readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-large_ner_conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9622389306599833
      - name: Recall
        type: recall
        value: 0.9692022887916526
      - name: F1
        type: f1
        value: 0.9657080573488722
      - name: Accuracy
        type: accuracy
        value: 0.9939449398387913
  ---
  
  <!-- 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. -->
  
  # roberta-large_ner_conll2003
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0345
  - Precision: 0.9622
  - Recall: 0.9692
  - F1: 0.9657
  - Accuracy: 0.9939
  
  ## 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: cosine
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1227        | 1.0   | 878  | 0.0431          | 0.9511    | 0.9559 | 0.9535 | 0.9914   |
  | 0.0295        | 2.0   | 1756 | 0.0334          | 0.9541    | 0.9657 | 0.9599 | 0.9930   |
  | 0.0163        | 3.0   | 2634 | 0.0327          | 0.9616    | 0.9682 | 0.9649 | 0.9938   |
  | 0.0073        | 4.0   | 3512 | 0.0342          | 0.9624    | 0.9692 | 0.9658 | 0.9939   |
  | 0.0042        | 5.0   | 4390 | 0.0345          | 0.9622    | 0.9692 | 0.9657 | 0.9939   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  