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: 0.1538
- Accuracy: 0.936
- F1: 0.9361
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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
No log | 1.0 | 63 | 0.8113 | 0.745 | 0.6916 |
No log | 2.0 | 126 | 0.2909 | 0.911 | 0.9107 |
No log | 3.0 | 189 | 0.2003 | 0.928 | 0.9277 |
0.5629 | 4.0 | 252 | 0.1698 | 0.938 | 0.9376 |
0.5629 | 5.0 | 315 | 0.1561 | 0.9365 | 0.9364 |
0.5629 | 6.0 | 378 | 0.1531 | 0.933 | 0.9334 |
0.5629 | 7.0 | 441 | 0.1584 | 0.9355 | 0.9345 |
0.1065 | 8.0 | 504 | 0.1493 | 0.9325 | 0.9321 |
0.1065 | 9.0 | 567 | 0.1504 | 0.936 | 0.9364 |
0.1065 | 10.0 | 630 | 0.1481 | 0.9395 | 0.9395 |
0.1065 | 11.0 | 693 | 0.1501 | 0.935 | 0.9353 |
0.0684 | 12.0 | 756 | 0.1504 | 0.936 | 0.9360 |
0.0684 | 13.0 | 819 | 0.1526 | 0.935 | 0.9352 |
0.0684 | 14.0 | 882 | 0.1526 | 0.9355 | 0.9357 |
0.0684 | 15.0 | 945 | 0.1538 | 0.936 | 0.9361 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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Finetuned from
Dataset used to train LeoTungAnh/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.936
- F1 on emotionvalidation set self-reported0.936