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distilbert-base-uncased-fine-tuned-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: 0.2156
- Accuracy: 0.9255
- F1: 0.9254
- Recall: 0.9255
Model description
This is the resuls of fine-tuning a distilbert-base-uncased trained on a NVIDIA GeForce GTX 1650, using a WSL with 7 gb of ram on windows 11.
The fine-tuning was obtained by following the book Natural Language Processing with Tranformers: Building Languaje Applications with Hugging Fabe, By Lewis Tunstall, Leandro von Werra & Thomas Wolf
Labels are associated to:
- LABEL_0 is sadness
- LABEL_1 is joy
- LABEL_2 is love
- LABEL_3 is anger
- LABEL_4 is fear
- LABEL_5 is surprise
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 | Recall |
|---|---|---|---|---|---|---|
| 0.7838 | 1.0 | 250 | 0.2995 | 0.906 | 0.9039 | 0.906 |
| 0.237 | 2.0 | 500 | 0.2156 | 0.9255 | 0.9254 | 0.9255 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.12.1
