readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conllpp
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-large-ner-conllpp-v1
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conllpp
        type: conllpp
        config: conllpp
        split: train
        args: conllpp
      metrics:
      - name: Precision
        type: precision
        value: 0.9581069237510955
      - name: Recall
        type: recall
        value: 0.9586110136794107
      - name: F1
        type: f1
        value: 0.9583589024283334
      - name: Accuracy
        type: accuracy
        value: 0.9629450230079922
  ---
  
  <!-- 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-conllpp-v1
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset.
  It achieves the following results on the evaluation set:
  - Loss: nan
  - Precision: 0.9581
  - Recall: 0.9586
  - F1: 0.9584
  - Accuracy: 0.9629
  
  ## 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: 1e-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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3602        | 1.0   | 878  | nan             | 0.9450    | 0.9484 | 0.9467 | 0.9541   |
  | 0.1101        | 2.0   | 1756 | nan             | 0.9547    | 0.9569 | 0.9558 | 0.9620   |
  | 0.053         | 3.0   | 2634 | nan             | 0.9537    | 0.9572 | 0.9554 | 0.9614   |
  | 0.0331        | 4.0   | 3512 | nan             | 0.9560    | 0.9567 | 0.9563 | 0.9614   |
  | 0.0219        | 5.0   | 4390 | nan             | 0.9581    | 0.9586 | 0.9584 | 0.9629   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  