readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner
    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-ner
  
  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: 0.0997
  - Proteinmutation F1: 0.1309
  - Snp F1: 0.1953
  - Dnamutation F1: 0.3778
  - Precision: 0.2380
  - Recall: 0.2416
  - F1: 0.2398
  - Accuracy: 0.9703
  
  ## 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 | Proteinmutation F1 | Snp F1 | Dnamutation F1 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:|:--------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 324  | 0.0533          | 0.0396             | 0.2830 | 0.4667         | 0.2334    | 0.3221 | 0.2707 | 0.9788   |
  | 0.1072        | 2.0   | 648  | 0.0437          | 0.6065             | 0.4906 | 0.5009         | 0.4802    | 0.6348 | 0.5468 | 0.9868   |
  | 0.1072        | 3.0   | 972  | 0.0592          | 0.1379             | 0.2485 | 0.2005         | 0.1639    | 0.2228 | 0.1889 | 0.9731   |
  | 0.0573        | 4.0   | 1296 | 0.0722          | 0.0749             | 0.2530 | 0.4692         | 0.2705    | 0.2959 | 0.2826 | 0.9749   |
  | 0.0431        | 5.0   | 1620 | 0.0766          | 0.1574             | 0.1847 | 0.2540         | 0.1766    | 0.2285 | 0.1992 | 0.9723   |
  | 0.0431        | 6.0   | 1944 | 0.0805          | 0.1099             | 0.2202 | 0.2383         | 0.1657    | 0.2097 | 0.1851 | 0.9715   |
  | 0.0396        | 7.0   | 2268 | 0.0886          | 0.1337             | 0.2138 | 0.4318         | 0.2683    | 0.2678 | 0.2680 | 0.9724   |
  | 0.0354        | 8.0   | 2592 | 0.0927          | 0.1535             | 0.2113 | 0.3769         | 0.2505    | 0.2528 | 0.2516 | 0.9714   |
  | 0.0354        | 9.0   | 2916 | 0.0978          | 0.1011             | 0.2540 | 0.3812         | 0.2495    | 0.2528 | 0.2512 | 0.9705   |
  | 0.0312        | 10.0  | 3240 | 0.0997          | 0.1309             | 0.1953 | 0.3778         | 0.2380    | 0.2416 | 0.2398 | 0.9703   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2
  - Datasets 2.0.0
  - Tokenizers 0.12.1
  