readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - article250v0_wikigold_split
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Article_250v0_NER_Model_3Epochs_AUGMENTED
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: article250v0_wikigold_split
        type: article250v0_wikigold_split
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.6609491137793024
      - name: Recall
        type: recall
        value: 0.6238532110091743
      - name: F1
        type: f1
        value: 0.6418656302054414
      - name: Accuracy
        type: accuracy
        value: 0.9219402505621587
  ---
  
  <!-- 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. -->
  
  # Article_250v0_NER_Model_3Epochs_AUGMENTED
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v0_wikigold_split dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2589
  - Precision: 0.6609
  - Recall: 0.6239
  - F1: 0.6419
  - Accuracy: 0.9219
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 112  | 0.2475          | 0.5938    | 0.5559 | 0.5742 | 0.9180   |
  | No log        | 2.0   | 224  | 0.2340          | 0.6483    | 0.6411 | 0.6447 | 0.9247   |
  | No log        | 3.0   | 336  | 0.2589          | 0.6609    | 0.6239 | 0.6419 | 0.9219   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.11.6
  