readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: bert-base-cased-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9860628716077
  ---
  
  <!-- 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
  
  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: 0.0620
  - Precision: 0.9406
  - Recall: 0.9463
  - F1: 0.9434
  - Accuracy: 0.9861
  
  ## 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: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.5855        | 1.0   | 878  | 0.0848          | 0.8965    | 0.8980 | 0.8973 | 0.9760   |
  | 0.058         | 2.0   | 1756 | 0.0607          | 0.9357    | 0.9379 | 0.9368 | 0.9840   |
  | 0.0282        | 3.0   | 2634 | 0.0604          | 0.9354    | 0.9420 | 0.9387 | 0.9852   |
  | 0.0148        | 4.0   | 3512 | 0.0606          | 0.9386    | 0.9485 | 0.9435 | 0.9861   |
  | 0.0101        | 5.0   | 4390 | 0.0620          | 0.9406    | 0.9463 | 0.9434 | 0.9861   |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.8.0
  - Tokenizers 0.10.3
  