readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: bert-large-uncased-whole-word-masking-ner-conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9886888970085945
  ---
  
  <!-- 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-large-uncased-whole-word-masking-ner-conll2003
  
  This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0592
  - Precision: 0.9527
  - Recall: 0.9569
  - F1: 0.9548
  - Accuracy: 0.9887
  
  ## 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: 4
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.4071        | 1.0   | 877  | 0.0584          | 0.9306    | 0.9418 | 0.9362 | 0.9851   |
  | 0.0482        | 2.0   | 1754 | 0.0594          | 0.9362    | 0.9491 | 0.9426 | 0.9863   |
  | 0.0217        | 3.0   | 2631 | 0.0550          | 0.9479    | 0.9584 | 0.9531 | 0.9885   |
  | 0.0103        | 4.0   | 3508 | 0.0592          | 0.9527    | 0.9569 | 0.9548 | 0.9887   |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.8.0
  - Tokenizers 0.10.3
  