bert-finetuned-ner / README.md
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lewtun
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Add evaluation results on the conll2003 config of conll2003 (#3)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test-bert-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9354625186165811
          - name: Recall
            type: recall
            value: 0.9513631773813531
          - name: F1
            type: f1
            value: 0.943345848977889
          - name: Accuracy
            type: accuracy
            value: 0.9867545770294931
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9003797607979704
            verified: true
          - name: Precision
            type: precision
            value: 0.9286807108391197
            verified: true
          - name: Recall
            type: recall
            value: 0.9158238551580065
            verified: true
          - name: F1
            type: f1
            value: 0.9222074745602832
            verified: true
          - name: loss
            type: loss
            value: 0.8705922365188599
            verified: true

test-bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0600
  • Precision: 0.9355
  • Recall: 0.9514
  • F1: 0.9433
  • Accuracy: 0.9868

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
0.0849 1.0 1756 0.0713 0.9144 0.9366 0.9253 0.9817
0.0359 2.0 3512 0.0658 0.9346 0.9500 0.9422 0.9860
0.0206 3.0 5268 0.0600 0.9355 0.9514 0.9433 0.9868

Framework versions

  • Transformers 4.11.0.dev0
  • Pytorch 1.8.1+cu111
  • Datasets 1.12.1.dev0
  • Tokenizers 0.10.3