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Bert-NER

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Bert-NER / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_awesome_wnut_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ner
          type: ner
          config: indian_names
          split: test
          args: indian_names
        metrics:
          - name: Precision
            type: precision
            value: 1
          - name: Recall
            type: recall
            value: 1
          - name: F1
            type: f1
            value: 1
          - name: Accuracy
            type: accuracy
            value: 1

my_awesome_wnut_model

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

  • Loss: 0.0000
  • Precision: 1.0
  • Recall: 1.0
  • F1: 1.0
  • Accuracy: 1.0

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 344 0.0000 1.0000 1.0000 1.0000 1.0000
0.0027 2.0 688 0.0000 1.0 1.0 1.0 1.0
0.0019 3.0 1032 0.0001 1.0 1.0 1.0 1.0
0.0019 4.0 1376 0.0000 1.0 1.0 1.0 1.0
0.0021 5.0 1720 0.0000 1.0 1.0 1.0 1.0
0.0016 6.0 2064 0.0000 1.0 1.0 1.0 1.0
0.0016 7.0 2408 0.0000 1.0 1.0 1.0 1.0
0.0007 8.0 2752 0.0000 1.0 1.0 1.0 1.0
0.001 9.0 3096 0.0000 1.0 1.0 1.0 1.0
0.001 10.0 3440 0.0000 1.0 1.0 1.0 1.0
0.001 11.0 3784 0.0001 1.0000 1.0000 1.0000 1.0000
0.0008 12.0 4128 0.0000 1.0 1.0 1.0 1.0
0.0008 13.0 4472 0.0000 1.0 1.0 1.0 1.0
0.0007 14.0 4816 0.0000 1.0 1.0 1.0 1.0
0.0009 15.0 5160 0.0000 1.0 1.0 1.0 1.0
0.0006 16.0 5504 0.0000 1.0 1.0 1.0 1.0
0.0006 17.0 5848 0.0000 1.0 1.0 1.0 1.0
0.0003 18.0 6192 0.0000 1.0 1.0 1.0 1.0
0.0006 19.0 6536 0.0000 1.0 1.0 1.0 1.0
0.0006 20.0 6880 0.0000 1.0 1.0 1.0 1.0
0.0007 21.0 7224 0.0000 1.0 1.0 1.0 1.0
0.0007 22.0 7568 0.0000 1.0 1.0 1.0 1.0
0.0007 23.0 7912 0.0000 1.0 1.0 1.0 1.0
0.0005 24.0 8256 0.0000 1.0 1.0 1.0 1.0
0.0001 25.0 8600 0.0000 1.0 1.0 1.0 1.0
0.0001 26.0 8944 0.0000 1.0 1.0 1.0 1.0
0.0002 27.0 9288 0.0000 1.0 1.0 1.0 1.0
0.0003 28.0 9632 0.0000 1.0 1.0 1.0 1.0
0.0003 29.0 9976 0.0000 1.0 1.0 1.0 1.0
0.0001 30.0 10320 0.0000 1.0 1.0 1.0 1.0
0.0 31.0 10664 0.0000 1.0 1.0 1.0 1.0
0.0001 32.0 11008 0.0000 1.0 1.0 1.0 1.0
0.0001 33.0 11352 0.0000 1.0 1.0 1.0 1.0
0.0001 34.0 11696 0.0000 1.0 1.0 1.0 1.0
0.0003 35.0 12040 0.0000 1.0 1.0 1.0 1.0
0.0003 36.0 12384 0.0000 1.0 1.0 1.0 1.0
0.0001 37.0 12728 0.0000 1.0 1.0 1.0 1.0
0.0001 38.0 13072 0.0000 1.0 1.0 1.0 1.0
0.0001 39.0 13416 0.0000 1.0 1.0 1.0 1.0
0.0002 40.0 13760 0.0000 1.0 1.0 1.0 1.0
0.0 41.0 14104 0.0000 1.0 1.0 1.0 1.0
0.0 42.0 14448 0.0000 1.0 1.0 1.0 1.0
0.0 43.0 14792 0.0000 1.0 1.0 1.0 1.0
0.0 44.0 15136 0.0000 1.0 1.0 1.0 1.0
0.0 45.0 15480 0.0000 1.0 1.0 1.0 1.0
0.0 46.0 15824 0.0000 1.0 1.0 1.0 1.0
0.0001 47.0 16168 0.0000 1.0 1.0 1.0 1.0
0.0 48.0 16512 0.0000 1.0 1.0 1.0 1.0
0.0 49.0 16856 0.0000 1.0 1.0 1.0 1.0
0.0 50.0 17200 0.0000 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3