readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-ner-conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9438052359513089
      - name: Recall
        type: recall
        value: 0.9525412319084483
      - name: F1
        type: f1
        value: 0.9481531116508919
      - name: Accuracy
        type: accuracy
        value: 0.9910634321093416
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9116307653519484
        verified: true
      - name: Precision
        type: precision
        value: 0.9366103911345081
        verified: true
      - name: Recall
        type: recall
        value: 0.9262526113340186
        verified: true
      - name: F1
        type: f1
        value: 0.9314027058794109
        verified: true
      - name: loss
        type: loss
        value: 0.4366346299648285
        verified: true
  ---
  
  <!-- 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-ner-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: 0.0355
  - Precision: 0.9438
  - Recall: 0.9525
  - F1: 0.9482
  - Accuracy: 0.9911
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  