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
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Dataset used to train lortigas/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionself-reported0.927
- F1 on emotionself-reported0.927