t5_base-qg-aap-test
This model is a fine-tuned version of unicamp-dl/ptt5-base-t5-vocab on the tiagoblima/preprocessed-du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set:
- Loss: 0.0278
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 1 | 8.5434 |
| No log | 2.0 | 2 | 7.3013 |
| No log | 3.0 | 3 | 6.1993 |
| No log | 4.0 | 4 | 5.2898 |
| No log | 5.0 | 5 | 4.5226 |
| No log | 6.0 | 6 | 3.9202 |
| No log | 7.0 | 7 | 3.4436 |
| No log | 8.0 | 8 | 3.0408 |
| No log | 9.0 | 9 | 2.7138 |
| No log | 10.0 | 10 | 2.4436 |
| No log | 11.0 | 11 | 2.2130 |
| No log | 12.0 | 12 | 2.0190 |
| No log | 13.0 | 13 | 1.8451 |
| No log | 14.0 | 14 | 1.6746 |
| No log | 15.0 | 15 | 1.5047 |
| No log | 16.0 | 16 | 1.3376 |
| No log | 17.0 | 17 | 1.1800 |
| No log | 18.0 | 18 | 1.0434 |
| No log | 19.0 | 19 | 0.9442 |
| No log | 20.0 | 20 | 0.8739 |
| No log | 21.0 | 21 | 0.8163 |
| No log | 22.0 | 22 | 0.7629 |
| No log | 23.0 | 23 | 0.7118 |
| No log | 24.0 | 24 | 0.6618 |
| No log | 25.0 | 25 | 0.6104 |
| No log | 26.0 | 26 | 0.5597 |
| No log | 27.0 | 27 | 0.5112 |
| No log | 28.0 | 28 | 0.4657 |
| No log | 29.0 | 29 | 0.4243 |
| No log | 30.0 | 30 | 0.3873 |
| No log | 31.0 | 31 | 0.3529 |
| No log | 32.0 | 32 | 0.3209 |
| No log | 33.0 | 33 | 0.2918 |
| No log | 34.0 | 34 | 0.2667 |
| No log | 35.0 | 35 | 0.2436 |
| No log | 36.0 | 36 | 0.2215 |
| No log | 37.0 | 37 | 0.2004 |
| No log | 38.0 | 38 | 0.1808 |
| No log | 39.0 | 39 | 0.1637 |
| No log | 40.0 | 40 | 0.1484 |
| No log | 41.0 | 41 | 0.1357 |
| No log | 42.0 | 42 | 0.1252 |
| No log | 43.0 | 43 | 0.1159 |
| No log | 44.0 | 44 | 0.1079 |
| No log | 45.0 | 45 | 0.0997 |
| No log | 46.0 | 46 | 0.0922 |
| No log | 47.0 | 47 | 0.0858 |
| No log | 48.0 | 48 | 0.0802 |
| No log | 49.0 | 49 | 0.0749 |
| No log | 50.0 | 50 | 0.0703 |
| No log | 51.0 | 51 | 0.0660 |
| No log | 52.0 | 52 | 0.0626 |
| No log | 53.0 | 53 | 0.0596 |
| No log | 54.0 | 54 | 0.0573 |
| No log | 55.0 | 55 | 0.0555 |
| No log | 56.0 | 56 | 0.0539 |
| No log | 57.0 | 57 | 0.0521 |
| No log | 58.0 | 58 | 0.0506 |
| No log | 59.0 | 59 | 0.0496 |
| No log | 60.0 | 60 | 0.0485 |
| No log | 61.0 | 61 | 0.0472 |
| No log | 62.0 | 62 | 0.0460 |
| No log | 63.0 | 63 | 0.0445 |
| No log | 64.0 | 64 | 0.0432 |
| No log | 65.0 | 65 | 0.0421 |
| No log | 66.0 | 66 | 0.0409 |
| No log | 67.0 | 67 | 0.0396 |
| No log | 68.0 | 68 | 0.0385 |
| No log | 69.0 | 69 | 0.0375 |
| No log | 70.0 | 70 | 0.0365 |
| No log | 71.0 | 71 | 0.0358 |
| No log | 72.0 | 72 | 0.0350 |
| No log | 73.0 | 73 | 0.0344 |
| No log | 74.0 | 74 | 0.0338 |
| No log | 75.0 | 75 | 0.0334 |
| No log | 76.0 | 76 | 0.0329 |
| No log | 77.0 | 77 | 0.0326 |
| No log | 78.0 | 78 | 0.0321 |
| No log | 79.0 | 79 | 0.0317 |
| No log | 80.0 | 80 | 0.0314 |
| No log | 81.0 | 81 | 0.0310 |
| No log | 82.0 | 82 | 0.0306 |
| No log | 83.0 | 83 | 0.0303 |
| No log | 84.0 | 84 | 0.0299 |
| No log | 85.0 | 85 | 0.0297 |
| No log | 86.0 | 86 | 0.0294 |
| No log | 87.0 | 87 | 0.0292 |
| No log | 88.0 | 88 | 0.0290 |
| No log | 89.0 | 89 | 0.0288 |
| No log | 90.0 | 90 | 0.0287 |
| No log | 91.0 | 91 | 0.0285 |
| No log | 92.0 | 92 | 0.0284 |
| No log | 93.0 | 93 | 0.0282 |
| No log | 94.0 | 94 | 0.0281 |
| No log | 95.0 | 95 | 0.0281 |
| No log | 96.0 | 96 | 0.0280 |
| No log | 97.0 | 97 | 0.0279 |
| No log | 98.0 | 98 | 0.0279 |
| No log | 99.0 | 99 | 0.0278 |
| 0.8788 | 100.0 | 100 | 0.0278 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0