readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-large-uncased_ner_wikiann
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8383588049015558
      - name: Recall
        type: recall
        value: 0.8608794005372543
      - name: F1
        type: f1
        value: 0.8494698660714285
      - name: Accuracy
        type: accuracy
        value: 0.9379407966623622
  ---
  
  <!-- 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-large-uncased_ner_wikiann
  
  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3373
  - Precision: 0.8384
  - Recall: 0.8609
  - F1: 0.8495
  - Accuracy: 0.9379
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3146        | 1.0   | 1250 | 0.2545          | 0.7956    | 0.8372 | 0.8159 | 0.9285   |
  | 0.1973        | 2.0   | 2500 | 0.2438          | 0.8267    | 0.8546 | 0.8404 | 0.9349   |
  | 0.1181        | 3.0   | 3750 | 0.2637          | 0.8320    | 0.8588 | 0.8452 | 0.9374   |
  | 0.0647        | 4.0   | 5000 | 0.3175          | 0.8389    | 0.8627 | 0.8507 | 0.9387   |
  | 0.0443        | 5.0   | 6250 | 0.3373          | 0.8384    | 0.8609 | 0.8495 | 0.9379   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  