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license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-sst-2-64-13
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-base-sst-2-64-13
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0411
- Accuracy: 0.8672
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6951 | 0.5 |
| No log | 2.0 | 8 | 0.6951 | 0.5 |
| 0.6962 | 3.0 | 12 | 0.6951 | 0.5 |
| 0.6962 | 4.0 | 16 | 0.6950 | 0.5 |
| 0.7017 | 5.0 | 20 | 0.6949 | 0.5 |
| 0.7017 | 6.0 | 24 | 0.6949 | 0.5 |
| 0.7017 | 7.0 | 28 | 0.6947 | 0.5 |
| 0.6966 | 8.0 | 32 | 0.6946 | 0.5 |
| 0.6966 | 9.0 | 36 | 0.6945 | 0.5 |
| 0.6927 | 10.0 | 40 | 0.6944 | 0.5 |
| 0.6927 | 11.0 | 44 | 0.6943 | 0.5 |
| 0.6927 | 12.0 | 48 | 0.6941 | 0.5 |
| 0.6961 | 13.0 | 52 | 0.6940 | 0.5 |
| 0.6961 | 14.0 | 56 | 0.6939 | 0.5 |
| 0.6875 | 15.0 | 60 | 0.6938 | 0.5 |
| 0.6875 | 16.0 | 64 | 0.6936 | 0.5 |
| 0.6875 | 17.0 | 68 | 0.6934 | 0.5 |
| 0.6935 | 18.0 | 72 | 0.6932 | 0.5 |
| 0.6935 | 19.0 | 76 | 0.6929 | 0.5 |
| 0.6948 | 20.0 | 80 | 0.6927 | 0.5 |
| 0.6948 | 21.0 | 84 | 0.6924 | 0.5 |
| 0.6948 | 22.0 | 88 | 0.6922 | 0.5 |
| 0.6906 | 23.0 | 92 | 0.6920 | 0.5 |
| 0.6906 | 24.0 | 96 | 0.6917 | 0.5 |
| 0.691 | 25.0 | 100 | 0.6913 | 0.5 |
| 0.691 | 26.0 | 104 | 0.6909 | 0.5 |
| 0.691 | 27.0 | 108 | 0.6904 | 0.5 |
| 0.6855 | 28.0 | 112 | 0.6899 | 0.5 |
| 0.6855 | 29.0 | 116 | 0.6891 | 0.5 |
| 0.6858 | 30.0 | 120 | 0.6882 | 0.5234 |
| 0.6858 | 31.0 | 124 | 0.6870 | 0.5156 |
| 0.6858 | 32.0 | 128 | 0.6852 | 0.6016 |
| 0.6764 | 33.0 | 132 | 0.6825 | 0.6562 |
| 0.6764 | 34.0 | 136 | 0.6782 | 0.7266 |
| 0.6616 | 35.0 | 140 | 0.6703 | 0.7969 |
| 0.6616 | 36.0 | 144 | 0.6545 | 0.8281 |
| 0.6616 | 37.0 | 148 | 0.6245 | 0.8516 |
| 0.6082 | 38.0 | 152 | 0.5651 | 0.8594 |
| 0.6082 | 39.0 | 156 | 0.4835 | 0.875 |
| 0.4548 | 40.0 | 160 | 0.4109 | 0.9062 |
| 0.4548 | 41.0 | 164 | 0.3606 | 0.875 |
| 0.4548 | 42.0 | 168 | 0.3454 | 0.8594 |
| 0.2218 | 43.0 | 172 | 0.3403 | 0.8594 |
| 0.2218 | 44.0 | 176 | 0.3537 | 0.8828 |
| 0.0892 | 45.0 | 180 | 0.4646 | 0.8516 |
| 0.0892 | 46.0 | 184 | 0.4402 | 0.875 |
| 0.0892 | 47.0 | 188 | 0.4719 | 0.8828 |
| 0.0254 | 48.0 | 192 | 0.5172 | 0.8828 |
| 0.0254 | 49.0 | 196 | 0.5613 | 0.8828 |
| 0.0105 | 50.0 | 200 | 0.6035 | 0.875 |
| 0.0105 | 51.0 | 204 | 0.6341 | 0.875 |
| 0.0105 | 52.0 | 208 | 0.6591 | 0.875 |
| 0.006 | 53.0 | 212 | 0.6804 | 0.875 |
| 0.006 | 54.0 | 216 | 0.6935 | 0.875 |
| 0.0041 | 55.0 | 220 | 0.7167 | 0.875 |
| 0.0041 | 56.0 | 224 | 0.7315 | 0.875 |
| 0.0041 | 57.0 | 228 | 0.7464 | 0.875 |
| 0.0032 | 58.0 | 232 | 0.7560 | 0.8594 |
| 0.0032 | 59.0 | 236 | 0.8753 | 0.8516 |
| 0.0098 | 60.0 | 240 | 0.9437 | 0.8438 |
| 0.0098 | 61.0 | 244 | 0.7740 | 0.8672 |
| 0.0098 | 62.0 | 248 | 0.7258 | 0.8828 |
| 0.0094 | 63.0 | 252 | 0.7815 | 0.8594 |
| 0.0094 | 64.0 | 256 | 0.7836 | 0.8516 |
| 0.0021 | 65.0 | 260 | 0.7854 | 0.8516 |
| 0.0021 | 66.0 | 264 | 0.7817 | 0.8594 |
| 0.0021 | 67.0 | 268 | 0.7698 | 0.8828 |
| 0.0019 | 68.0 | 272 | 0.7848 | 0.875 |
| 0.0019 | 69.0 | 276 | 0.7895 | 0.8828 |
| 0.0017 | 70.0 | 280 | 0.7971 | 0.8828 |
| 0.0017 | 71.0 | 284 | 0.8038 | 0.8828 |
| 0.0017 | 72.0 | 288 | 0.8091 | 0.8828 |
| 0.0014 | 73.0 | 292 | 0.8139 | 0.8828 |
| 0.0014 | 74.0 | 296 | 0.8183 | 0.8828 |
| 0.0014 | 75.0 | 300 | 0.8223 | 0.8828 |
| 0.0014 | 76.0 | 304 | 0.8274 | 0.8828 |
| 0.0014 | 77.0 | 308 | 0.8357 | 0.875 |
| 0.0012 | 78.0 | 312 | 0.8436 | 0.875 |
| 0.0012 | 79.0 | 316 | 0.8523 | 0.875 |
| 0.0012 | 80.0 | 320 | 0.8591 | 0.875 |
| 0.0012 | 81.0 | 324 | 0.8653 | 0.875 |
| 0.0012 | 82.0 | 328 | 0.8708 | 0.875 |
| 0.001 | 83.0 | 332 | 0.8271 | 0.8594 |
| 0.001 | 84.0 | 336 | 1.0450 | 0.8438 |
| 0.0012 | 85.0 | 340 | 1.1347 | 0.8281 |
| 0.0012 | 86.0 | 344 | 1.1696 | 0.8281 |
| 0.0012 | 87.0 | 348 | 0.8631 | 0.8672 |
| 0.0137 | 88.0 | 352 | 1.1491 | 0.8359 |
| 0.0137 | 89.0 | 356 | 1.0635 | 0.8516 |
| 0.0012 | 90.0 | 360 | 0.9027 | 0.875 |
| 0.0012 | 91.0 | 364 | 0.9503 | 0.8594 |
| 0.0012 | 92.0 | 368 | 1.0398 | 0.8281 |
| 0.0185 | 93.0 | 372 | 0.9044 | 0.875 |
| 0.0185 | 94.0 | 376 | 1.0978 | 0.8438 |
| 0.0009 | 95.0 | 380 | 0.9955 | 0.8672 |
| 0.0009 | 96.0 | 384 | 0.9313 | 0.875 |
| 0.0009 | 97.0 | 388 | 0.9295 | 0.875 |
| 0.0008 | 98.0 | 392 | 1.0927 | 0.8516 |
| 0.0008 | 99.0 | 396 | 0.9251 | 0.875 |
| 0.0007 | 100.0 | 400 | 0.9454 | 0.8594 |
| 0.0007 | 101.0 | 404 | 1.0023 | 0.8516 |
| 0.0007 | 102.0 | 408 | 1.0098 | 0.8516 |
| 0.0006 | 103.0 | 412 | 0.9944 | 0.8594 |
| 0.0006 | 104.0 | 416 | 0.9832 | 0.8516 |
| 0.0006 | 105.0 | 420 | 0.9090 | 0.8828 |
| 0.0006 | 106.0 | 424 | 1.2248 | 0.8359 |
| 0.0006 | 107.0 | 428 | 0.8722 | 0.8906 |
| 0.0197 | 108.0 | 432 | 0.8764 | 0.8828 |
| 0.0197 | 109.0 | 436 | 0.9771 | 0.875 |
| 0.0005 | 110.0 | 440 | 0.9871 | 0.875 |
| 0.0005 | 111.0 | 444 | 0.9235 | 0.875 |
| 0.0005 | 112.0 | 448 | 0.8418 | 0.8828 |
| 0.0005 | 113.0 | 452 | 0.8653 | 0.8906 |
| 0.0005 | 114.0 | 456 | 0.9098 | 0.8828 |
| 0.0005 | 115.0 | 460 | 0.9285 | 0.8828 |
| 0.0005 | 116.0 | 464 | 0.9443 | 0.875 |
| 0.0005 | 117.0 | 468 | 0.9584 | 0.8672 |
| 0.0005 | 118.0 | 472 | 0.9704 | 0.8672 |
| 0.0005 | 119.0 | 476 | 0.9805 | 0.8672 |
| 0.0004 | 120.0 | 480 | 0.9904 | 0.8672 |
| 0.0004 | 121.0 | 484 | 0.9920 | 0.8672 |
| 0.0004 | 122.0 | 488 | 0.9927 | 0.8672 |
| 0.0004 | 123.0 | 492 | 1.0015 | 0.8672 |
| 0.0004 | 124.0 | 496 | 1.0181 | 0.8672 |
| 0.0004 | 125.0 | 500 | 1.0289 | 0.8672 |
| 0.0004 | 126.0 | 504 | 1.0374 | 0.8672 |
| 0.0004 | 127.0 | 508 | 1.0408 | 0.8672 |
| 0.0004 | 128.0 | 512 | 1.0432 | 0.8672 |
| 0.0004 | 129.0 | 516 | 1.0472 | 0.8672 |
| 0.0003 | 130.0 | 520 | 1.0489 | 0.8672 |
| 0.0003 | 131.0 | 524 | 1.0497 | 0.8672 |
| 0.0003 | 132.0 | 528 | 1.0496 | 0.8672 |
| 0.0003 | 133.0 | 532 | 1.0497 | 0.8672 |
| 0.0003 | 134.0 | 536 | 1.0496 | 0.8672 |
| 0.0003 | 135.0 | 540 | 1.0492 | 0.8672 |
| 0.0003 | 136.0 | 544 | 1.0491 | 0.8672 |
| 0.0003 | 137.0 | 548 | 1.0482 | 0.8672 |
| 0.0003 | 138.0 | 552 | 1.0471 | 0.8672 |
| 0.0003 | 139.0 | 556 | 1.0456 | 0.8672 |
| 0.0003 | 140.0 | 560 | 1.0432 | 0.8672 |
| 0.0003 | 141.0 | 564 | 1.0411 | 0.8672 |
| 0.0003 | 142.0 | 568 | 1.0399 | 0.8672 |
| 0.0003 | 143.0 | 572 | 1.0398 | 0.8672 |
| 0.0003 | 144.0 | 576 | 1.0396 | 0.8672 |
| 0.0003 | 145.0 | 580 | 1.0393 | 0.8672 |
| 0.0003 | 146.0 | 584 | 1.0396 | 0.8672 |
| 0.0003 | 147.0 | 588 | 1.0400 | 0.8672 |
| 0.0003 | 148.0 | 592 | 1.0405 | 0.8672 |
| 0.0003 | 149.0 | 596 | 1.0409 | 0.8672 |
| 0.0003 | 150.0 | 600 | 1.0411 | 0.8672 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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