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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: 0.927
            name: Accuracy
          - type: f1
            value: 0.9271664736493986
            name: F1

distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. The model is trained in Chapter 2: Text Classification in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

It achieves the following results on the evaluation set:

  • Loss: 0.2192
  • Accuracy: 0.927
  • F1: 0.9272

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
0.8569 1.0 250 0.3386 0.894 0.8888
0.2639 2.0 500 0.2192 0.927 0.9272

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.1+cu102
  • Datasets 1.13.0
  • Tokenizers 0.10.3