readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - ontonotes5
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner-ontonotes
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ontonotes5
        type: ontonotes5
        config: ontonotes5
        split: train
        args: ontonotes5
      metrics:
      - name: Precision
        type: precision
        value: 0.8567258883248731
      - name: Recall
        type: recall
        value: 0.8841595180407308
      - name: F1
        type: f1
        value: 0.8702265476459025
      - name: Accuracy
        type: accuracy
        value: 0.9754933764288157
  ---
  
  <!-- 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-ontonotes
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ontonotes5 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1503
  - Precision: 0.8567
  - Recall: 0.8842
  - F1: 0.8702
  - Accuracy: 0.9755
  
  ## 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: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0842        | 1.0   | 7491  | 0.0950          | 0.8524    | 0.8715 | 0.8618 | 0.9745   |
  | 0.0523        | 2.0   | 14982 | 0.1044          | 0.8449    | 0.8827 | 0.8634 | 0.9744   |
  | 0.036         | 3.0   | 22473 | 0.1118          | 0.8529    | 0.8843 | 0.8683 | 0.9760   |
  | 0.0231        | 4.0   | 29964 | 0.1240          | 0.8589    | 0.8805 | 0.8696 | 0.9752   |
  | 0.0118        | 5.0   | 37455 | 0.1416          | 0.8570    | 0.8804 | 0.8685 | 0.9753   |
  | 0.0077        | 6.0   | 44946 | 0.1503          | 0.8567    | 0.8842 | 0.8702 | 0.9755   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  