readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-to-distilbert-NER
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.014488935721812434
      - name: Recall
        type: recall
        value: 0.018512285425782565
      - name: F1
        type: f1
        value: 0.016255356878971478
      - name: Accuracy
        type: accuracy
        value: 0.7597280273150055
  ---
  
  <!-- 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-to-distilbert-NER
  
  This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 44.0386
  - Precision: 0.0145
  - Recall: 0.0185
  - F1: 0.0163
  - Accuracy: 0.7597
  
  ## 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: 6e-05
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 201.4012      | 1.0   | 110  | 133.7231        | 0.0153    | 0.0106 | 0.0125 | 0.7539   |
  | 106.9317      | 2.0   | 220  | 99.3629         | 0.0266    | 0.0305 | 0.0284 | 0.7593   |
  | 81.3601       | 3.0   | 330  | 80.3763         | 0.0159    | 0.0214 | 0.0183 | 0.7604   |
  | 63.8325       | 4.0   | 440  | 67.7620         | 0.0179    | 0.0244 | 0.0207 | 0.7599   |
  | 52.0271       | 5.0   | 550  | 59.0806         | 0.0203    | 0.0268 | 0.0231 | 0.7598   |
  | 44.4419       | 6.0   | 660  | 55.3208         | 0.0211    | 0.0278 | 0.0240 | 0.7603   |
  | 39.2351       | 7.0   | 770  | 52.4510         | 0.0170    | 0.0222 | 0.0193 | 0.7598   |
  | 35.3438       | 8.0   | 880  | 50.4576         | 0.0205    | 0.0268 | 0.0232 | 0.7604   |
  | 32.7385       | 9.0   | 990  | 48.3418         | 0.0173    | 0.0227 | 0.0197 | 0.7595   |
  | 30.6531       | 10.0  | 1100 | 46.7304         | 0.0147    | 0.0188 | 0.0165 | 0.7600   |
  | 29.0811       | 11.0  | 1210 | 46.3386         | 0.0151    | 0.0190 | 0.0168 | 0.7599   |
  | 27.9501       | 12.0  | 1320 | 45.4516         | 0.0163    | 0.0204 | 0.0181 | 0.7604   |
  | 26.7452       | 13.0  | 1430 | 44.3425         | 0.0154    | 0.0199 | 0.0173 | 0.7592   |
  | 25.5367       | 14.0  | 1540 | 44.0415         | 0.0146    | 0.0190 | 0.0165 | 0.7594   |
  | 24.5507       | 15.0  | 1650 | 44.0386         | 0.0145    | 0.0185 | 0.0163 | 0.7597   |
  
  
  ### Framework versions
  
  - Transformers 4.19.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  