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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: ESM1b_AAV2_classification |
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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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# ESM1b_AAV2_classification |
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To load tokenizer from ESM, you need to install transformers with this version as follow: |
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!git clone -b add_esm-proper --single-branch https://github.com/liujas000/transformers.git |
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!pip -q install ./transformers |
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This model is a fine-tuned version of [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) on AAV2 dataset with ~230k sequences (Bryant et al 2020). |
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The WT sequence (aa561-588): D E E E I R T T N P V A T E Q Y G S V S T N L Q R G N R |
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Maximum length: 50 |
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It achieves the following results on the evaluation set. |
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Note:this is result of the last epoch, I think the pushed model is loaded with best checkpoint - best val_loss, I'm not so sure though :/ |
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- Loss: 0.2250 |
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- Accuracy: 0.9620 |
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- F1: 0.9632 |
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- Precision: 0.9642 |
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- Recall: 0.9622 |
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- Auroc: 0.9620 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 1024 |
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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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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 8 |
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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 | F1 | Precision | Recall | Auroc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 232 | 0.1311 | 0.9495 | 0.9501 | 0.9711 | 0.9299 | 0.9502 | |
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| No log | 2.0 | 464 | 0.1032 | 0.9606 | 0.9620 | 0.9583 | 0.9657 | 0.9604 | |
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| 0.1924 | 3.0 | 696 | 0.0995 | 0.9627 | 0.9641 | 0.9584 | 0.9700 | 0.9625 | |
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| 0.1924 | 4.0 | 928 | 0.1218 | 0.9611 | 0.9624 | 0.9607 | 0.9641 | 0.9610 | |
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| 0.067 | 5.0 | 1160 | 0.1187 | 0.9622 | 0.9633 | 0.9678 | 0.9588 | 0.9623 | |
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| 0.067 | 6.0 | 1392 | 0.1514 | 0.9612 | 0.9621 | 0.9710 | 0.9534 | 0.9615 | |
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| 0.0271 | 7.0 | 1624 | 0.1890 | 0.9612 | 0.9626 | 0.9580 | 0.9673 | 0.9610 | |
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| 0.0271 | 8.0 | 1856 | 0.2250 | 0.9620 | 0.9632 | 0.9642 | 0.9622 | 0.9620 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.10.3 |
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