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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: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9405
          - name: F1
            type: f1
            value: 0.9405428930790032

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: 0.2602
  • Accuracy: 0.9405
  • F1: 0.9405

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: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.04 1.0 125 0.2096 0.9385 0.9386
0.041 2.0 250 0.2138 0.9395 0.9396
0.0323 3.0 375 0.2165 0.94 0.9401
0.024 4.0 500 0.2315 0.941 0.9412
0.0229 5.0 625 0.2263 0.9375 0.9374
0.0179 6.0 750 0.2561 0.9415 0.9418
0.0149 7.0 875 0.2518 0.943 0.9433
0.0144 8.0 1000 0.2574 0.941 0.9409
0.011 9.0 1125 0.2598 0.943 0.9430
0.009 10.0 1250 0.2602 0.9405 0.9405

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1
  • Datasets 2.14.5
  • Tokenizers 0.14.1