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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-sst-2-64-21
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. -->
# best_model-sst-2-64-21
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0374
- Accuracy: 0.8906
## 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 | 1.1068 | 0.8672 |
| No log | 2.0 | 8 | 1.1055 | 0.8672 |
| 0.5789 | 3.0 | 12 | 1.1002 | 0.8672 |
| 0.5789 | 4.0 | 16 | 1.0902 | 0.8672 |
| 0.4952 | 5.0 | 20 | 1.0797 | 0.8672 |
| 0.4952 | 6.0 | 24 | 1.0662 | 0.8672 |
| 0.4952 | 7.0 | 28 | 1.0461 | 0.8672 |
| 0.4202 | 8.0 | 32 | 1.0329 | 0.8672 |
| 0.4202 | 9.0 | 36 | 1.0326 | 0.8672 |
| 0.5159 | 10.0 | 40 | 1.0217 | 0.8672 |
| 0.5159 | 11.0 | 44 | 1.0053 | 0.8672 |
| 0.5159 | 12.0 | 48 | 0.9908 | 0.875 |
| 0.4018 | 13.0 | 52 | 0.9818 | 0.8828 |
| 0.4018 | 14.0 | 56 | 0.9686 | 0.8828 |
| 0.2452 | 15.0 | 60 | 0.9591 | 0.8828 |
| 0.2452 | 16.0 | 64 | 0.9489 | 0.8828 |
| 0.2452 | 17.0 | 68 | 0.9421 | 0.8828 |
| 0.1966 | 18.0 | 72 | 0.9354 | 0.8828 |
| 0.1966 | 19.0 | 76 | 0.9318 | 0.8906 |
| 0.1955 | 20.0 | 80 | 0.9353 | 0.8828 |
| 0.1955 | 21.0 | 84 | 0.9552 | 0.8828 |
| 0.1955 | 22.0 | 88 | 0.9728 | 0.875 |
| 0.1316 | 23.0 | 92 | 0.9686 | 0.875 |
| 0.1316 | 24.0 | 96 | 0.9555 | 0.875 |
| 0.0488 | 25.0 | 100 | 0.9442 | 0.8828 |
| 0.0488 | 26.0 | 104 | 0.9410 | 0.8828 |
| 0.0488 | 27.0 | 108 | 0.9413 | 0.8828 |
| 0.0023 | 28.0 | 112 | 0.9522 | 0.8828 |
| 0.0023 | 29.0 | 116 | 0.9614 | 0.8828 |
| 0.0019 | 30.0 | 120 | 0.9603 | 0.8828 |
| 0.0019 | 31.0 | 124 | 0.9474 | 0.8828 |
| 0.0019 | 32.0 | 128 | 0.9408 | 0.8906 |
| 0.0136 | 33.0 | 132 | 0.9417 | 0.8906 |
| 0.0136 | 34.0 | 136 | 0.9433 | 0.8906 |
| 0.0037 | 35.0 | 140 | 0.9412 | 0.8906 |
| 0.0037 | 36.0 | 144 | 0.9529 | 0.8906 |
| 0.0037 | 37.0 | 148 | 0.9641 | 0.8828 |
| 0.0003 | 38.0 | 152 | 0.9868 | 0.8828 |
| 0.0003 | 39.0 | 156 | 0.9985 | 0.875 |
| 0.0002 | 40.0 | 160 | 1.0006 | 0.875 |
| 0.0002 | 41.0 | 164 | 1.0009 | 0.875 |
| 0.0002 | 42.0 | 168 | 1.0038 | 0.875 |
| 0.0013 | 43.0 | 172 | 0.9982 | 0.8828 |
| 0.0013 | 44.0 | 176 | 0.9853 | 0.8828 |
| 0.0102 | 45.0 | 180 | 0.9790 | 0.8828 |
| 0.0102 | 46.0 | 184 | 0.9900 | 0.8828 |
| 0.0102 | 47.0 | 188 | 1.0004 | 0.8828 |
| 0.0002 | 48.0 | 192 | 1.0063 | 0.875 |
| 0.0002 | 49.0 | 196 | 1.0095 | 0.875 |
| 0.0001 | 50.0 | 200 | 1.0136 | 0.875 |
| 0.0001 | 51.0 | 204 | 1.0180 | 0.8672 |
| 0.0001 | 52.0 | 208 | 1.0206 | 0.8672 |
| 0.0001 | 53.0 | 212 | 1.0178 | 0.8672 |
| 0.0001 | 54.0 | 216 | 1.0157 | 0.8672 |
| 0.0001 | 55.0 | 220 | 1.0140 | 0.875 |
| 0.0001 | 56.0 | 224 | 1.0128 | 0.875 |
| 0.0001 | 57.0 | 228 | 1.0117 | 0.875 |
| 0.0001 | 58.0 | 232 | 1.0097 | 0.875 |
| 0.0001 | 59.0 | 236 | 1.0082 | 0.875 |
| 0.0001 | 60.0 | 240 | 1.0002 | 0.8828 |
| 0.0001 | 61.0 | 244 | 0.9944 | 0.8828 |
| 0.0001 | 62.0 | 248 | 0.9913 | 0.8906 |
| 0.0001 | 63.0 | 252 | 0.9897 | 0.8906 |
| 0.0001 | 64.0 | 256 | 0.9893 | 0.8906 |
| 0.0001 | 65.0 | 260 | 0.9895 | 0.8906 |
| 0.0001 | 66.0 | 264 | 0.9899 | 0.8906 |
| 0.0001 | 67.0 | 268 | 0.9905 | 0.8906 |
| 0.0001 | 68.0 | 272 | 0.9913 | 0.8906 |
| 0.0001 | 69.0 | 276 | 0.9962 | 0.8906 |
| 0.0001 | 70.0 | 280 | 1.0023 | 0.8828 |
| 0.0001 | 71.0 | 284 | 1.0079 | 0.8828 |
| 0.0001 | 72.0 | 288 | 1.0118 | 0.875 |
| 0.0001 | 73.0 | 292 | 1.0144 | 0.875 |
| 0.0001 | 74.0 | 296 | 1.0161 | 0.875 |
| 0.0001 | 75.0 | 300 | 1.0172 | 0.875 |
| 0.0001 | 76.0 | 304 | 1.0178 | 0.875 |
| 0.0001 | 77.0 | 308 | 1.0241 | 0.875 |
| 0.0183 | 78.0 | 312 | 1.0549 | 0.8672 |
| 0.0183 | 79.0 | 316 | 1.0631 | 0.8672 |
| 0.0001 | 80.0 | 320 | 1.0629 | 0.875 |
| 0.0001 | 81.0 | 324 | 1.0650 | 0.875 |
| 0.0001 | 82.0 | 328 | 1.0672 | 0.8594 |
| 0.0001 | 83.0 | 332 | 1.0686 | 0.8594 |
| 0.0001 | 84.0 | 336 | 1.0632 | 0.875 |
| 0.0131 | 85.0 | 340 | 1.0157 | 0.8672 |
| 0.0131 | 86.0 | 344 | 0.9959 | 0.8828 |
| 0.0131 | 87.0 | 348 | 0.9946 | 0.8906 |
| 0.0001 | 88.0 | 352 | 0.9933 | 0.8906 |
| 0.0001 | 89.0 | 356 | 0.9933 | 0.8906 |
| 0.0001 | 90.0 | 360 | 0.9941 | 0.8828 |
| 0.0001 | 91.0 | 364 | 0.9949 | 0.8828 |
| 0.0001 | 92.0 | 368 | 0.9954 | 0.8828 |
| 0.0001 | 93.0 | 372 | 0.9959 | 0.8828 |
| 0.0001 | 94.0 | 376 | 0.9962 | 0.8828 |
| 0.0001 | 95.0 | 380 | 0.9961 | 0.8828 |
| 0.0001 | 96.0 | 384 | 0.9963 | 0.8828 |
| 0.0001 | 97.0 | 388 | 0.9967 | 0.8828 |
| 0.0001 | 98.0 | 392 | 0.9987 | 0.8906 |
| 0.0001 | 99.0 | 396 | 1.0214 | 0.8828 |
| 0.0105 | 100.0 | 400 | 1.0346 | 0.875 |
| 0.0105 | 101.0 | 404 | 1.0406 | 0.875 |
| 0.0105 | 102.0 | 408 | 1.0435 | 0.875 |
| 0.0001 | 103.0 | 412 | 1.0444 | 0.875 |
| 0.0001 | 104.0 | 416 | 1.0446 | 0.875 |
| 0.0001 | 105.0 | 420 | 1.0447 | 0.875 |
| 0.0001 | 106.0 | 424 | 1.0448 | 0.875 |
| 0.0001 | 107.0 | 428 | 1.0453 | 0.8828 |
| 0.0001 | 108.0 | 432 | 1.0457 | 0.8828 |
| 0.0001 | 109.0 | 436 | 1.0488 | 0.875 |
| 0.0184 | 110.0 | 440 | 1.0597 | 0.875 |
| 0.0184 | 111.0 | 444 | 1.0939 | 0.8594 |
| 0.0184 | 112.0 | 448 | 1.1410 | 0.8438 |
| 0.0001 | 113.0 | 452 | 1.1659 | 0.8438 |
| 0.0001 | 114.0 | 456 | 1.1104 | 0.8594 |
| 0.0001 | 115.0 | 460 | 1.0816 | 0.8672 |
| 0.0001 | 116.0 | 464 | 1.0695 | 0.875 |
| 0.0001 | 117.0 | 468 | 1.0702 | 0.875 |
| 0.0 | 118.0 | 472 | 1.0709 | 0.875 |
| 0.0 | 119.0 | 476 | 1.0704 | 0.875 |
| 0.0 | 120.0 | 480 | 1.0693 | 0.875 |
| 0.0 | 121.0 | 484 | 1.0684 | 0.875 |
| 0.0 | 122.0 | 488 | 1.0677 | 0.875 |
| 0.0 | 123.0 | 492 | 1.0676 | 0.875 |
| 0.0 | 124.0 | 496 | 1.0676 | 0.875 |
| 0.0 | 125.0 | 500 | 1.0675 | 0.875 |
| 0.0 | 126.0 | 504 | 1.0675 | 0.875 |
| 0.0 | 127.0 | 508 | 1.0676 | 0.875 |
| 0.0 | 128.0 | 512 | 1.0687 | 0.875 |
| 0.0 | 129.0 | 516 | 1.0694 | 0.875 |
| 0.0 | 130.0 | 520 | 1.0701 | 0.875 |
| 0.0 | 131.0 | 524 | 1.0707 | 0.875 |
| 0.0 | 132.0 | 528 | 1.0661 | 0.875 |
| 0.0001 | 133.0 | 532 | 1.0391 | 0.8906 |
| 0.0001 | 134.0 | 536 | 1.0258 | 0.8906 |
| 0.0 | 135.0 | 540 | 1.0188 | 0.8906 |
| 0.0 | 136.0 | 544 | 1.0171 | 0.8906 |
| 0.0 | 137.0 | 548 | 1.0188 | 0.8828 |
| 0.0 | 138.0 | 552 | 1.0210 | 0.875 |
| 0.0 | 139.0 | 556 | 1.0223 | 0.875 |
| 0.0001 | 140.0 | 560 | 1.0202 | 0.8828 |
| 0.0001 | 141.0 | 564 | 1.0235 | 0.8906 |
| 0.0001 | 142.0 | 568 | 1.0288 | 0.8906 |
| 0.0 | 143.0 | 572 | 1.0322 | 0.8906 |
| 0.0 | 144.0 | 576 | 1.0343 | 0.8906 |
| 0.0 | 145.0 | 580 | 1.0356 | 0.8906 |
| 0.0 | 146.0 | 584 | 1.0364 | 0.8906 |
| 0.0 | 147.0 | 588 | 1.0369 | 0.8906 |
| 0.0 | 148.0 | 592 | 1.0372 | 0.8906 |
| 0.0 | 149.0 | 596 | 1.0374 | 0.8906 |
| 0.0 | 150.0 | 600 | 1.0374 | 0.8906 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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