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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_2024-04-25_21-48-08 |
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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_2024-04-25_21-48-08 |
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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: 0.5189 |
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- Accuracy: 0.8809 |
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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: 0.0005701568055793089 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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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: cosine |
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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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| 0.6192 | 1.0 | 128 | 0.6737 | 0.6055 | |
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| 0.4321 | 2.0 | 256 | 0.6507 | 0.6289 | |
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| 0.571 | 3.0 | 384 | 0.5572 | 0.7188 | |
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| 0.3053 | 4.0 | 512 | 0.5090 | 0.7852 | |
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| 0.5055 | 5.0 | 640 | 0.3370 | 0.8516 | |
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| 0.2786 | 6.0 | 768 | 0.3710 | 0.8594 | |
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| 0.1327 | 7.0 | 896 | 0.3055 | 0.8711 | |
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| 0.2127 | 8.0 | 1024 | 0.2891 | 0.8945 | |
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| 0.0913 | 9.0 | 1152 | 0.3454 | 0.8691 | |
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| 0.0134 | 10.0 | 1280 | 0.3354 | 0.8809 | |
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| 0.2597 | 11.0 | 1408 | 0.3436 | 0.8848 | |
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| 0.0276 | 12.0 | 1536 | 0.4181 | 0.8633 | |
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| 0.0929 | 13.0 | 1664 | 0.3722 | 0.8789 | |
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| 0.9377 | 14.0 | 1792 | 0.5086 | 0.8730 | |
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| 0.2894 | 15.0 | 1920 | 0.3311 | 0.8906 | |
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| 0.3138 | 16.0 | 2048 | 0.4739 | 0.8809 | |
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| 0.0088 | 17.0 | 2176 | 0.3875 | 0.8867 | |
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| 0.3591 | 18.0 | 2304 | 0.4032 | 0.8809 | |
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| 0.0436 | 19.0 | 2432 | 0.4316 | 0.8887 | |
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| 0.0037 | 20.0 | 2560 | 0.4931 | 0.8789 | |
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| 0.0322 | 21.0 | 2688 | 0.4787 | 0.8809 | |
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| 0.0035 | 22.0 | 2816 | 0.4460 | 0.8770 | |
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| 0.0859 | 23.0 | 2944 | 0.4914 | 0.8828 | |
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| 0.039 | 24.0 | 3072 | 0.4955 | 0.8770 | |
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| 0.4208 | 25.0 | 3200 | 0.5211 | 0.8828 | |
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| 0.1874 | 26.0 | 3328 | 0.5376 | 0.8711 | |
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| 0.4433 | 27.0 | 3456 | 0.5319 | 0.875 | |
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| 0.2976 | 28.0 | 3584 | 0.5201 | 0.8809 | |
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| 0.0223 | 29.0 | 3712 | 0.5179 | 0.8809 | |
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| 0.0021 | 30.0 | 3840 | 0.5189 | 0.8809 | |
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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 |
