readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-tiny-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.8083060109289617
      - name: Recall
        type: recall
        value: 0.8273856136033113
      - name: F1
        type: f1
        value: 0.8177345348001547
      - name: Accuracy
        type: accuracy
        value: 0.9597597979252387
  ---
  
  <!-- 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-tiny-finetuned-ner
  
  This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1689
  - Precision: 0.8083
  - Recall: 0.8274
  - F1: 0.8177
  - Accuracy: 0.9598
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0355        | 1.0   | 878  | 0.1692          | 0.8072    | 0.8248 | 0.8159 | 0.9594   |
  | 0.0411        | 2.0   | 1756 | 0.1678          | 0.8101    | 0.8277 | 0.8188 | 0.9600   |
  | 0.0386        | 3.0   | 2634 | 0.1697          | 0.8103    | 0.8269 | 0.8186 | 0.9599   |
  | 0.0373        | 4.0   | 3512 | 0.1694          | 0.8106    | 0.8263 | 0.8183 | 0.9600   |
  | 0.0383        | 5.0   | 4390 | 0.1689          | 0.8083    | 0.8274 | 0.8177 | 0.9598   |
  
  
  ### Framework versions
  
  - Transformers 4.10.0
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  