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tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-mc-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-mc-6
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6310
- Accuracy: 0.95
## 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-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6594 | 1.0 | 23 | 0.6523 | 0.95 |
| 0.6979 | 2.0 | 46 | 0.6400 | 0.95 |
| 0.6407 | 3.0 | 69 | 0.6331 | 0.95 |
| 0.7082 | 4.0 | 92 | 0.6360 | 0.95 |
| 0.6493 | 5.0 | 115 | 0.6258 | 0.95 |
| 0.6827 | 6.0 | 138 | 0.6239 | 0.95 |
| 0.6511 | 7.0 | 161 | 0.6399 | 0.95 |
| 0.6459 | 8.0 | 184 | 0.6279 | 0.95 |
| 0.6623 | 9.0 | 207 | 0.6247 | 0.95 |
| 0.6583 | 10.0 | 230 | 0.6307 | 0.95 |
| 0.6613 | 11.0 | 253 | 0.6269 | 0.95 |
| 0.6223 | 12.0 | 276 | 0.6270 | 0.95 |
| 0.6375 | 13.0 | 299 | 0.6284 | 0.95 |
| 0.7009 | 14.0 | 322 | 0.6309 | 0.95 |
| 0.6705 | 15.0 | 345 | 0.6299 | 0.95 |
| 0.6503 | 16.0 | 368 | 0.6396 | 0.95 |
| 0.7073 | 17.0 | 391 | 0.6305 | 0.95 |
| 0.614 | 18.0 | 414 | 0.6308 | 0.95 |
| 0.6512 | 19.0 | 437 | 0.6305 | 0.95 |
| 0.7055 | 20.0 | 460 | 0.6308 | 0.95 |
| 0.5702 | 21.0 | 483 | 0.6304 | 0.95 |
| 0.6654 | 22.0 | 506 | 0.6305 | 0.95 |
| 0.6129 | 23.0 | 529 | 0.6308 | 0.95 |
| 0.6477 | 24.0 | 552 | 0.6310 | 0.95 |
| 0.6178 | 25.0 | 575 | 0.6312 | 0.95 |
| 0.6562 | 26.0 | 598 | 0.6312 | 0.95 |
| 0.5972 | 27.0 | 621 | 0.6317 | 0.95 |
| 0.6324 | 28.0 | 644 | 0.6312 | 0.95 |
| 0.6064 | 29.0 | 667 | 0.6312 | 0.95 |
| 0.5833 | 30.0 | 690 | 0.6312 | 0.95 |
| 0.6916 | 31.0 | 713 | 0.6312 | 0.95 |
| 0.5591 | 32.0 | 736 | 0.6312 | 0.95 |
| 0.6477 | 33.0 | 759 | 0.6312 | 0.95 |
| 0.6483 | 34.0 | 782 | 0.6311 | 0.95 |
| 0.5563 | 35.0 | 805 | 0.6310 | 0.95 |
| 0.6061 | 36.0 | 828 | 0.6310 | 0.95 |
| 0.6043 | 37.0 | 851 | 0.6310 | 0.95 |
| 0.6274 | 38.0 | 874 | 0.6310 | 0.95 |
| 0.6115 | 39.0 | 897 | 0.6310 | 0.95 |
| 0.7107 | 40.0 | 920 | 0.6310 | 0.95 |
| 0.6703 | 41.0 | 943 | 0.6310 | 0.95 |
| 0.6052 | 42.0 | 966 | 0.6310 | 0.95 |
| 0.6228 | 43.0 | 989 | 0.6310 | 0.95 |
| 0.6629 | 44.0 | 1012 | 0.6310 | 0.95 |
| 0.5804 | 45.0 | 1035 | 0.6310 | 0.95 |
| 0.6194 | 46.0 | 1058 | 0.6310 | 0.95 |
| 0.6529 | 47.0 | 1081 | 0.6310 | 0.95 |
| 0.5779 | 48.0 | 1104 | 0.6310 | 0.95 |
| 0.6652 | 49.0 | 1127 | 0.6310 | 0.95 |
| 0.6163 | 50.0 | 1150 | 0.6310 | 0.95 |
| 0.6873 | 51.0 | 1173 | 0.6310 | 0.95 |
| 0.5608 | 52.0 | 1196 | 0.6310 | 0.95 |
| 0.6646 | 53.0 | 1219 | 0.6310 | 0.95 |
| 0.6222 | 54.0 | 1242 | 0.6310 | 0.95 |
| 0.6629 | 55.0 | 1265 | 0.6310 | 0.95 |
| 0.592 | 56.0 | 1288 | 0.6310 | 0.95 |
| 0.6047 | 57.0 | 1311 | 0.6310 | 0.95 |
| 0.5668 | 58.0 | 1334 | 0.6310 | 0.95 |
| 0.6358 | 59.0 | 1357 | 0.6310 | 0.95 |
| 0.648 | 60.0 | 1380 | 0.6310 | 0.95 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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