README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          args: default
        metrics:
          - type: accuracy
            value: 0.918
            name: Accuracy
          - type: f1
            value: 0.9182094401352938
            name: F1
      - 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: 0.8948630809230339
            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: 0.9190547804558933
            name: Precision Weighted
            verified: true
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          - type: recall
            value: 0.860108882009274
            name: Recall Macro
            verified: true
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          - type: recall
            value: 0.9185
            name: Recall Micro
            verified: true
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          - type: recall
            value: 0.9185
            name: Recall Weighted
            verified: true
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          - type: f1
            value: 0.8727941247828231
            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: 0.9177368694234422
            name: F1 Weighted
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          - type: loss
            value: 0.21989187598228455
            name: loss
            verified: true
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distilbert-base-uncased-finetuned-emotion

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: 0.2287
• Accuracy: 0.918
• F1: 0.9182

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8478	1.0	250	0.3294	0.9015	0.8980
0.2616	2.0	500	0.2287	0.918	0.9182

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 1.18.4
• Tokenizers 0.11.6