BERT-tiny-Massive-intent
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.6740
- Accuracy: 0.8475
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: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6104 | 1.0 | 720 | 3.0911 | 0.3601 |
| 2.8025 | 2.0 | 1440 | 2.3800 | 0.5165 |
| 2.2292 | 3.0 | 2160 | 1.9134 | 0.5991 |
| 1.818 | 4.0 | 2880 | 1.5810 | 0.6744 |
| 1.5171 | 5.0 | 3600 | 1.3522 | 0.7108 |
| 1.2876 | 6.0 | 4320 | 1.1686 | 0.7442 |
| 1.1049 | 7.0 | 5040 | 1.0355 | 0.7683 |
| 0.9623 | 8.0 | 5760 | 0.9466 | 0.7885 |
| 0.8424 | 9.0 | 6480 | 0.8718 | 0.7875 |
| 0.7473 | 10.0 | 7200 | 0.8107 | 0.8028 |
| 0.6735 | 11.0 | 7920 | 0.7710 | 0.8180 |
| 0.6085 | 12.0 | 8640 | 0.7404 | 0.8210 |
| 0.5536 | 13.0 | 9360 | 0.7180 | 0.8229 |
| 0.5026 | 14.0 | 10080 | 0.6980 | 0.8318 |
| 0.4652 | 15.0 | 10800 | 0.6970 | 0.8337 |
| 0.4234 | 16.0 | 11520 | 0.6822 | 0.8372 |
| 0.3987 | 17.0 | 12240 | 0.6691 | 0.8436 |
| 0.3707 | 18.0 | 12960 | 0.6679 | 0.8455 |
| 0.3433 | 19.0 | 13680 | 0.6740 | 0.8475 |
| 0.3206 | 20.0 | 14400 | 0.6760 | 0.8451 |
| 0.308 | 21.0 | 15120 | 0.6704 | 0.8436 |
| 0.2813 | 22.0 | 15840 | 0.6701 | 0.8416 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1