README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: deberta-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.9577488309953239
            name: Precision
          - type: recall
            value: 0.9651632446987546
            name: Recall
          - type: f1
            value: 0.961441743503772
            name: F1
          - type: accuracy
            value: 0.9907182964622135
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - type: accuracy
            value: 0.9108823919384779
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9308372971460548
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9213792387183881
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9260841198729938
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.8661637306213379
            name: loss
            verified: true
            verifyToken: >-
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deberta-finetuned-ner

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

• Loss: 0.0515
• Precision: 0.9577
• Recall: 0.9652
• F1: 0.9614
• Accuracy: 0.9907

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0742	1.0	1756	0.0526	0.9390	0.9510	0.9450	0.9868
0.0374	2.0	3512	0.0528	0.9421	0.9554	0.9487	0.9879
0.0205	3.0	5268	0.0505	0.9505	0.9636	0.9570	0.9900
0.0089	4.0	7024	0.0528	0.9531	0.9636	0.9583	0.9898
0.0076	5.0	8780	0.0515	0.9577	0.9652	0.9614	0.9907

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1