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Bert-NER

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Bert-NER / README.md
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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - indian_names
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_awesome_wnut_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: indian_names
          type: indian_names
          config: indian_names
          split: train
          args: indian_names
        metrics:
          - name: Precision
            type: precision
            value: 0.9941348973607038
          - name: Recall
            type: recall
            value: 0.9921951219512195
          - name: F1
            type: f1
            value: 0.9931640625
          - name: Accuracy
            type: accuracy
            value: 0.9998052125131481

my_awesome_wnut_model

This model is a fine-tuned version of distilbert-base-uncased on the indian_names dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0012
  • Precision: 0.9941
  • Recall: 0.9922
  • F1: 0.9932
  • Accuracy: 0.9998

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 63 0.1413 0.0 0.0 0.0 0.9745
No log 2.0 126 0.1211 0.0 0.0 0.0 0.9745
No log 3.0 189 0.0656 0.6231 0.2790 0.3854 0.9811
No log 4.0 252 0.0380 0.7297 0.6059 0.6620 0.9894
No log 5.0 315 0.0259 0.8341 0.7259 0.7762 0.9931
No log 6.0 378 0.0136 0.8842 0.8712 0.8776 0.9963
No log 7.0 441 0.0076 0.9286 0.9268 0.9277 0.9981
0.0748 8.0 504 0.0054 0.9409 0.9473 0.9441 0.9985
0.0748 9.0 567 0.0042 0.9520 0.9678 0.9598 0.9991
0.0748 10.0 630 0.0025 0.9738 0.9795 0.9767 0.9995
0.0748 11.0 693 0.0019 0.9863 0.9863 0.9863 0.9997
0.0748 12.0 756 0.0015 0.9961 0.9912 0.9936 0.9998
0.0748 13.0 819 0.0014 0.9912 0.9912 0.9912 0.9998
0.0748 14.0 882 0.0013 0.9912 0.9912 0.9912 0.9998
0.0748 15.0 945 0.0012 0.9941 0.9922 0.9932 0.9998

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3