readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: train
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9427525378598769
      - name: Recall
        type: recall
        value: 0.9533826994278021
      - name: F1
        type: f1
        value: 0.9480378211028366
      - name: Accuracy
        type: accuracy
        value: 0.9866957084829575
  ---
  
  <!-- 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 conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1128
  - Precision: 0.9428
  - Recall: 0.9534
  - F1: 0.9480
  - Accuracy: 0.9867
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0937        | 1.0   | 1756  | 0.0660          | 0.9179    | 0.9332 | 0.9255 | 0.9825   |
  | 0.0378        | 2.0   | 3512  | 0.0766          | 0.9246    | 0.9451 | 0.9348 | 0.9843   |
  | 0.0245        | 3.0   | 5268  | 0.0667          | 0.9241    | 0.9409 | 0.9325 | 0.9843   |
  | 0.017         | 4.0   | 7024  | 0.0712          | 0.9343    | 0.9505 | 0.9424 | 0.9863   |
  | 0.0143        | 5.0   | 8780  | 0.0898          | 0.9366    | 0.9492 | 0.9428 | 0.9855   |
  | 0.0049        | 6.0   | 10536 | 0.0964          | 0.9294    | 0.9482 | 0.9387 | 0.9853   |
  | 0.0039        | 7.0   | 12292 | 0.1001          | 0.9353    | 0.9512 | 0.9432 | 0.9860   |
  | 0.0036        | 8.0   | 14048 | 0.1002          | 0.9388    | 0.9522 | 0.9454 | 0.9862   |
  | 0.0018        | 9.0   | 15804 | 0.1049          | 0.9363    | 0.9495 | 0.9428 | 0.9861   |
  | 0.0019        | 10.0  | 17560 | 0.1191          | 0.9375    | 0.9497 | 0.9436 | 0.9849   |
  | 0.0008        | 11.0  | 19316 | 0.1083          | 0.9396    | 0.9530 | 0.9463 | 0.9864   |
  | 0.0003        | 12.0  | 21072 | 0.1064          | 0.9419    | 0.9530 | 0.9475 | 0.9864   |
  | 0.0004        | 13.0  | 22828 | 0.1091          | 0.9448    | 0.9527 | 0.9487 | 0.9865   |
  | 0.0006        | 14.0  | 24584 | 0.1132          | 0.9464    | 0.9542 | 0.9503 | 0.9867   |
  | 0.0004        | 15.0  | 26340 | 0.1128          | 0.9428    | 0.9534 | 0.9480 | 0.9867   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  