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multi-class-classification

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multi-class-classification / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: autoevaluate/multi-class-classification
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9185
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8738350796775306
            name: Precision Macro
            verified: true
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          - type: precision
            value: 0.9185
            name: Precision Micro
            verified: true
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          - type: precision
            value: 0.9179425177997311
            name: Precision Weighted
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.8650962919021573
            name: Recall Macro
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9185
            name: Recall Micro
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9185
            name: Recall Weighted
            verified: true
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          - type: f1
            value: 0.8692821860210945
            name: F1 Macro
            verified: true
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          - type: f1
            value: 0.9185
            name: F1 Micro
            verified: true
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          - type: f1
            value: 0.9181177508591364
            name: F1 Weighted
            verified: true
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          - type: loss
            value: 0.20907790958881378
            name: loss
            verified: true
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multi-class-classification

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

  • Loss: 0.2009
  • Accuracy: 0.928

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 Accuracy
0.2643 1.0 1000 0.2009 0.928

Framework versions

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