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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: train[:2000]
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.895
          - name: F1
            type: f1
            value: 0.8961058726378275

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: 0.7264
  • Accuracy: 0.895
  • Balanced accuracy: 0.8746
  • F1: 0.8961

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: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy Balanced accuracy F1
0.001 1.0 25 0.7713 0.89 0.8807 0.8915
0.0069 2.0 50 0.7734 0.905 0.8906 0.9070
0.0019 3.0 75 0.8670 0.88 0.8749 0.8819
0.0012 4.0 100 0.7387 0.895 0.8806 0.8953
0.0002 5.0 125 0.7841 0.885 0.8649 0.8858
0.0002 6.0 150 0.7415 0.9 0.8753 0.9001
0.0002 7.0 175 0.7378 0.895 0.8719 0.8955
0.0002 8.0 200 0.7452 0.89 0.8711 0.8910
0.0002 9.0 225 0.7555 0.89 0.8787 0.8908
0.0001 10.0 250 0.7541 0.895 0.8822 0.8959
0.0001 11.0 275 0.7536 0.9 0.8857 0.9009
0.0001 12.0 300 0.7530 0.9 0.8857 0.9009
0.0001 13.0 325 0.7542 0.9 0.8857 0.9009
0.0001 14.0 350 0.7532 0.895 0.8746 0.8957
0.0002 15.0 375 0.8554 0.88 0.8424 0.8803
0.0001 16.0 400 0.7700 0.9 0.8867 0.9011
0.0001 17.0 425 0.7302 0.895 0.8746 0.8961
0.0001 18.0 450 0.7304 0.895 0.8746 0.8961
0.0001 19.0 475 0.7284 0.895 0.8746 0.8961
0.0001 20.0 500 0.7264 0.895 0.8746 0.8961

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2