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distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.0244
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: 60
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 30 | 3.5643 |
| No log | 2.0 | 60 | 2.4546 |
| No log | 3.0 | 90 | 2.3018 |
| No log | 4.0 | 120 | 2.4636 |
| No log | 5.0 | 150 | 2.4736 |
| No log | 6.0 | 180 | 2.5580 |
| No log | 7.0 | 210 | 2.6686 |
| No log | 8.0 | 240 | 2.7249 |
| No log | 9.0 | 270 | 3.2596 |
| No log | 10.0 | 300 | 3.5904 |
| No log | 11.0 | 330 | 3.6709 |
| No log | 12.0 | 360 | 3.6431 |
| No log | 13.0 | 390 | 3.6343 |
| No log | 14.0 | 420 | 3.8316 |
| No log | 15.0 | 450 | 3.6363 |
| No log | 16.0 | 480 | 3.8468 |
| 0.8931 | 17.0 | 510 | 3.7114 |
| 0.8931 | 18.0 | 540 | 3.8719 |
| 0.8931 | 19.0 | 570 | 4.0872 |
| 0.8931 | 20.0 | 600 | 4.2989 |
| 0.8931 | 21.0 | 630 | 4.5494 |
| 0.8931 | 22.0 | 660 | 4.2565 |
| 0.8931 | 23.0 | 690 | 4.3009 |
| 0.8931 | 24.0 | 720 | 4.1816 |
| 0.8931 | 25.0 | 750 | 4.2583 |
| 0.8931 | 26.0 | 780 | 4.2276 |
| 0.8931 | 27.0 | 810 | 4.3481 |
| 0.8931 | 28.0 | 840 | 4.4369 |
| 0.8931 | 29.0 | 870 | 4.4891 |
| 0.8931 | 30.0 | 900 | 4.5521 |
| 0.8931 | 31.0 | 930 | 4.5201 |
| 0.8931 | 32.0 | 960 | 4.6323 |
| 0.8931 | 33.0 | 990 | 4.4766 |
| 0.0297 | 34.0 | 1020 | 4.7612 |
| 0.0297 | 35.0 | 1050 | 4.9057 |
| 0.0297 | 36.0 | 1080 | 4.7580 |
| 0.0297 | 37.0 | 1110 | 4.6351 |
| 0.0297 | 38.0 | 1140 | 4.6495 |
| 0.0297 | 39.0 | 1170 | 4.5980 |
| 0.0297 | 40.0 | 1200 | 4.6370 |
| 0.0297 | 41.0 | 1230 | 4.6523 |
| 0.0297 | 42.0 | 1260 | 4.5802 |
| 0.0297 | 43.0 | 1290 | 4.6304 |
| 0.0297 | 44.0 | 1320 | 4.7111 |
| 0.0297 | 45.0 | 1350 | 4.7219 |
| 0.0297 | 46.0 | 1380 | 4.7323 |
| 0.0297 | 47.0 | 1410 | 4.9115 |
| 0.0297 | 48.0 | 1440 | 4.7873 |
| 0.0297 | 49.0 | 1470 | 4.9340 |
| 0.0023 | 50.0 | 1500 | 5.0638 |
| 0.0023 | 51.0 | 1530 | 5.0750 |
| 0.0023 | 52.0 | 1560 | 4.9338 |
| 0.0023 | 53.0 | 1590 | 4.9197 |
| 0.0023 | 54.0 | 1620 | 4.9282 |
| 0.0023 | 55.0 | 1650 | 5.0038 |
| 0.0023 | 56.0 | 1680 | 4.9848 |
| 0.0023 | 57.0 | 1710 | 4.9932 |
| 0.0023 | 58.0 | 1740 | 5.0134 |
| 0.0023 | 59.0 | 1770 | 5.0303 |
| 0.0023 | 60.0 | 1800 | 5.0244 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
