readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-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: 0.9309775429326288
      - name: Recall
        type: recall
        value: 0.9488387748232918
      - name: F1
        type: f1
        value: 0.9398233038839806
      - name: Accuracy
        type: accuracy
        value: 0.9861806087007712
  ---
  
  <!-- 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-finetuned-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: 0.0630
  - Precision: 0.9310
  - Recall: 0.9488
  - F1: 0.9398
  - Accuracy: 0.9862
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0911        | 1.0   | 1756 | 0.0702          | 0.9197    | 0.9345 | 0.9270 | 0.9826   |
  | 0.0336        | 2.0   | 3512 | 0.0623          | 0.9294    | 0.9480 | 0.9386 | 0.9864   |
  | 0.0174        | 3.0   | 5268 | 0.0630          | 0.9310    | 0.9488 | 0.9398 | 0.9862   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  