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Add evaluation results on the default config of emotion
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: bert-finetuned-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.937
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.93
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.8939874310281785
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.93
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9310544672210583
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.8930616486578864
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.93
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.93
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.8927862771696669
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.93
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.930070287337576
            verified: true
          - name: loss
            type: loss
            value: 0.19910235702991486
            verified: true

bert-finetuned-emotion

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

  • Loss: 0.1582
  • Accuracy: 0.937

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: 10
  • eval_batch_size: 10
  • 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
0.553 1.0 1600 0.2631 0.9255
0.161 2.0 3200 0.1582 0.937

Framework versions

  • Transformers 4.19.4
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1