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--- |
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license: mit |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: facebook/esm2_t30_150M_UR50D |
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metrics: |
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- accuracy |
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model-index: |
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- name: esm2_t130_150M-lora-classifier_2024-04-26_10-08-51 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# esm2_t130_150M-lora-classifier_2024-04-26_10-08-51 |
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This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co./facebook/esm2_t30_150M_UR50D) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4537 |
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- Accuracy: 0.8984 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0008701568055793088 |
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- train_batch_size: 28 |
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- eval_batch_size: 28 |
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- seed: 8893 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6764 | 1.0 | 55 | 0.6794 | 0.5820 | |
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| 0.5521 | 2.0 | 110 | 0.6192 | 0.6777 | |
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| 0.5409 | 3.0 | 165 | 0.5147 | 0.7383 | |
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| 0.5518 | 4.0 | 220 | 0.3518 | 0.8672 | |
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| 0.1386 | 5.0 | 275 | 0.3596 | 0.8574 | |
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| 0.303 | 6.0 | 330 | 0.4030 | 0.8359 | |
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| 0.1962 | 7.0 | 385 | 0.3143 | 0.8848 | |
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| 0.1501 | 8.0 | 440 | 0.3232 | 0.8652 | |
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| 0.2994 | 9.0 | 495 | 0.3014 | 0.8770 | |
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| 0.0914 | 10.0 | 550 | 0.2980 | 0.8887 | |
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| 0.2108 | 11.0 | 605 | 0.2854 | 0.8770 | |
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| 0.2896 | 12.0 | 660 | 0.3684 | 0.8691 | |
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| 0.0818 | 13.0 | 715 | 0.3349 | 0.8828 | |
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| 0.3152 | 14.0 | 770 | 0.3530 | 0.8848 | |
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| 0.0554 | 15.0 | 825 | 0.3371 | 0.8887 | |
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| 0.1928 | 16.0 | 880 | 0.3347 | 0.875 | |
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| 0.2658 | 17.0 | 935 | 0.3765 | 0.8867 | |
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| 0.4242 | 18.0 | 990 | 0.4166 | 0.8945 | |
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| 0.0964 | 19.0 | 1045 | 0.3400 | 0.8945 | |
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| 0.0375 | 20.0 | 1100 | 0.3581 | 0.9004 | |
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| 0.1781 | 21.0 | 1155 | 0.3816 | 0.8848 | |
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| 0.1563 | 22.0 | 1210 | 0.3940 | 0.8867 | |
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| 0.017 | 23.0 | 1265 | 0.4098 | 0.8926 | |
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| 0.1866 | 24.0 | 1320 | 0.4710 | 0.8770 | |
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| 0.0632 | 25.0 | 1375 | 0.4541 | 0.8828 | |
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| 0.1501 | 26.0 | 1430 | 0.4645 | 0.8828 | |
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| 0.109 | 27.0 | 1485 | 0.4434 | 0.8926 | |
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| 0.0353 | 28.0 | 1540 | 0.4264 | 0.8984 | |
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| 0.4502 | 29.0 | 1595 | 0.4479 | 0.8984 | |
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| 0.0341 | 30.0 | 1650 | 0.4537 | 0.8984 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
