readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-large_ner_wikiann
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8462551098177787
      - name: Recall
        type: recall
        value: 0.8634242895518167
      - name: F1
        type: f1
        value: 0.8547534903250638
      - name: Accuracy
        type: accuracy
        value: 0.9382388000397338
  ---
  
  <!-- 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_wikiann
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2783
  - Precision: 0.8463
  - Recall: 0.8634
  - F1: 0.8548
  - Accuracy: 0.9382
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3395        | 1.0   | 1250 | 0.2652          | 0.8039    | 0.8308 | 0.8171 | 0.9242   |
  | 0.2343        | 2.0   | 2500 | 0.2431          | 0.8354    | 0.8503 | 0.8428 | 0.9329   |
  | 0.1721        | 3.0   | 3750 | 0.2315          | 0.8330    | 0.8503 | 0.8416 | 0.9352   |
  | 0.1156        | 4.0   | 5000 | 0.2709          | 0.8477    | 0.8634 | 0.8554 | 0.9385   |
  | 0.1026        | 5.0   | 6250 | 0.2783          | 0.8463    | 0.8634 | 0.8548 | 0.9382   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  