README.md

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:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.9354625186165811
            name: Precision
          - type: recall
            value: 0.9513631773813531
            name: Recall
          - type: f1
            value: 0.943345848977889
            name: F1
          - type: accuracy
            value: 0.9867545770294931
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - type: accuracy
            value: 0.9003797607979704
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9286807108391197
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9158238551580065
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9222074745602832
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.8705922365188599
            name: loss
            verified: true
            verifyToken: >-
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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