readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.819622641509434
      - name: Recall
        type: recall
        value: 0.8444790046656299
      - name: F1
        type: f1
        value: 0.8318651857525853
      - name: Accuracy
        type: accuracy
        value: 0.9269227060339613
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: wikiann
        type: wikiann
        config: en
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8492771401033908
        verified: true
      - name: Precision
        type: precision
        value: 0.857294905524994
        verified: true
      - name: Recall
        type: recall
        value: 0.865900059186607
        verified: true
      - name: F1
        type: f1
        value: 0.8615759964905745
        verified: true
      - name: loss
        type: loss
        value: 1.054654836654663
        verified: true
  ---
  
  <!-- 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-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3217
  - Precision: 0.8196
  - Recall: 0.8445
  - F1: 0.8319
  - Accuracy: 0.9269
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2821        | 1.0   | 2500 | 0.2906          | 0.7983    | 0.8227 | 0.8103 | 0.9193   |
  | 0.2087        | 2.0   | 5000 | 0.2614          | 0.8030    | 0.8379 | 0.8201 | 0.9257   |
  | 0.1404        | 3.0   | 7500 | 0.3217          | 0.8196    | 0.8445 | 0.8319 | 0.9269   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  