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
        config: conll2003
        split: train
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.014729299363057325
      - name: Recall
        type: recall
        value: 0.018680578929653316
      - name: F1
        type: f1
        value: 0.016471286541029827
      - name: Accuracy
        type: accuracy
        value: 0.7599340672278802
  ---
  
  <!-- 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: 43.2398
  - Precision: 0.0147
  - Recall: 0.0187
  - F1: 0.0165
  - Accuracy: 0.7599
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 190.2685      | 1.0   | 110  | 127.2351        | 0.0157    | 0.0098 | 0.0120 | 0.7569   |
  | 105.4389      | 2.0   | 220  | 97.1100         | 0.0281    | 0.0298 | 0.0289 | 0.7587   |
  | 77.0337       | 3.0   | 330  | 76.9433         | 0.0136    | 0.0173 | 0.0152 | 0.7615   |
  | 60.3477       | 4.0   | 440  | 65.9181         | 0.0130    | 0.0158 | 0.0143 | 0.7603   |
  | 50.4086       | 5.0   | 550  | 58.5255         | 0.0170    | 0.0220 | 0.0192 | 0.7603   |
  | 43.298        | 6.0   | 660  | 54.5405         | 0.0144    | 0.0187 | 0.0163 | 0.7594   |
  | 39.0911       | 7.0   | 770  | 52.4767         | 0.0155    | 0.0195 | 0.0172 | 0.7613   |
  | 35.07         | 8.0   | 880  | 49.1975         | 0.0170    | 0.0219 | 0.0192 | 0.7602   |
  | 32.215        | 9.0   | 990  | 47.4422         | 0.0144    | 0.0187 | 0.0163 | 0.7599   |
  | 29.9923       | 10.0  | 1100 | 46.5558         | 0.0167    | 0.0212 | 0.0187 | 0.7606   |
  | 28.3599       | 11.0  | 1210 | 45.6301         | 0.0171    | 0.0214 | 0.0190 | 0.7613   |
  | 26.8163       | 12.0  | 1320 | 45.0483         | 0.0141    | 0.0177 | 0.0157 | 0.7606   |
  | 25.7434       | 13.0  | 1430 | 44.0639         | 0.0176    | 0.0222 | 0.0196 | 0.7605   |
  | 24.9853       | 14.0  | 1540 | 43.6618         | 0.0148    | 0.0187 | 0.0165 | 0.7606   |
  | 24.3179       | 15.0  | 1650 | 43.2398         | 0.0147    | 0.0187 | 0.0165 | 0.7599   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  