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tiny_bert_29_mva_intents
This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3517
- Accuracy: 0.9211
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 406 | 2.9779 | 0.3421 |
| 3.1383 | 2.0 | 812 | 2.6427 | 0.4474 |
| 2.743 | 3.0 | 1218 | 2.3476 | 0.5439 |
| 2.3913 | 4.0 | 1624 | 2.0961 | 0.6082 |
| 2.0764 | 5.0 | 2030 | 1.8721 | 0.6550 |
| 2.0764 | 6.0 | 2436 | 1.6858 | 0.6696 |
| 1.8321 | 7.0 | 2842 | 1.5348 | 0.6959 |
| 1.6047 | 8.0 | 3248 | 1.3980 | 0.7281 |
| 1.439 | 9.0 | 3654 | 1.2846 | 0.7544 |
| 1.2773 | 10.0 | 4060 | 1.1894 | 0.7632 |
| 1.2773 | 11.0 | 4466 | 1.0994 | 0.7661 |
| 1.1596 | 12.0 | 4872 | 1.0221 | 0.7982 |
| 1.0563 | 13.0 | 5278 | 0.9486 | 0.8158 |
| 0.962 | 14.0 | 5684 | 0.8931 | 0.8275 |
| 0.8814 | 15.0 | 6090 | 0.8328 | 0.8363 |
| 0.8814 | 16.0 | 6496 | 0.7830 | 0.8392 |
| 0.8114 | 17.0 | 6902 | 0.7394 | 0.8596 |
| 0.7515 | 18.0 | 7308 | 0.6997 | 0.8684 |
| 0.6936 | 19.0 | 7714 | 0.6659 | 0.8860 |
| 0.6491 | 20.0 | 8120 | 0.6316 | 0.8801 |
| 0.614 | 21.0 | 8526 | 0.6014 | 0.8860 |
| 0.614 | 22.0 | 8932 | 0.5799 | 0.8947 |
| 0.5693 | 23.0 | 9338 | 0.5529 | 0.8918 |
| 0.5362 | 24.0 | 9744 | 0.5368 | 0.9035 |
| 0.5026 | 25.0 | 10150 | 0.5137 | 0.9006 |
| 0.4733 | 26.0 | 10556 | 0.4944 | 0.9035 |
| 0.4733 | 27.0 | 10962 | 0.4807 | 0.9006 |
| 0.4582 | 28.0 | 11368 | 0.4646 | 0.9152 |
| 0.4276 | 29.0 | 11774 | 0.4536 | 0.9064 |
| 0.4047 | 30.0 | 12180 | 0.4377 | 0.9123 |
| 0.3941 | 31.0 | 12586 | 0.4286 | 0.9094 |
| 0.3941 | 32.0 | 12992 | 0.4188 | 0.9123 |
| 0.3741 | 33.0 | 13398 | 0.4076 | 0.9123 |
| 0.3613 | 34.0 | 13804 | 0.4024 | 0.9094 |
| 0.3483 | 35.0 | 14210 | 0.3961 | 0.9094 |
| 0.3437 | 36.0 | 14616 | 0.3918 | 0.9094 |
| 0.3266 | 37.0 | 15022 | 0.3830 | 0.9094 |
| 0.3266 | 38.0 | 15428 | 0.3770 | 0.9152 |
| 0.3216 | 39.0 | 15834 | 0.3746 | 0.9123 |
| 0.3042 | 40.0 | 16240 | 0.3704 | 0.9211 |
| 0.3052 | 41.0 | 16646 | 0.3660 | 0.9211 |
| 0.2962 | 42.0 | 17052 | 0.3655 | 0.9181 |
| 0.2962 | 43.0 | 17458 | 0.3604 | 0.9123 |
| 0.2912 | 44.0 | 17864 | 0.3584 | 0.9152 |
| 0.2909 | 45.0 | 18270 | 0.3557 | 0.9152 |
| 0.2854 | 46.0 | 18676 | 0.3546 | 0.9181 |
| 0.2829 | 47.0 | 19082 | 0.3548 | 0.9152 |
| 0.2829 | 48.0 | 19488 | 0.3530 | 0.9181 |
| 0.278 | 49.0 | 19894 | 0.3519 | 0.9211 |
| 0.2827 | 50.0 | 20300 | 0.3517 | 0.9211 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
