readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: ner_conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9772880710440217
  ---
  
  <!-- 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. -->
  
  # ner_conll2003
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1495
  - Precision: 0.8985
  - Recall: 0.9130
  - F1: 0.9057
  - Accuracy: 0.9773
  
  ## 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: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.423         | 1.0   | 877  | 0.0656          | 0.9158    | 0.9268 | 0.9213 | 0.9818   |
  | 0.0575        | 2.0   | 1754 | 0.0574          | 0.9285    | 0.9445 | 0.9364 | 0.9847   |
  | 0.0295        | 3.0   | 2631 | 0.0631          | 0.9414    | 0.9456 | 0.9435 | 0.9859   |
  | 0.0155        | 4.0   | 3508 | 0.0680          | 0.9395    | 0.9467 | 0.9431 | 0.9860   |
  | 0.0097        | 5.0   | 4385 | 0.0694          | 0.9385    | 0.9513 | 0.9449 | 0.9863   |
  | 0.0059        | 6.0   | 5262 | 0.0743          | 0.9363    | 0.9471 | 0.9416 | 0.9860   |
  | 0.0041        | 7.0   | 6139 | 0.0803          | 0.9371    | 0.9518 | 0.9444 | 0.9862   |
  
  
  ### Framework versions
  
  - Transformers 4.9.1
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.2
  