entity-extraction / README.md
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Add evaluation results on the autoevaluate--conll2003-sample config and test split of autoevaluate/conll2003-sample
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
  - autoevaluate/conll2003-sample
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: entity-extraction
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.8862817854414493
            name: Precision
          - type: recall
            value: 0.9084908826490659
            name: Recall
          - type: f1
            value: 0.8972489227709645
            name: F1
          - type: accuracy
            value: 0.9774889986814304
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: autoevaluate/conll2003-sample
          type: autoevaluate/conll2003-sample
          config: autoevaluate--conll2003-sample
          split: test
        metrics:
          - type: accuracy
            value: 0.9680247550283652
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9708377518557795
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9754928076718167
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9731597129949509
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.1187286302447319
            name: loss
            verified: true
            verifyToken: >-
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entity-extraction

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

  • Loss: 0.0808
  • Precision: 0.8863
  • Recall: 0.9085
  • F1: 0.8972
  • Accuracy: 0.9775

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: 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: 1

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2552 1.0 878 0.0808 0.8863 0.9085 0.8972 0.9775

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1