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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-large-uncased-en-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9094335178424214
          - name: Recall
            type: recall
            value: 0.9164490861618799
          - name: F1
            type: f1
            value: 0.9129278240822842
          - name: Accuracy
            type: accuracy
            value: 0.979076030660866
language:
  - en
library_name: transformers

bert-large-uncased-en-ner

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

  • Loss: 0.1402
  • Precision: 0.9094
  • Recall: 0.9164
  • F1: 0.9129
  • Accuracy: 0.9791

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

The model was trained on data that follows the IOB convention. Full tagset with indices:

{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 4e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 0
  • 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
0.0787 1.0 1756 0.1122 0.8932 0.9046 0.8989 0.9761
0.0406 2.0 3512 0.1312 0.9042 0.9124 0.9083 0.9777
0.0177 3.0 5268 0.1402 0.9094 0.9164 0.9129 0.9791

Framework versions

  • Transformers 4.27.2
  • Pytorch 2.0.0+cu117
  • Datasets 2.10.1
  • Tokenizers 0.13.2