README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.9227969559942649
            name: Precision
          - type: recall
            value: 0.9360107394563151
            name: Recall
          - type: f1
            value: 0.9293568810396535
            name: F1
          - type: accuracy
            value: 0.9833034139831922
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - type: accuracy
            value: 0.973914094330502
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9791360147483736
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9793269742207723
            name: Recall
            verified: true
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          - type: f1
            value: 0.9792314851748437
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.10710480064153671
            name: loss
            verified: true
            verifyToken: >-
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distilbert-base-uncased-finetuned-ner

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: 0.0614
• Precision: 0.9228
• Recall: 0.9360
• F1: 0.9294
• Accuracy: 0.9833

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2433	1.0	878	0.0732	0.9079	0.9190	0.9134	0.9795
0.0553	2.0	1756	0.0599	0.9170	0.9333	0.9251	0.9826
0.0305	3.0	2634	0.0614	0.9228	0.9360	0.9294	0.9833

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6