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distilbert-base-cased-finetuned-cv2
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1933
- Precision: 0.5105
- Recall: 0.5893
- F1: 0.5471
- Accuracy: 0.9497
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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 17 | 0.5040 | 0.0 | 0.0 | 0.0 | 0.9043 |
| No log | 2.0 | 34 | 0.4049 | 0.0 | 0.0 | 0.0 | 0.9043 |
| No log | 3.0 | 51 | 0.3508 | 0.2712 | 0.1744 | 0.2123 | 0.9125 |
| No log | 4.0 | 68 | 0.3097 | 0.3073 | 0.2437 | 0.2718 | 0.9209 |
| No log | 5.0 | 85 | 0.2815 | 0.3323 | 0.3246 | 0.3284 | 0.9256 |
| No log | 6.0 | 102 | 0.2569 | 0.3522 | 0.3866 | 0.3686 | 0.9295 |
| No log | 7.0 | 119 | 0.2479 | 0.3545 | 0.4391 | 0.3923 | 0.9318 |
| No log | 8.0 | 136 | 0.2281 | 0.4159 | 0.4569 | 0.4354 | 0.9359 |
| No log | 9.0 | 153 | 0.2310 | 0.5044 | 0.4202 | 0.4585 | 0.9401 |
| No log | 10.0 | 170 | 0.2258 | 0.4144 | 0.5063 | 0.4558 | 0.9337 |
| No log | 11.0 | 187 | 0.2127 | 0.4413 | 0.4979 | 0.4679 | 0.9383 |
| No log | 12.0 | 204 | 0.2172 | 0.4056 | 0.5210 | 0.4561 | 0.9364 |
| No log | 13.0 | 221 | 0.2093 | 0.4313 | 0.5441 | 0.4812 | 0.9404 |
| No log | 14.0 | 238 | 0.2062 | 0.4263 | 0.5378 | 0.4756 | 0.9389 |
| No log | 15.0 | 255 | 0.2010 | 0.4554 | 0.5578 | 0.5014 | 0.9429 |
| No log | 16.0 | 272 | 0.1959 | 0.4683 | 0.5578 | 0.5091 | 0.9455 |
| No log | 17.0 | 289 | 0.1955 | 0.4668 | 0.5620 | 0.5100 | 0.9449 |
| No log | 18.0 | 306 | 0.1950 | 0.4768 | 0.5714 | 0.5198 | 0.9466 |
| No log | 19.0 | 323 | 0.1942 | 0.4892 | 0.5735 | 0.5280 | 0.9474 |
| No log | 20.0 | 340 | 0.1937 | 0.5408 | 0.5641 | 0.5522 | 0.9501 |
| No log | 21.0 | 357 | 0.1993 | 0.4665 | 0.5924 | 0.5220 | 0.9457 |
| No log | 22.0 | 374 | 0.1925 | 0.5086 | 0.5882 | 0.5455 | 0.9489 |
| No log | 23.0 | 391 | 0.2035 | 0.4604 | 0.6050 | 0.5229 | 0.9444 |
| No log | 24.0 | 408 | 0.1902 | 0.5190 | 0.5725 | 0.5445 | 0.9497 |
| No log | 25.0 | 425 | 0.1937 | 0.4970 | 0.5998 | 0.5436 | 0.9481 |
| No log | 26.0 | 442 | 0.1942 | 0.4969 | 0.5914 | 0.5400 | 0.9488 |
| No log | 27.0 | 459 | 0.1937 | 0.5072 | 0.5924 | 0.5465 | 0.9489 |
| No log | 28.0 | 476 | 0.1927 | 0.5146 | 0.5914 | 0.5503 | 0.9500 |
| No log | 29.0 | 493 | 0.1937 | 0.5108 | 0.5956 | 0.5500 | 0.9495 |
| 0.1908 | 30.0 | 510 | 0.1933 | 0.5105 | 0.5893 | 0.5471 | 0.9497 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
