-v3-small_v1_no_entities_with_context

2024-05-03T19:07:59+00:00

{}

serbest/DeeplabV3-small-b0-finetuned-segments-sidewalk-2

2024-05-03T19:07:59+00:00

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_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_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3453 - F1 Score: 0.8437 - Accuracy: 0.844 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5764 | 1.34 | 200 | 0.5276 | 0.7290 | 0.729 | | 0.4864 | 2.68 | 400 | 0.5099 | 0.7339 | 0.735 | | 0.4755 | 4.03 | 600 | 0.5020 | 0.7475 | 0.749 | | 0.4666 | 5.37 | 800 | 0.5031 | 0.7510 | 0.751 | | 0.4555 | 6.71 | 1000 | 0.4985 | 0.7576 | 0.759 | | 0.4472 | 8.05 | 1200 | 0.4870 | 0.7650 | 0.765 | | 0.4434 | 9.4 | 1400 | 0.4770 | 0.7660 | 0.766 | | 0.4363 | 10.74 | 1600 | 0.4825 | 0.7683 | 0.769 | | 0.4306 | 12.08 | 1800 | 0.4921 | 0.7653 | 0.767 | | 0.4298 | 13.42 | 2000 | 0.4703 | 0.7706 | 0.771 | | 0.4204 | 14.77 | 2200 | 0.4711 | 0.7780 | 0.778 | | 0.4214 | 16.11 | 2400 | 0.4657 | 0.7794 | 0.78 | | 0.4143 | 17.45 | 2600 | 0.4682 | 0.7690 | 0.769 | | 0.4146 | 18.79 | 2800 | 0.4683 | 0.7789 | 0.779 | | 0.4115 | 20.13 | 3000 | 0.4607 | 0.7830 | 0.783 | | 0.4067 | 21.48 | 3200 | 0.4599 | 0.7754 | 0.776 | | 0.4058 | 22.82 | 3400 | 0.4602 | 0.7845 | 0.785 | | 0.4037 | 24.16 | 3600 | 0.4567 | 0.7869 | 0.787 | | 0.4037 | 25.5 | 3800 | 0.4618 | 0.7751 | 0.777 | | 0.3983 | 26.85 | 4000 | 0.4500 | 0.7869 | 0.787 | | 0.3997 | 28.19 | 4200 | 0.4478 | 0.7908 | 0.791 | | 0.3934 | 29.53 | 4400 | 0.4553 | 0.7870 | 0.787 | | 0.3952 | 30.87 | 4600 | 0.4489 | 0.7950 | 0.795 | | 0.3942 | 32.21 | 4800 | 0.4456 | 0.7858 | 0.786 | | 0.3902 | 33.56 | 5000 | 0.4470 | 0.7899 | 0.79 | | 0.3929 | 34.9 | 5200 | 0.4444 | 0.7869 | 0.787 | | 0.3912 | 36.24 | 5400 | 0.4407 | 0.7938 | 0.794 | | 0.385 | 37.58 | 5600 | 0.4421 | 0.7956 | 0.796 | | 0.3848 | 38.93 | 5800 | 0.4436 | 0.7910 | 0.791 | | 0.385 | 40.27 | 6000 | 0.4459 | 0.7946 | 0.795 | | 0.3824 | 41.61 | 6200 | 0.4448 | 0.7919 | 0.792 | | 0.3834 | 42.95 | 6400 | 0.4399 | 0.7979 | 0.798 | | 0.3866 | 44.3 | 6600 | 0.4380 | 0.7955 | 0.796 | | 0.3756 | 45.64 | 6800 | 0.4389 | 0.796 | 0.796 | | 0.383 | 46.98 | 7000 | 0.4373 | 0.7950 | 0.795 | | 0.3819 | 48.32 | 7200 | 0.4338 | 0.7978 | 0.798 | | 0.3741 | 49.66 | 7400 | 0.4343 | 0.7980 | 0.798 | | 0.3766 | 51.01 | 7600 | 0.4344 | 0.7979 | 0.798 | | 0.3767 | 52.35 | 7800 | 0.4364 | 0.7966 | 0.797 | | 0.371 | 53.69 | 8000 | 0.4383 | 0.8040 | 0.804 | | 0.3771 | 55.03 | 8200 | 0.4385 | 0.7970 | 0.797 | | 0.3722 | 56.38 | 8400 | 0.4358 | 0.7999 | 0.8 | | 0.3745 | 57.72 | 8600 | 0.4348 | 0.8036 | 0.804 | | 0.3711 | 59.06 | 8800 | 0.4338 | 0.7979 | 0.798 | | 0.3688 | 60.4 | 9000 | 0.4342 | 0.8019 | 0.802 | | 0.3685 | 61.74 | 9200 | 0.4342 | 0.8037 | 0.804 | | 0.3723 | 63.09 | 9400 | 0.4327 | 0.8027 | 0.803 | | 0.3719 | 64.43 | 9600 | 0.4326 | 0.8037 | 0.804 | | 0.3661 | 65.77 | 9800 | 0.4331 | 0.8018 | 0.802 | | 0.3721 | 67.11 | 10000 | 0.4325 | 0.8018 | 0.802 | ### 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_tf_4-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_4096_512_15M-L8_f

2024-05-03T19:08:28+00:00

# 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": []}

Nitish/vit-base-patch16-224-in21k-lora-fine-tuned-chest-xray

2024-05-03T19:08:59+00:00

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) sqlcoder-7b-2 - bnb 4bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation.  ## 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:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] ### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)

{}

RichardErkhov/defog_-_sqlcoder-7b-2-4bits

2024-05-03T19:09:04+00:00

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_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_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3704 - F1 Score: 0.8588 - Accuracy: 0.859 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5525 | 1.34 | 200 | 0.5126 | 0.7380 | 0.738 | | 0.4762 | 2.68 | 400 | 0.4937 | 0.7496 | 0.75 | | 0.4601 | 4.03 | 600 | 0.4891 | 0.7548 | 0.756 | | 0.4466 | 5.37 | 800 | 0.4828 | 0.7580 | 0.758 | | 0.4325 | 6.71 | 1000 | 0.4821 | 0.7670 | 0.769 | | 0.4221 | 8.05 | 1200 | 0.4624 | 0.7769 | 0.777 | | 0.416 | 9.4 | 1400 | 0.4501 | 0.7809 | 0.781 | | 0.4062 | 10.74 | 1600 | 0.4531 | 0.7800 | 0.78 | | 0.3994 | 12.08 | 1800 | 0.4526 | 0.7831 | 0.784 | | 0.3951 | 13.42 | 2000 | 0.4485 | 0.7939 | 0.794 | | 0.3826 | 14.77 | 2200 | 0.4444 | 0.7958 | 0.796 | | 0.3825 | 16.11 | 2400 | 0.4407 | 0.7955 | 0.796 | | 0.3734 | 17.45 | 2600 | 0.4475 | 0.7848 | 0.785 | | 0.367 | 18.79 | 2800 | 0.4480 | 0.7940 | 0.794 | | 0.3628 | 20.13 | 3000 | 0.4385 | 0.8019 | 0.802 | | 0.3505 | 21.48 | 3200 | 0.4360 | 0.8079 | 0.808 | | 0.3513 | 22.82 | 3400 | 0.4419 | 0.8037 | 0.804 | | 0.345 | 24.16 | 3600 | 0.4359 | 0.8080 | 0.808 | | 0.3405 | 25.5 | 3800 | 0.4313 | 0.8097 | 0.81 | | 0.3327 | 26.85 | 4000 | 0.4307 | 0.8130 | 0.813 | | 0.3347 | 28.19 | 4200 | 0.4333 | 0.7970 | 0.797 | | 0.319 | 29.53 | 4400 | 0.4489 | 0.8188 | 0.819 | | 0.3213 | 30.87 | 4600 | 0.4355 | 0.8050 | 0.805 | | 0.3171 | 32.21 | 4800 | 0.4279 | 0.8090 | 0.809 | | 0.3143 | 33.56 | 5000 | 0.4330 | 0.8120 | 0.812 | | 0.3113 | 34.9 | 5200 | 0.4400 | 0.8070 | 0.807 | | 0.3048 | 36.24 | 5400 | 0.4414 | 0.798 | 0.798 | | 0.2986 | 37.58 | 5600 | 0.4316 | 0.8146 | 0.815 | | 0.295 | 38.93 | 5800 | 0.4465 | 0.8040 | 0.804 | | 0.295 | 40.27 | 6000 | 0.4404 | 0.8098 | 0.81 | | 0.2883 | 41.61 | 6200 | 0.4515 | 0.8090 | 0.809 | | 0.2897 | 42.95 | 6400 | 0.4408 | 0.8110 | 0.811 | | 0.2857 | 44.3 | 6600 | 0.4365 | 0.8145 | 0.815 | | 0.2787 | 45.64 | 6800 | 0.4331 | 0.8120 | 0.812 | | 0.2862 | 46.98 | 7000 | 0.4335 | 0.8189 | 0.819 | | 0.2767 | 48.32 | 7200 | 0.4339 | 0.8148 | 0.815 | | 0.2712 | 49.66 | 7400 | 0.4270 | 0.8129 | 0.813 | | 0.2712 | 51.01 | 7600 | 0.4322 | 0.8170 | 0.817 | | 0.2708 | 52.35 | 7800 | 0.4382 | 0.8198 | 0.82 | | 0.2644 | 53.69 | 8000 | 0.4400 | 0.8160 | 0.816 | | 0.2678 | 55.03 | 8200 | 0.4366 | 0.8230 | 0.823 | | 0.2635 | 56.38 | 8400 | 0.4318 | 0.8229 | 0.823 | | 0.261 | 57.72 | 8600 | 0.4403 | 0.8178 | 0.818 | | 0.262 | 59.06 | 8800 | 0.4338 | 0.8179 | 0.818 | | 0.2617 | 60.4 | 9000 | 0.4364 | 0.8220 | 0.822 | | 0.2545 | 61.74 | 9200 | 0.4385 | 0.8219 | 0.822 | | 0.2568 | 63.09 | 9400 | 0.4400 | 0.8289 | 0.829 | | 0.257 | 64.43 | 9600 | 0.4372 | 0.8239 | 0.824 | | 0.2581 | 65.77 | 9800 | 0.4372 | 0.8249 | 0.825 | | 0.2546 | 67.11 | 10000 | 0.4370 | 0.8259 | 0.826 | ### 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_tf_4-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_4096_512_15M-L32_f

2024-05-03T19:09:48+00:00

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_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_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5670 - F1 Score: 0.6927 - Accuracy: 0.696 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6616 | 0.93 | 200 | 0.6000 | 0.6809 | 0.682 | | 0.618 | 1.87 | 400 | 0.5894 | 0.6799 | 0.68 | | 0.6049 | 2.8 | 600 | 0.5722 | 0.7073 | 0.711 | | 0.6017 | 3.74 | 800 | 0.5692 | 0.7085 | 0.71 | | 0.5991 | 4.67 | 1000 | 0.5638 | 0.7147 | 0.716 | | 0.5926 | 5.61 | 1200 | 0.5632 | 0.7197 | 0.72 | | 0.5904 | 6.54 | 1400 | 0.5591 | 0.7143 | 0.716 | | 0.5889 | 7.48 | 1600 | 0.5586 | 0.7229 | 0.723 | | 0.5877 | 8.41 | 1800 | 0.5571 | 0.7163 | 0.717 | | 0.59 | 9.35 | 2000 | 0.5569 | 0.7177 | 0.719 | | 0.5839 | 10.28 | 2200 | 0.5590 | 0.7100 | 0.71 | | 0.5842 | 11.21 | 2400 | 0.5519 | 0.7183 | 0.72 | | 0.5841 | 12.15 | 2600 | 0.5506 | 0.7176 | 0.721 | | 0.581 | 13.08 | 2800 | 0.5494 | 0.7161 | 0.719 | | 0.5822 | 14.02 | 3000 | 0.5530 | 0.7166 | 0.717 | | 0.5808 | 14.95 | 3200 | 0.5503 | 0.7212 | 0.722 | | 0.5786 | 15.89 | 3400 | 0.5493 | 0.7234 | 0.725 | | 0.5755 | 16.82 | 3600 | 0.5515 | 0.7176 | 0.718 | | 0.5761 | 17.76 | 3800 | 0.5495 | 0.7271 | 0.729 | | 0.5766 | 18.69 | 4000 | 0.5525 | 0.7197 | 0.72 | | 0.5732 | 19.63 | 4200 | 0.5478 | 0.7169 | 0.721 | | 0.5766 | 20.56 | 4400 | 0.5462 | 0.7184 | 0.72 | | 0.5746 | 21.5 | 4600 | 0.5500 | 0.7120 | 0.712 | | 0.5734 | 22.43 | 4800 | 0.5467 | 0.7263 | 0.728 | | 0.5739 | 23.36 | 5000 | 0.5478 | 0.7246 | 0.725 | | 0.5734 | 24.3 | 5200 | 0.5494 | 0.7121 | 0.712 | | 0.5696 | 25.23 | 5400 | 0.5453 | 0.7188 | 0.722 | | 0.5745 | 26.17 | 5600 | 0.5448 | 0.7234 | 0.725 | | 0.568 | 27.1 | 5800 | 0.5439 | 0.7209 | 0.724 | | 0.5682 | 28.04 | 6000 | 0.5437 | 0.7299 | 0.731 | | 0.569 | 28.97 | 6200 | 0.5486 | 0.7161 | 0.716 | | 0.5717 | 29.91 | 6400 | 0.5448 | 0.7316 | 0.733 | | 0.5681 | 30.84 | 6600 | 0.5447 | 0.7337 | 0.735 | | 0.5686 | 31.78 | 6800 | 0.5464 | 0.7217 | 0.722 | | 0.5681 | 32.71 | 7000 | 0.5444 | 0.7319 | 0.733 | | 0.5714 | 33.64 | 7200 | 0.5447 | 0.7315 | 0.733 | | 0.5642 | 34.58 | 7400 | 0.5480 | 0.7131 | 0.713 | | 0.5704 | 35.51 | 7600 | 0.5458 | 0.7226 | 0.723 | | 0.5689 | 36.45 | 7800 | 0.5453 | 0.7246 | 0.725 | | 0.5676 | 37.38 | 8000 | 0.5453 | 0.7236 | 0.724 | | 0.5647 | 38.32 | 8200 | 0.5449 | 0.7317 | 0.733 | | 0.5652 | 39.25 | 8400 | 0.5451 | 0.7284 | 0.729 | | 0.5662 | 40.19 | 8600 | 0.5453 | 0.7284 | 0.729 | | 0.5649 | 41.12 | 8800 | 0.5455 | 0.7275 | 0.728 | | 0.5682 | 42.06 | 9000 | 0.5454 | 0.7285 | 0.729 | | 0.5665 | 42.99 | 9200 | 0.5461 | 0.7217 | 0.722 | | 0.565 | 43.93 | 9400 | 0.5464 | 0.7199 | 0.72 | | 0.5637 | 44.86 | 9600 | 0.5452 | 0.7266 | 0.727 | | 0.5659 | 45.79 | 9800 | 0.5451 | 0.7285 | 0.729 | | 0.562 | 46.73 | 10000 | 0.5452 | 0.7256 | 0.726 | ### 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_tf_3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L1_f

2024-05-03T19:09:48+00:00

transformers

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct"}

arnavgrg/Meta-Llama-3-70B-Instruct-dequantized

2024-05-03T19:10:06+00:00

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_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_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5516 - F1 Score: 0.7033 - Accuracy: 0.706 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6433 | 0.93 | 200 | 0.5805 | 0.7011 | 0.701 | | 0.6017 | 1.87 | 400 | 0.5751 | 0.7004 | 0.701 | | 0.594 | 2.8 | 600 | 0.5608 | 0.7085 | 0.711 | | 0.5899 | 3.74 | 800 | 0.5582 | 0.7090 | 0.709 | | 0.5889 | 4.67 | 1000 | 0.5522 | 0.7134 | 0.714 | | 0.5826 | 5.61 | 1200 | 0.5491 | 0.7141 | 0.715 | | 0.5801 | 6.54 | 1400 | 0.5494 | 0.7171 | 0.718 | | 0.5778 | 7.48 | 1600 | 0.5482 | 0.7223 | 0.723 | | 0.5758 | 8.41 | 1800 | 0.5475 | 0.7218 | 0.722 | | 0.5787 | 9.35 | 2000 | 0.5472 | 0.7054 | 0.709 | | 0.5717 | 10.28 | 2200 | 0.5482 | 0.7199 | 0.72 | | 0.5721 | 11.21 | 2400 | 0.5441 | 0.7227 | 0.724 | | 0.5709 | 12.15 | 2600 | 0.5453 | 0.7008 | 0.707 | | 0.5673 | 13.08 | 2800 | 0.5479 | 0.6937 | 0.701 | | 0.5676 | 14.02 | 3000 | 0.5444 | 0.7196 | 0.721 | | 0.5661 | 14.95 | 3200 | 0.5459 | 0.7086 | 0.712 | | 0.5641 | 15.89 | 3400 | 0.5448 | 0.7142 | 0.716 | | 0.5601 | 16.82 | 3600 | 0.5457 | 0.7172 | 0.719 | | 0.5597 | 17.76 | 3800 | 0.5455 | 0.7127 | 0.716 | | 0.5602 | 18.69 | 4000 | 0.5471 | 0.7187 | 0.719 | | 0.558 | 19.63 | 4200 | 0.5495 | 0.7043 | 0.709 | | 0.559 | 20.56 | 4400 | 0.5477 | 0.7125 | 0.716 | | 0.5577 | 21.5 | 4600 | 0.5518 | 0.7161 | 0.716 | | 0.5555 | 22.43 | 4800 | 0.5469 | 0.7103 | 0.714 | | 0.5556 | 23.36 | 5000 | 0.5495 | 0.7171 | 0.717 | | 0.5544 | 24.3 | 5200 | 0.5554 | 0.6955 | 0.696 | | 0.5502 | 25.23 | 5400 | 0.5482 | 0.7157 | 0.719 | | 0.5575 | 26.17 | 5600 | 0.5434 | 0.7264 | 0.728 | | 0.5477 | 27.1 | 5800 | 0.5433 | 0.7174 | 0.719 | | 0.5481 | 28.04 | 6000 | 0.5441 | 0.7282 | 0.73 | | 0.5482 | 28.97 | 6200 | 0.5480 | 0.7231 | 0.723 | | 0.5491 | 29.91 | 6400 | 0.5455 | 0.7245 | 0.727 | | 0.5473 | 30.84 | 6600 | 0.5441 | 0.7217 | 0.723 | | 0.5492 | 31.78 | 6800 | 0.5472 | 0.7217 | 0.722 | | 0.5466 | 32.71 | 7000 | 0.5442 | 0.7272 | 0.728 | | 0.5503 | 33.64 | 7200 | 0.5444 | 0.7283 | 0.73 | | 0.542 | 34.58 | 7400 | 0.5502 | 0.7191 | 0.719 | | 0.5477 | 35.51 | 7600 | 0.5458 | 0.7290 | 0.729 | | 0.5467 | 36.45 | 7800 | 0.5461 | 0.7257 | 0.726 | | 0.5466 | 37.38 | 8000 | 0.5456 | 0.7278 | 0.728 | | 0.5417 | 38.32 | 8200 | 0.5471 | 0.7259 | 0.727 | | 0.5427 | 39.25 | 8400 | 0.5465 | 0.7237 | 0.724 | | 0.5423 | 40.19 | 8600 | 0.5461 | 0.7255 | 0.726 | | 0.5414 | 41.12 | 8800 | 0.5461 | 0.7285 | 0.729 | | 0.5451 | 42.06 | 9000 | 0.5452 | 0.7277 | 0.728 | | 0.5428 | 42.99 | 9200 | 0.5468 | 0.7259 | 0.726 | | 0.541 | 43.93 | 9400 | 0.5469 | 0.7259 | 0.726 | | 0.538 | 44.86 | 9600 | 0.5463 | 0.7257 | 0.726 | | 0.5423 | 45.79 | 9800 | 0.5461 | 0.7293 | 0.73 | | 0.5373 | 46.73 | 10000 | 0.5468 | 0.7248 | 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_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L8_f

2024-05-03T19:10:15+00:00

{}

darksparda07/Angelina-Jolie-v1

2024-05-03T19:11:08+00:00

<!-- 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_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_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7710 - Accuracy: 0.771 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6238 | 1.34 | 200 | 0.5599 | 0.7084 | 0.71 | | 0.5565 | 2.68 | 400 | 0.5340 | 0.7329 | 0.733 | | 0.538 | 4.03 | 600 | 0.5271 | 0.7310 | 0.731 | | 0.5338 | 5.37 | 800 | 0.5242 | 0.7350 | 0.735 | | 0.5298 | 6.71 | 1000 | 0.5203 | 0.7365 | 0.737 | | 0.5247 | 8.05 | 1200 | 0.5171 | 0.7490 | 0.749 | | 0.5214 | 9.4 | 1400 | 0.5140 | 0.7415 | 0.742 | | 0.5209 | 10.74 | 1600 | 0.5141 | 0.7418 | 0.742 | | 0.5181 | 12.08 | 1800 | 0.5195 | 0.7438 | 0.744 | | 0.5183 | 13.42 | 2000 | 0.5135 | 0.7440 | 0.744 | | 0.5182 | 14.77 | 2200 | 0.5122 | 0.7470 | 0.747 | | 0.5123 | 16.11 | 2400 | 0.5162 | 0.7407 | 0.741 | | 0.5154 | 17.45 | 2600 | 0.5111 | 0.7399 | 0.74 | | 0.5098 | 18.79 | 2800 | 0.5099 | 0.7400 | 0.74 | | 0.5091 | 20.13 | 3000 | 0.5103 | 0.7400 | 0.74 | | 0.5095 | 21.48 | 3200 | 0.5116 | 0.7359 | 0.736 | | 0.5106 | 22.82 | 3400 | 0.5074 | 0.7399 | 0.74 | | 0.5052 | 24.16 | 3600 | 0.5060 | 0.7358 | 0.736 | | 0.5024 | 25.5 | 3800 | 0.5064 | 0.7342 | 0.735 | | 0.505 | 26.85 | 4000 | 0.5060 | 0.7375 | 0.738 | | 0.5014 | 28.19 | 4200 | 0.5058 | 0.7340 | 0.734 | | 0.5024 | 29.53 | 4400 | 0.5097 | 0.7410 | 0.741 | | 0.5034 | 30.87 | 4600 | 0.5076 | 0.7380 | 0.738 | | 0.5015 | 32.21 | 4800 | 0.5058 | 0.7390 | 0.739 | | 0.5012 | 33.56 | 5000 | 0.5107 | 0.7417 | 0.742 | | 0.5032 | 34.9 | 5200 | 0.5063 | 0.7389 | 0.739 | | 0.4975 | 36.24 | 5400 | 0.5017 | 0.7367 | 0.737 | | 0.4993 | 37.58 | 5600 | 0.5034 | 0.7420 | 0.742 | | 0.4966 | 38.93 | 5800 | 0.5047 | 0.7370 | 0.737 | | 0.497 | 40.27 | 6000 | 0.5033 | 0.7360 | 0.736 | | 0.4973 | 41.61 | 6200 | 0.5028 | 0.7320 | 0.732 | | 0.4951 | 42.95 | 6400 | 0.5043 | 0.7340 | 0.734 | | 0.4949 | 44.3 | 6600 | 0.5056 | 0.7370 | 0.737 | | 0.4977 | 45.64 | 6800 | 0.5057 | 0.7420 | 0.742 | | 0.4943 | 46.98 | 7000 | 0.5042 | 0.7400 | 0.74 | | 0.4949 | 48.32 | 7200 | 0.5059 | 0.7380 | 0.738 | | 0.4923 | 49.66 | 7400 | 0.5017 | 0.7390 | 0.739 | | 0.4941 | 51.01 | 7600 | 0.5031 | 0.7400 | 0.74 | | 0.4942 | 52.35 | 7800 | 0.5022 | 0.7390 | 0.739 | | 0.4957 | 53.69 | 8000 | 0.5019 | 0.7299 | 0.73 | | 0.492 | 55.03 | 8200 | 0.5023 | 0.7410 | 0.741 | | 0.4959 | 56.38 | 8400 | 0.5038 | 0.7400 | 0.74 | | 0.494 | 57.72 | 8600 | 0.5026 | 0.7370 | 0.737 | | 0.4905 | 59.06 | 8800 | 0.5026 | 0.7340 | 0.734 | | 0.4909 | 60.4 | 9000 | 0.5039 | 0.7390 | 0.739 | | 0.4921 | 61.74 | 9200 | 0.5022 | 0.7360 | 0.736 | | 0.4956 | 63.09 | 9400 | 0.5020 | 0.7360 | 0.736 | | 0.4896 | 64.43 | 9600 | 0.5025 | 0.7380 | 0.738 | | 0.4913 | 65.77 | 9800 | 0.5032 | 0.7370 | 0.737 | | 0.4887 | 67.11 | 10000 | 0.5025 | 0.7370 | 0.737 | ### 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_tf_2-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L1_f

2024-05-03T19:11:10+00:00

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": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_32_32_0.05_2_5e-05

2024-05-03T19:11:20+00:00

{}

nihal-mp/Llama2-7b-chat-finetune_medical

2024-05-03T19:11:26+00:00

{}

ttc0000/mistral_HFTrainer_instruct02_Sample1_lora_r64_a128_optim32bit_cosine

2024-05-03T19:11:30+00:00

{}

GitBag/rebel_nectar_1714606257

2024-05-03T19:12:39+00:00

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": []}

Armandodelca/Prototipo_7_EMI

2024-05-03T19:12:51+00:00

{"license": "mit"}

sureshsanghani/sn25_7

2024-05-03T19:13:12+00:00

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sqlcoder-7b-2 - bnb 8bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation.  ## 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:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] ### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)

{}

RichardErkhov/defog_-_sqlcoder-7b-2-8bits

2024-05-03T19:13:29+00:00

{"license": "mit"}

sureshsanghani/sn25_8

2024-05-03T19:13:33+00:00

{"license": "mit"}

sureshsanghani/sn25_9

2024-05-03T19:13:55+00:00

<!-- 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_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_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7889 - Accuracy: 0.789 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5955 | 1.34 | 200 | 0.5400 | 0.7222 | 0.723 | | 0.5387 | 2.68 | 400 | 0.5279 | 0.7378 | 0.738 | | 0.5259 | 4.03 | 600 | 0.5212 | 0.7440 | 0.744 | | 0.5216 | 5.37 | 800 | 0.5194 | 0.7418 | 0.742 | | 0.5186 | 6.71 | 1000 | 0.5178 | 0.7380 | 0.738 | | 0.5121 | 8.05 | 1200 | 0.5113 | 0.7370 | 0.737 | | 0.5072 | 9.4 | 1400 | 0.5088 | 0.7378 | 0.738 | | 0.5049 | 10.74 | 1600 | 0.5100 | 0.7390 | 0.739 | | 0.5029 | 12.08 | 1800 | 0.5164 | 0.7475 | 0.748 | | 0.4997 | 13.42 | 2000 | 0.5137 | 0.7435 | 0.744 | | 0.5004 | 14.77 | 2200 | 0.5058 | 0.7422 | 0.743 | | 0.4932 | 16.11 | 2400 | 0.5088 | 0.7445 | 0.745 | | 0.4954 | 17.45 | 2600 | 0.5046 | 0.7419 | 0.742 | | 0.489 | 18.79 | 2800 | 0.4987 | 0.7417 | 0.742 | | 0.4875 | 20.13 | 3000 | 0.5027 | 0.7400 | 0.74 | | 0.486 | 21.48 | 3200 | 0.5136 | 0.7389 | 0.74 | | 0.4861 | 22.82 | 3400 | 0.5056 | 0.7339 | 0.734 | | 0.4817 | 24.16 | 3600 | 0.4967 | 0.7400 | 0.74 | | 0.4779 | 25.5 | 3800 | 0.4973 | 0.7370 | 0.737 | | 0.4792 | 26.85 | 4000 | 0.5002 | 0.7398 | 0.74 | | 0.4759 | 28.19 | 4200 | 0.5024 | 0.7369 | 0.737 | | 0.4746 | 29.53 | 4400 | 0.5073 | 0.7470 | 0.747 | | 0.4749 | 30.87 | 4600 | 0.5034 | 0.7409 | 0.741 | | 0.4733 | 32.21 | 4800 | 0.4998 | 0.7419 | 0.742 | | 0.4726 | 33.56 | 5000 | 0.5061 | 0.7393 | 0.74 | | 0.4737 | 34.9 | 5200 | 0.5063 | 0.7414 | 0.742 | | 0.4669 | 36.24 | 5400 | 0.4962 | 0.7449 | 0.745 | | 0.469 | 37.58 | 5600 | 0.5000 | 0.7450 | 0.745 | | 0.4658 | 38.93 | 5800 | 0.5001 | 0.7380 | 0.738 | | 0.4631 | 40.27 | 6000 | 0.5003 | 0.7379 | 0.738 | | 0.464 | 41.61 | 6200 | 0.4970 | 0.7400 | 0.74 | | 0.4623 | 42.95 | 6400 | 0.5046 | 0.7459 | 0.746 | | 0.46 | 44.3 | 6600 | 0.5083 | 0.7489 | 0.749 | | 0.4634 | 45.64 | 6800 | 0.5060 | 0.7437 | 0.744 | | 0.4588 | 46.98 | 7000 | 0.5045 | 0.7439 | 0.744 | | 0.4597 | 48.32 | 7200 | 0.5028 | 0.746 | 0.746 | | 0.4557 | 49.66 | 7400 | 0.5030 | 0.7510 | 0.751 | | 0.4585 | 51.01 | 7600 | 0.5068 | 0.7386 | 0.739 | | 0.4579 | 52.35 | 7800 | 0.5012 | 0.7440 | 0.744 | | 0.4594 | 53.69 | 8000 | 0.5003 | 0.7460 | 0.746 | | 0.4561 | 55.03 | 8200 | 0.5002 | 0.7450 | 0.745 | | 0.4584 | 56.38 | 8400 | 0.5024 | 0.7428 | 0.743 | | 0.4565 | 57.72 | 8600 | 0.5004 | 0.7470 | 0.747 | | 0.4528 | 59.06 | 8800 | 0.5026 | 0.7459 | 0.746 | | 0.4547 | 60.4 | 9000 | 0.5034 | 0.7458 | 0.746 | | 0.4547 | 61.74 | 9200 | 0.5012 | 0.7459 | 0.746 | | 0.4584 | 63.09 | 9400 | 0.5009 | 0.7459 | 0.746 | | 0.4507 | 64.43 | 9600 | 0.5012 | 0.7489 | 0.749 | | 0.4539 | 65.77 | 9800 | 0.5020 | 0.7469 | 0.747 | | 0.4504 | 67.11 | 10000 | 0.5006 | 0.7470 | 0.747 | ### 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_tf_2-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L8_f

2024-05-03T19:14:29+00:00

<!-- 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_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_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4798 - F1 Score: 0.7869 - Accuracy: 0.787 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.578 | 1.34 | 200 | 0.5342 | 0.7303 | 0.733 | | 0.531 | 2.68 | 400 | 0.5274 | 0.7439 | 0.745 | | 0.5177 | 4.03 | 600 | 0.5157 | 0.7400 | 0.74 | | 0.5099 | 5.37 | 800 | 0.5128 | 0.7489 | 0.749 | | 0.5048 | 6.71 | 1000 | 0.5149 | 0.7448 | 0.745 | | 0.4968 | 8.05 | 1200 | 0.5041 | 0.7375 | 0.738 | | 0.4897 | 9.4 | 1400 | 0.5042 | 0.7520 | 0.752 | | 0.486 | 10.74 | 1600 | 0.5024 | 0.7480 | 0.748 | | 0.4817 | 12.08 | 1800 | 0.5059 | 0.7574 | 0.758 | | 0.4755 | 13.42 | 2000 | 0.5121 | 0.7437 | 0.744 | | 0.4763 | 14.77 | 2200 | 0.5078 | 0.7339 | 0.736 | | 0.4663 | 16.11 | 2400 | 0.5129 | 0.7577 | 0.758 | | 0.4685 | 17.45 | 2600 | 0.5037 | 0.7478 | 0.748 | | 0.4603 | 18.79 | 2800 | 0.4975 | 0.7444 | 0.745 | | 0.4557 | 20.13 | 3000 | 0.5109 | 0.7469 | 0.747 | | 0.4502 | 21.48 | 3200 | 0.5222 | 0.7300 | 0.731 | | 0.4525 | 22.82 | 3400 | 0.5181 | 0.7539 | 0.754 | | 0.4457 | 24.16 | 3600 | 0.5046 | 0.7480 | 0.748 | | 0.4382 | 25.5 | 3800 | 0.5103 | 0.7479 | 0.748 | | 0.4378 | 26.85 | 4000 | 0.5076 | 0.7479 | 0.748 | | 0.4323 | 28.19 | 4200 | 0.5127 | 0.7404 | 0.741 | | 0.4281 | 29.53 | 4400 | 0.5187 | 0.7369 | 0.737 | | 0.4288 | 30.87 | 4600 | 0.5104 | 0.7460 | 0.746 | | 0.4232 | 32.21 | 4800 | 0.5187 | 0.7560 | 0.756 | | 0.4203 | 33.56 | 5000 | 0.5202 | 0.7537 | 0.754 | | 0.4205 | 34.9 | 5200 | 0.5271 | 0.7454 | 0.746 | | 0.409 | 36.24 | 5400 | 0.5216 | 0.7489 | 0.749 | | 0.4114 | 37.58 | 5600 | 0.5241 | 0.7477 | 0.748 | | 0.4077 | 38.93 | 5800 | 0.5173 | 0.7479 | 0.748 | | 0.404 | 40.27 | 6000 | 0.5202 | 0.7560 | 0.756 | | 0.4026 | 41.61 | 6200 | 0.5207 | 0.7430 | 0.743 | | 0.3983 | 42.95 | 6400 | 0.5391 | 0.7477 | 0.748 | | 0.3954 | 44.3 | 6600 | 0.5431 | 0.7377 | 0.738 | | 0.3973 | 45.64 | 6800 | 0.5416 | 0.7351 | 0.736 | | 0.3911 | 46.98 | 7000 | 0.5404 | 0.7419 | 0.742 | | 0.3916 | 48.32 | 7200 | 0.5340 | 0.7429 | 0.743 | | 0.3874 | 49.66 | 7400 | 0.5330 | 0.7450 | 0.745 | | 0.3831 | 51.01 | 7600 | 0.5419 | 0.7387 | 0.739 | | 0.3811 | 52.35 | 7800 | 0.5460 | 0.7430 | 0.743 | | 0.3823 | 53.69 | 8000 | 0.5400 | 0.7440 | 0.744 | | 0.3795 | 55.03 | 8200 | 0.5479 | 0.7407 | 0.741 | | 0.3828 | 56.38 | 8400 | 0.5518 | 0.7407 | 0.741 | | 0.379 | 57.72 | 8600 | 0.5405 | 0.7458 | 0.746 | | 0.3751 | 59.06 | 8800 | 0.5438 | 0.7388 | 0.739 | | 0.3759 | 60.4 | 9000 | 0.5491 | 0.7407 | 0.741 | | 0.3729 | 61.74 | 9200 | 0.5489 | 0.7458 | 0.746 | | 0.3759 | 63.09 | 9400 | 0.5501 | 0.7437 | 0.744 | | 0.3732 | 64.43 | 9600 | 0.5483 | 0.7388 | 0.739 | | 0.375 | 65.77 | 9800 | 0.5503 | 0.7446 | 0.745 | | 0.369 | 67.11 | 10000 | 0.5488 | 0.7369 | 0.737 | ### 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_tf_2-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L32_f

2024-05-03T19:15:29+00:00

<!-- 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_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_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.9282 - F1 Score: 0.2779 - Accuracy: 0.2832 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1857 | 0.35 | 200 | 2.1860 | 0.0716 | 0.1278 | | 2.183 | 0.7 | 400 | 2.1840 | 0.0572 | 0.1247 | | 2.1793 | 1.05 | 600 | 2.1784 | 0.0832 | 0.1352 | | 2.1765 | 1.4 | 800 | 2.1736 | 0.0908 | 0.1396 | | 2.1694 | 1.75 | 1000 | 2.1685 | 0.1077 | 0.1492 | | 2.1674 | 2.09 | 1200 | 2.1739 | 0.0989 | 0.1451 | | 2.1644 | 2.44 | 1400 | 2.1706 | 0.1217 | 0.1480 | | 2.1612 | 2.79 | 1600 | 2.1591 | 0.1324 | 0.1615 | | 2.1568 | 3.14 | 1800 | 2.1538 | 0.1270 | 0.1687 | | 2.1522 | 3.49 | 2000 | 2.1578 | 0.1333 | 0.1689 | | 2.1535 | 3.84 | 2200 | 2.1464 | 0.1515 | 0.1814 | | 2.1446 | 4.19 | 2400 | 2.1428 | 0.1506 | 0.1716 | | 2.1418 | 4.54 | 2600 | 2.1369 | 0.1560 | 0.1859 | | 2.138 | 4.89 | 2800 | 2.1304 | 0.1727 | 0.1887 | | 2.133 | 5.24 | 3000 | 2.1352 | 0.1610 | 0.1908 | | 2.1303 | 5.58 | 3200 | 2.1227 | 0.1787 | 0.2040 | | 2.127 | 5.93 | 3400 | 2.1300 | 0.1451 | 0.1809 | | 2.121 | 6.28 | 3600 | 2.1118 | 0.1827 | 0.2046 | | 2.1132 | 6.63 | 3800 | 2.0989 | 0.1781 | 0.2027 | | 2.11 | 6.98 | 4000 | 2.0828 | 0.2078 | 0.2254 | | 2.0955 | 7.33 | 4200 | 2.0556 | 0.2196 | 0.2338 | | 2.0834 | 7.68 | 4400 | 2.0488 | 0.2224 | 0.2342 | | 2.0747 | 8.03 | 4600 | 2.0685 | 0.1803 | 0.2083 | | 2.0662 | 8.38 | 4800 | 2.0344 | 0.2150 | 0.2323 | | 2.0627 | 8.73 | 5000 | 2.0267 | 0.2107 | 0.2333 | | 2.0541 | 9.08 | 5200 | 2.0213 | 0.2244 | 0.2355 | | 2.0482 | 9.42 | 5400 | 2.0056 | 0.2347 | 0.2490 | | 2.0413 | 9.77 | 5600 | 2.0041 | 0.2293 | 0.2441 | | 2.0395 | 10.12 | 5800 | 1.9909 | 0.2505 | 0.2573 | | 2.0322 | 10.47 | 6000 | 1.9841 | 0.2563 | 0.2616 | | 2.0275 | 10.82 | 6200 | 1.9875 | 0.2414 | 0.2515 | | 2.0227 | 11.17 | 6400 | 1.9840 | 0.2401 | 0.2509 | | 2.0205 | 11.52 | 6600 | 1.9861 | 0.2374 | 0.2514 | | 2.0191 | 11.87 | 6800 | 1.9717 | 0.2484 | 0.2594 | | 2.0118 | 12.22 | 7000 | 1.9615 | 0.2657 | 0.2700 | | 2.008 | 12.57 | 7200 | 1.9528 | 0.2658 | 0.2708 | | 2.0108 | 12.91 | 7400 | 1.9626 | 0.2555 | 0.2638 | | 2.0043 | 13.26 | 7600 | 1.9508 | 0.2567 | 0.2681 | | 1.9972 | 13.61 | 7800 | 1.9566 | 0.2538 | 0.2635 | | 1.9999 | 13.96 | 8000 | 1.9473 | 0.2719 | 0.2755 | | 1.9947 | 14.31 | 8200 | 1.9432 | 0.2678 | 0.2758 | | 1.9987 | 14.66 | 8400 | 1.9337 | 0.2747 | 0.2785 | | 1.9902 | 15.01 | 8600 | 1.9422 | 0.2650 | 0.2717 | | 1.9921 | 15.36 | 8800 | 1.9332 | 0.2762 | 0.2783 | | 1.9841 | 15.71 | 9000 | 1.9405 | 0.2699 | 0.2780 | | 1.9876 | 16.06 | 9200 | 1.9298 | 0.2772 | 0.2806 | | 1.9878 | 16.4 | 9400 | 1.9299 | 0.2749 | 0.2798 | | 1.9869 | 16.75 | 9600 | 1.9348 | 0.2755 | 0.2804 | | 1.9865 | 17.1 | 9800 | 1.9314 | 0.2739 | 0.2793 | | 1.9921 | 17.45 | 10000 | 1.9304 | 0.2764 | 0.2804 | ### 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_virus_covid-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L1_f

2024-05-03T19:16:42+00:00

{"license": "cc-by-sa-4.0"}

grecosalvatore/binary-toxicity-BERT-xai-course

2024-05-03T19:16:45+00:00

# 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": []}

golf2248/ofn2ele

2024-05-03T19:16:49+00:00

{}

andrealexroom/MultiARoomv0.0.0.1.6

2024-05-03T19:16:53+00:00

<!-- 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_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_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.5376 - F1 Score: 0.4255 - Accuracy: 0.4204 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1853 | 0.35 | 200 | 2.1837 | 0.0874 | 0.1342 | | 2.1805 | 0.7 | 400 | 2.1771 | 0.0925 | 0.1295 | | 2.1715 | 1.05 | 600 | 2.1691 | 0.1098 | 0.1369 | | 2.1617 | 1.4 | 800 | 2.1614 | 0.1110 | 0.1502 | | 2.1494 | 1.75 | 1000 | 2.1453 | 0.1504 | 0.1804 | | 2.1399 | 2.09 | 1200 | 2.1424 | 0.1226 | 0.1747 | | 2.11 | 2.44 | 1400 | 2.0699 | 0.1890 | 0.2135 | | 2.0694 | 2.79 | 1600 | 2.0231 | 0.2130 | 0.2406 | | 2.0288 | 3.14 | 1800 | 2.0134 | 0.2062 | 0.2318 | | 1.9962 | 3.49 | 2000 | 1.9502 | 0.2475 | 0.2598 | | 1.9712 | 3.84 | 2200 | 1.8961 | 0.2710 | 0.2816 | | 1.9382 | 4.19 | 2400 | 1.8577 | 0.2936 | 0.2901 | | 1.9121 | 4.54 | 2600 | 1.8328 | 0.3132 | 0.3178 | | 1.8976 | 4.89 | 2800 | 1.8175 | 0.3134 | 0.3129 | | 1.875 | 5.24 | 3000 | 1.7826 | 0.3280 | 0.3340 | | 1.8617 | 5.58 | 3200 | 1.7518 | 0.3499 | 0.3488 | | 1.8365 | 5.93 | 3400 | 1.7553 | 0.3296 | 0.3388 | | 1.8209 | 6.28 | 3600 | 1.7260 | 0.3515 | 0.3516 | | 1.8059 | 6.63 | 3800 | 1.7081 | 0.3620 | 0.3599 | | 1.8003 | 6.98 | 4000 | 1.7012 | 0.3732 | 0.3702 | | 1.7834 | 7.33 | 4200 | 1.6943 | 0.3664 | 0.3658 | | 1.7706 | 7.68 | 4400 | 1.6790 | 0.3783 | 0.3660 | | 1.767 | 8.03 | 4600 | 1.6793 | 0.3684 | 0.3688 | | 1.7547 | 8.38 | 4800 | 1.6680 | 0.3748 | 0.3752 | | 1.7509 | 8.73 | 5000 | 1.6592 | 0.3763 | 0.3802 | | 1.7496 | 9.08 | 5200 | 1.6561 | 0.3869 | 0.3803 | | 1.7273 | 9.42 | 5400 | 1.6421 | 0.3869 | 0.3880 | | 1.7283 | 9.77 | 5600 | 1.6331 | 0.3979 | 0.3955 | | 1.725 | 10.12 | 5800 | 1.6186 | 0.4024 | 0.3932 | | 1.7221 | 10.47 | 6000 | 1.6145 | 0.3986 | 0.3946 | | 1.7101 | 10.82 | 6200 | 1.6078 | 0.4082 | 0.4012 | | 1.6922 | 11.17 | 6400 | 1.6023 | 0.4073 | 0.4024 | | 1.6973 | 11.52 | 6600 | 1.5917 | 0.4116 | 0.4045 | | 1.6989 | 11.87 | 6800 | 1.5862 | 0.4106 | 0.4053 | | 1.684 | 12.22 | 7000 | 1.5780 | 0.4176 | 0.4108 | | 1.674 | 12.57 | 7200 | 1.5750 | 0.4172 | 0.4123 | | 1.6799 | 12.91 | 7400 | 1.5693 | 0.4194 | 0.4140 | | 1.6687 | 13.26 | 7600 | 1.5574 | 0.4183 | 0.4153 | | 1.6716 | 13.61 | 7800 | 1.5663 | 0.4222 | 0.4162 | | 1.6615 | 13.96 | 8000 | 1.5567 | 0.4226 | 0.4177 | | 1.6562 | 14.31 | 8200 | 1.5533 | 0.4217 | 0.4166 | | 1.6584 | 14.66 | 8400 | 1.5481 | 0.4290 | 0.4196 | | 1.656 | 15.01 | 8600 | 1.5455 | 0.4272 | 0.4237 | | 1.6563 | 15.36 | 8800 | 1.5480 | 0.4297 | 0.4204 | | 1.639 | 15.71 | 9000 | 1.5463 | 0.4260 | 0.4224 | | 1.6507 | 16.06 | 9200 | 1.5438 | 0.4242 | 0.4192 | | 1.6477 | 16.4 | 9400 | 1.5385 | 0.4275 | 0.4226 | | 1.6475 | 16.75 | 9600 | 1.5404 | 0.4289 | 0.4243 | | 1.6414 | 17.1 | 9800 | 1.5406 | 0.4294 | 0.4249 | | 1.6511 | 17.45 | 10000 | 1.5388 | 0.4300 | 0.4249 | ### 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_virus_covid-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L8_f

2024-05-03T19:16:59+00:00