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tiny_bert_29_medicare_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: 0.3859
- Accuracy: 0.9245
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 | 276 | 3.1352 | 0.2551 |
| 3.2002 | 2.0 | 552 | 2.9038 | 0.3837 |
| 3.2002 | 3.0 | 828 | 2.6758 | 0.5 |
| 2.8069 | 4.0 | 1104 | 2.4689 | 0.5510 |
| 2.8069 | 5.0 | 1380 | 2.2695 | 0.5796 |
| 2.4336 | 6.0 | 1656 | 2.0859 | 0.6265 |
| 2.4336 | 7.0 | 1932 | 1.9171 | 0.6449 |
| 2.1115 | 8.0 | 2208 | 1.7675 | 0.6592 |
| 2.1115 | 9.0 | 2484 | 1.6326 | 0.6898 |
| 1.8187 | 10.0 | 2760 | 1.5044 | 0.7224 |
| 1.5769 | 11.0 | 3036 | 1.3916 | 0.7469 |
| 1.5769 | 12.0 | 3312 | 1.2908 | 0.7694 |
| 1.3698 | 13.0 | 3588 | 1.2009 | 0.7898 |
| 1.3698 | 14.0 | 3864 | 1.1191 | 0.8122 |
| 1.1965 | 15.0 | 4140 | 1.0441 | 0.8224 |
| 1.1965 | 16.0 | 4416 | 0.9788 | 0.8367 |
| 1.0564 | 17.0 | 4692 | 0.9202 | 0.8510 |
| 1.0564 | 18.0 | 4968 | 0.8699 | 0.8612 |
| 0.9545 | 19.0 | 5244 | 0.8236 | 0.8653 |
| 0.8449 | 20.0 | 5520 | 0.7833 | 0.8673 |
| 0.8449 | 21.0 | 5796 | 0.7402 | 0.8837 |
| 0.7507 | 22.0 | 6072 | 0.7087 | 0.8816 |
| 0.7507 | 23.0 | 6348 | 0.6743 | 0.8837 |
| 0.6936 | 24.0 | 6624 | 0.6459 | 0.8980 |
| 0.6936 | 25.0 | 6900 | 0.6198 | 0.8980 |
| 0.6268 | 26.0 | 7176 | 0.5965 | 0.9 |
| 0.6268 | 27.0 | 7452 | 0.5739 | 0.9061 |
| 0.5721 | 28.0 | 7728 | 0.5510 | 0.9122 |
| 0.5298 | 29.0 | 8004 | 0.5345 | 0.9122 |
| 0.5298 | 30.0 | 8280 | 0.5183 | 0.9143 |
| 0.4958 | 31.0 | 8556 | 0.5027 | 0.9163 |
| 0.4958 | 32.0 | 8832 | 0.4892 | 0.9163 |
| 0.4595 | 33.0 | 9108 | 0.4766 | 0.9163 |
| 0.4595 | 34.0 | 9384 | 0.4649 | 0.9163 |
| 0.4268 | 35.0 | 9660 | 0.4527 | 0.9163 |
| 0.4268 | 36.0 | 9936 | 0.4444 | 0.9184 |
| 0.4085 | 37.0 | 10212 | 0.4353 | 0.9163 |
| 0.4085 | 38.0 | 10488 | 0.4271 | 0.9163 |
| 0.3855 | 39.0 | 10764 | 0.4208 | 0.9163 |
| 0.3684 | 40.0 | 11040 | 0.4152 | 0.9163 |
| 0.3684 | 41.0 | 11316 | 0.4098 | 0.9184 |
| 0.3591 | 42.0 | 11592 | 0.4035 | 0.9184 |
| 0.3591 | 43.0 | 11868 | 0.3993 | 0.9204 |
| 0.3416 | 44.0 | 12144 | 0.3950 | 0.9245 |
| 0.3416 | 45.0 | 12420 | 0.3919 | 0.9224 |
| 0.3377 | 46.0 | 12696 | 0.3897 | 0.9245 |
| 0.3377 | 47.0 | 12972 | 0.3887 | 0.9245 |
| 0.3364 | 48.0 | 13248 | 0.3871 | 0.9245 |
| 0.3282 | 49.0 | 13524 | 0.3862 | 0.9245 |
| 0.3282 | 50.0 | 13800 | 0.3859 | 0.9245 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
