readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-finetuned-conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9409771754636234
      - name: Recall
        type: recall
        value: 0.946886775524852
      - name: F1
        type: f1
        value: 0.9439227260531259
      - name: Accuracy
        type: accuracy
        value: 0.9859745687878966
  ---
  
  <!-- 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-base-cased-finetuned-conll2003
  
  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: 0.0643
  - Precision: 0.9410
  - Recall: 0.9469
  - F1: 0.9439
  - Accuracy: 0.9860
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2349        | 0.57  | 500  | 0.0885          | 0.8957    | 0.8980 | 0.8968 | 0.9747   |
  | 0.0822        | 1.14  | 1000 | 0.0774          | 0.9184    | 0.9219 | 0.9202 | 0.9802   |
  | 0.0476        | 1.71  | 1500 | 0.0683          | 0.9345    | 0.9325 | 0.9335 | 0.9833   |
  | 0.0368        | 2.28  | 2000 | 0.0653          | 0.9333    | 0.9430 | 0.9381 | 0.9847   |
  | 0.028         | 2.85  | 2500 | 0.0670          | 0.9279    | 0.9342 | 0.9311 | 0.9835   |
  | 0.0171        | 3.42  | 3000 | 0.0643          | 0.9410    | 0.9469 | 0.9439 | 0.9860   |
  | 0.0149        | 3.99  | 3500 | 0.0667          | 0.9369    | 0.9477 | 0.9422 | 0.9856   |
  | 0.0088        | 4.56  | 4000 | 0.0698          | 0.9360    | 0.9473 | 0.9416 | 0.9855   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  