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distilbert-base-uncased_finetuned_SPEECH_TEXT_DISPLAY
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2319
- Accuracy: 0.7368
- F1: 0.7282
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.0388 | 1.0 | 19 | 0.9710 | 0.4211 | 0.2495 |
| 0.9432 | 2.0 | 38 | 0.9188 | 0.5789 | 0.4964 |
| 0.7889 | 3.0 | 57 | 0.8813 | 0.5789 | 0.5263 |
| 0.5823 | 4.0 | 76 | 0.7974 | 0.6842 | 0.6452 |
| 0.4275 | 5.0 | 95 | 0.7669 | 0.6316 | 0.5965 |
| 0.2995 | 6.0 | 114 | 0.6675 | 0.8421 | 0.8344 |
| 0.1676 | 7.0 | 133 | 0.7643 | 0.7368 | 0.7333 |
| 0.0976 | 8.0 | 152 | 0.7864 | 0.7895 | 0.7839 |
| 0.0477 | 9.0 | 171 | 0.7838 | 0.7895 | 0.7772 |
| 0.0247 | 10.0 | 190 | 1.1000 | 0.6842 | 0.6817 |
| 0.0127 | 11.0 | 209 | 0.9551 | 0.7895 | 0.7772 |
| 0.0084 | 12.0 | 228 | 1.1178 | 0.6842 | 0.6792 |
| 0.0071 | 13.0 | 247 | 1.1489 | 0.6842 | 0.6792 |
| 0.0055 | 14.0 | 266 | 1.1278 | 0.7368 | 0.7282 |
| 0.0051 | 15.0 | 285 | 1.0925 | 0.7368 | 0.7282 |
| 0.0049 | 16.0 | 304 | 1.1031 | 0.7368 | 0.7282 |
| 0.0042 | 17.0 | 323 | 1.1299 | 0.7368 | 0.7282 |
| 0.0037 | 18.0 | 342 | 1.1644 | 0.7368 | 0.7282 |
| 0.0035 | 19.0 | 361 | 1.1659 | 0.7368 | 0.7282 |
| 0.0031 | 20.0 | 380 | 1.1704 | 0.7368 | 0.7282 |
| 0.0028 | 21.0 | 399 | 1.1664 | 0.7368 | 0.7282 |
| 0.0029 | 22.0 | 418 | 1.1693 | 0.7368 | 0.7282 |
| 0.0028 | 23.0 | 437 | 1.1858 | 0.7368 | 0.7282 |
| 0.0024 | 24.0 | 456 | 1.2007 | 0.7368 | 0.7282 |
| 0.0024 | 25.0 | 475 | 1.1982 | 0.7368 | 0.7282 |
| 0.0022 | 26.0 | 494 | 1.1896 | 0.7368 | 0.7282 |
| 0.002 | 27.0 | 513 | 1.1955 | 0.7368 | 0.7282 |
| 0.0019 | 28.0 | 532 | 1.2016 | 0.7368 | 0.7282 |
| 0.0019 | 29.0 | 551 | 1.2066 | 0.7368 | 0.7282 |
| 0.0021 | 30.0 | 570 | 1.2120 | 0.7368 | 0.7282 |
| 0.0019 | 31.0 | 589 | 1.2145 | 0.7368 | 0.7282 |
| 0.0019 | 32.0 | 608 | 1.2179 | 0.7368 | 0.7282 |
| 0.0018 | 33.0 | 627 | 1.2221 | 0.7368 | 0.7282 |
| 0.0019 | 34.0 | 646 | 1.2237 | 0.7368 | 0.7282 |
| 0.0016 | 35.0 | 665 | 1.2275 | 0.7368 | 0.7282 |
| 0.0016 | 36.0 | 684 | 1.2294 | 0.7368 | 0.7282 |
| 0.0015 | 37.0 | 703 | 1.2305 | 0.7368 | 0.7282 |
| 0.0017 | 38.0 | 722 | 1.2315 | 0.7368 | 0.7282 |
| 0.0016 | 39.0 | 741 | 1.2318 | 0.7368 | 0.7282 |
| 0.0018 | 40.0 | 760 | 1.2319 | 0.7368 | 0.7282 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.10.2
- Datasets 2.5.2
- Tokenizers 0.12.1
