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tiny_bert_31_erc_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: 0.3615
- Accuracy: 0.9485
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 | 273 | 3.2312 | 0.2247 |
| 3.2677 | 2.0 | 546 | 3.0029 | 0.4454 |
| 3.2677 | 3.0 | 819 | 2.7765 | 0.5196 |
| 2.8826 | 4.0 | 1092 | 2.5522 | 0.6 |
| 2.8826 | 5.0 | 1365 | 2.3412 | 0.6371 |
| 2.4879 | 6.0 | 1638 | 2.1518 | 0.6577 |
| 2.4879 | 7.0 | 1911 | 1.9724 | 0.6742 |
| 2.137 | 8.0 | 2184 | 1.8147 | 0.6928 |
| 2.137 | 9.0 | 2457 | 1.6748 | 0.7216 |
| 1.8286 | 10.0 | 2730 | 1.5458 | 0.7608 |
| 1.592 | 11.0 | 3003 | 1.4381 | 0.7876 |
| 1.592 | 12.0 | 3276 | 1.3335 | 0.8 |
| 1.3969 | 13.0 | 3549 | 1.2423 | 0.8124 |
| 1.3969 | 14.0 | 3822 | 1.1551 | 0.8330 |
| 1.2209 | 15.0 | 4095 | 1.0825 | 0.8474 |
| 1.2209 | 16.0 | 4368 | 1.0141 | 0.8536 |
| 1.0896 | 17.0 | 4641 | 0.9527 | 0.8763 |
| 1.0896 | 18.0 | 4914 | 0.8963 | 0.8825 |
| 0.9763 | 19.0 | 5187 | 0.8472 | 0.8948 |
| 0.9763 | 20.0 | 5460 | 0.7968 | 0.9052 |
| 0.8742 | 21.0 | 5733 | 0.7577 | 0.9072 |
| 0.784 | 22.0 | 6006 | 0.7193 | 0.9113 |
| 0.784 | 23.0 | 6279 | 0.6834 | 0.9113 |
| 0.7159 | 24.0 | 6552 | 0.6500 | 0.9196 |
| 0.7159 | 25.0 | 6825 | 0.6224 | 0.9196 |
| 0.6496 | 26.0 | 7098 | 0.5931 | 0.9237 |
| 0.6496 | 27.0 | 7371 | 0.5679 | 0.9278 |
| 0.5962 | 28.0 | 7644 | 0.5459 | 0.9258 |
| 0.5962 | 29.0 | 7917 | 0.5243 | 0.9320 |
| 0.5553 | 30.0 | 8190 | 0.5065 | 0.9361 |
| 0.5553 | 31.0 | 8463 | 0.4888 | 0.9320 |
| 0.517 | 32.0 | 8736 | 0.4732 | 0.9340 |
| 0.4827 | 33.0 | 9009 | 0.4607 | 0.9361 |
| 0.4827 | 34.0 | 9282 | 0.4477 | 0.9381 |
| 0.4479 | 35.0 | 9555 | 0.4346 | 0.9423 |
| 0.4479 | 36.0 | 9828 | 0.4240 | 0.9423 |
| 0.4231 | 37.0 | 10101 | 0.4153 | 0.9485 |
| 0.4231 | 38.0 | 10374 | 0.4065 | 0.9464 |
| 0.4157 | 39.0 | 10647 | 0.3990 | 0.9464 |
| 0.4157 | 40.0 | 10920 | 0.3915 | 0.9464 |
| 0.3914 | 41.0 | 11193 | 0.3866 | 0.9464 |
| 0.3914 | 42.0 | 11466 | 0.3810 | 0.9443 |
| 0.3747 | 43.0 | 11739 | 0.3763 | 0.9464 |
| 0.3684 | 44.0 | 12012 | 0.3727 | 0.9505 |
| 0.3684 | 45.0 | 12285 | 0.3690 | 0.9505 |
| 0.3605 | 46.0 | 12558 | 0.3662 | 0.9505 |
| 0.3605 | 47.0 | 12831 | 0.3643 | 0.9485 |
| 0.3499 | 48.0 | 13104 | 0.3628 | 0.9485 |
| 0.3499 | 49.0 | 13377 | 0.3618 | 0.9485 |
| 0.3514 | 50.0 | 13650 | 0.3615 | 0.9485 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
