readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-mutation-recognition-3
    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. -->
  
  # bert-finetuned-mutation-recognition-3
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0727
  - Dnamutation F1: 0.6484
  - Proteinmutation F1: 0.8571
  - Snp F1: 1.0
  - Precision: 0.7966
  - Recall: 0.7625
  - F1: 0.7792
  - Accuracy: 0.9872
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Dnamutation F1 | Proteinmutation F1 | Snp F1 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------------:|:------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 324  | 0.0323          | 0.5996         | 0.7886             | 1.0    | 0.6583    | 0.7982 | 0.7215 | 0.9901   |
  | 0.0788        | 2.0   | 648  | 0.0314          | 0.6765         | 0.8783             | 1.0    | 0.7453    | 0.8571 | 0.7973 | 0.9907   |
  | 0.0788        | 3.0   | 972  | 0.0306          | 0.6391         | 0.8679             | 1.0    | 0.7341    | 0.8232 | 0.7761 | 0.9903   |
  | 0.0273        | 4.0   | 1296 | 0.0424          | 0.6360         | 0.8714             | 1.0    | 0.7792    | 0.775  | 0.7771 | 0.9885   |
  | 0.0178        | 5.0   | 1620 | 0.0462          | 0.5885         | 0.8683             | 1.0    | 0.7576    | 0.7589 | 0.7583 | 0.9869   |
  | 0.0178        | 6.0   | 1944 | 0.0531          | 0.6176         | 0.8701             | 1.0    | 0.7734    | 0.7679 | 0.7706 | 0.9873   |
  | 0.0165        | 7.0   | 2268 | 0.0573          | 0.6597         | 0.8658             | 1.0    | 0.8022    | 0.775  | 0.7884 | 0.9881   |
  | 0.0144        | 8.0   | 2592 | 0.0636          | 0.6596         | 0.8454             | 1.0    | 0.7919    | 0.7679 | 0.7797 | 0.9871   |
  | 0.0144        | 9.0   | 2916 | 0.0710          | 0.6568         | 0.8748             | 1.0    | 0.8159    | 0.7679 | 0.7912 | 0.9872   |
  | 0.0108        | 10.0  | 3240 | 0.0727          | 0.6484         | 0.8571             | 1.0    | 0.7966    | 0.7625 | 0.7792 | 0.9872   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2
  - Datasets 2.0.0
  - Tokenizers 0.12.1
  