readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: ESM1b_AAV2_classification
    results: []
  ---
  
  <!-- 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. -->
  
  # ESM1b_AAV2_classification
  
  To load tokenizer from ESM, you need to install transformers with this version as follow:
  
  !git clone -b add_esm-proper --single-branch https://github.com/liujas000/transformers.git 
  !pip -q install ./transformers
  
  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).
  
  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 
  Maximum length: 50
  
  It achieves the following results on the evaluation set. 
  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 :/
  
  - Loss: 0.2250
  - Accuracy: 0.9620
  - F1: 0.9632
  - Precision: 0.9642
  - Recall: 0.9622
  - Auroc: 0.9620
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 1024
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 200
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 232  | 0.1311          | 0.9495   | 0.9501 | 0.9711    | 0.9299 | 0.9502 |
  | No log        | 2.0   | 464  | 0.1032          | 0.9606   | 0.9620 | 0.9583    | 0.9657 | 0.9604 |
  | 0.1924        | 3.0   | 696  | 0.0995          | 0.9627   | 0.9641 | 0.9584    | 0.9700 | 0.9625 |
  | 0.1924        | 4.0   | 928  | 0.1218          | 0.9611   | 0.9624 | 0.9607    | 0.9641 | 0.9610 |
  | 0.067         | 5.0   | 1160 | 0.1187          | 0.9622   | 0.9633 | 0.9678    | 0.9588 | 0.9623 |
  | 0.067         | 6.0   | 1392 | 0.1514          | 0.9612   | 0.9621 | 0.9710    | 0.9534 | 0.9615 |
  | 0.0271        | 7.0   | 1624 | 0.1890          | 0.9612   | 0.9626 | 0.9580    | 0.9673 | 0.9610 |
  | 0.0271        | 8.0   | 1856 | 0.2250          | 0.9620   | 0.9632 | 0.9642    | 0.9622 | 0.9620 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.10.3
  