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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-uncased-finetuned-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.9355
          - name: F1
            type: f1
            value:
              f1: 0.935388774713548

bert-base-uncased-finetuned-emotion

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

  • Loss: 0.1651
  • Accuracy: {'accuracy': 0.9355}
  • F1: {'f1': 0.935388774713548}

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 Accuracy F1
0.2519 1.0 1000 0.1878 {'accuracy': 0.9325} {'f1': 0.9323540471733189}
0.1434 2.0 2000 0.1799 {'accuracy': 0.9335} {'f1': 0.9341179573678701}
0.0907 3.0 3000 0.1651 {'accuracy': 0.9355} {'f1': 0.935388774713548}

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3