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bert-uncased-massive-intent-classification
This model is a fine-tuned version of bert-base-uncased on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.8396
- Accuracy: 0.8854
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4984 | 1.0 | 720 | 0.6402 | 0.8495 |
| 0.4376 | 2.0 | 1440 | 0.5394 | 0.8731 |
| 0.2318 | 3.0 | 2160 | 0.5903 | 0.8760 |
| 0.1414 | 4.0 | 2880 | 0.6221 | 0.8805 |
| 0.087 | 5.0 | 3600 | 0.7072 | 0.8819 |
| 0.0622 | 6.0 | 4320 | 0.7121 | 0.8819 |
| 0.036 | 7.0 | 5040 | 0.7750 | 0.8805 |
| 0.0234 | 8.0 | 5760 | 0.7767 | 0.8834 |
| 0.0157 | 9.0 | 6480 | 0.8243 | 0.8805 |
| 0.0122 | 10.0 | 7200 | 0.8198 | 0.8839 |
| 0.0092 | 11.0 | 7920 | 0.8105 | 0.8849 |
| 0.0047 | 12.0 | 8640 | 0.8561 | 0.8844 |
| 0.0038 | 13.0 | 9360 | 0.8367 | 0.8815 |
| 0.0029 | 14.0 | 10080 | 0.8396 | 0.8854 |
| 0.0014 | 15.0 | 10800 | 0.8410 | 0.8849 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
