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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-2-64-100
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-2-64-100
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: 0.9480
- 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 | 0.8613 | 0.9141 |
| No log | 2.0 | 8 | 0.8613 | 0.9141 |
| 0.6496 | 3.0 | 12 | 0.8614 | 0.9141 |
| 0.6496 | 4.0 | 16 | 0.8614 | 0.9141 |
| 0.6483 | 5.0 | 20 | 0.8596 | 0.9141 |
| 0.6483 | 6.0 | 24 | 0.8575 | 0.9141 |
| 0.6483 | 7.0 | 28 | 0.8557 | 0.9141 |
| 0.6867 | 8.0 | 32 | 0.8528 | 0.9141 |
| 0.6867 | 9.0 | 36 | 0.8506 | 0.9141 |
| 0.3821 | 10.0 | 40 | 0.8542 | 0.9062 |
| 0.3821 | 11.0 | 44 | 0.8721 | 0.8984 |
| 0.3821 | 12.0 | 48 | 0.8877 | 0.8984 |
| 0.4452 | 13.0 | 52 | 0.8920 | 0.8984 |
| 0.4452 | 14.0 | 56 | 0.8952 | 0.8984 |
| 0.3224 | 15.0 | 60 | 0.8920 | 0.9062 |
| 0.3224 | 16.0 | 64 | 0.8833 | 0.9062 |
| 0.3224 | 17.0 | 68 | 0.8727 | 0.9062 |
| 0.2699 | 18.0 | 72 | 0.8284 | 0.8984 |
| 0.2699 | 19.0 | 76 | 0.7829 | 0.9062 |
| 0.1873 | 20.0 | 80 | 0.7713 | 0.9062 |
| 0.1873 | 21.0 | 84 | 0.7646 | 0.8984 |
| 0.1873 | 22.0 | 88 | 0.7517 | 0.8984 |
| 0.1282 | 23.0 | 92 | 0.7379 | 0.9062 |
| 0.1282 | 24.0 | 96 | 0.7295 | 0.9062 |
| 0.0438 | 25.0 | 100 | 0.7243 | 0.8984 |
| 0.0438 | 26.0 | 104 | 0.7038 | 0.9141 |
| 0.0438 | 27.0 | 108 | 0.6994 | 0.9219 |
| 0.0154 | 28.0 | 112 | 0.6997 | 0.9062 |
| 0.0154 | 29.0 | 116 | 0.7184 | 0.8984 |
| 0.0019 | 30.0 | 120 | 0.7601 | 0.9062 |
| 0.0019 | 31.0 | 124 | 0.7739 | 0.9062 |
| 0.0019 | 32.0 | 128 | 0.7854 | 0.9062 |
| 0.0003 | 33.0 | 132 | 0.7934 | 0.9062 |
| 0.0003 | 34.0 | 136 | 0.7945 | 0.9062 |
| 0.0002 | 35.0 | 140 | 0.7896 | 0.9062 |
| 0.0002 | 36.0 | 144 | 0.7711 | 0.9062 |
| 0.0002 | 37.0 | 148 | 0.7503 | 0.9062 |
| 0.0004 | 38.0 | 152 | 0.7436 | 0.9062 |
| 0.0004 | 39.0 | 156 | 0.7464 | 0.9062 |
| 0.0001 | 40.0 | 160 | 0.7492 | 0.9062 |
| 0.0001 | 41.0 | 164 | 0.7990 | 0.9062 |
| 0.0001 | 42.0 | 168 | 0.8244 | 0.9062 |
| 0.0059 | 43.0 | 172 | 0.8377 | 0.9062 |
| 0.0059 | 44.0 | 176 | 0.8496 | 0.9062 |
| 0.0001 | 45.0 | 180 | 0.8582 | 0.9062 |
| 0.0001 | 46.0 | 184 | 0.8646 | 0.9062 |
| 0.0001 | 47.0 | 188 | 0.8286 | 0.9062 |
| 0.0005 | 48.0 | 192 | 0.8002 | 0.9062 |
| 0.0005 | 49.0 | 196 | 0.7854 | 0.9062 |
| 0.0001 | 50.0 | 200 | 0.7691 | 0.9062 |
| 0.0001 | 51.0 | 204 | 0.7594 | 0.9062 |
| 0.0001 | 52.0 | 208 | 0.7618 | 0.9062 |
| 0.0003 | 53.0 | 212 | 0.8175 | 0.9062 |
| 0.0003 | 54.0 | 216 | 0.8539 | 0.9062 |
| 0.0001 | 55.0 | 220 | 0.8737 | 0.9062 |
| 0.0001 | 56.0 | 224 | 0.8661 | 0.9062 |
| 0.0001 | 57.0 | 228 | 0.8398 | 0.9062 |
| 0.0038 | 58.0 | 232 | 0.8162 | 0.9062 |
| 0.0038 | 59.0 | 236 | 0.7946 | 0.9062 |
| 0.0001 | 60.0 | 240 | 0.7866 | 0.9062 |
| 0.0001 | 61.0 | 244 | 0.7776 | 0.9141 |
| 0.0001 | 62.0 | 248 | 0.7781 | 0.9141 |
| 0.0001 | 63.0 | 252 | 0.7963 | 0.9062 |
| 0.0001 | 64.0 | 256 | 0.8099 | 0.9062 |
| 0.0 | 65.0 | 260 | 0.8196 | 0.9062 |
| 0.0 | 66.0 | 264 | 0.8284 | 0.9062 |
| 0.0 | 67.0 | 268 | 0.8880 | 0.9062 |
| 0.0045 | 68.0 | 272 | 0.9217 | 0.9062 |
| 0.0045 | 69.0 | 276 | 0.9374 | 0.8984 |
| 0.0082 | 70.0 | 280 | 0.9364 | 0.9062 |
| 0.0082 | 71.0 | 284 | 0.8651 | 0.9062 |
| 0.0082 | 72.0 | 288 | 0.7849 | 0.8984 |
| 0.0003 | 73.0 | 292 | 0.7981 | 0.8984 |
| 0.0003 | 74.0 | 296 | 0.7808 | 0.9141 |
| 0.021 | 75.0 | 300 | 0.8438 | 0.9062 |
| 0.021 | 76.0 | 304 | 0.8882 | 0.8984 |
| 0.021 | 77.0 | 308 | 0.9214 | 0.8984 |
| 0.0001 | 78.0 | 312 | 0.9396 | 0.8984 |
| 0.0001 | 79.0 | 316 | 0.9493 | 0.8984 |
| 0.0 | 80.0 | 320 | 0.9549 | 0.8984 |
| 0.0 | 81.0 | 324 | 0.9466 | 0.8984 |
| 0.0 | 82.0 | 328 | 0.9041 | 0.8984 |
| 0.0001 | 83.0 | 332 | 0.8993 | 0.8984 |
| 0.0001 | 84.0 | 336 | 0.9616 | 0.8984 |
| 0.0001 | 85.0 | 340 | 0.9844 | 0.8984 |
| 0.0001 | 86.0 | 344 | 0.9934 | 0.8906 |
| 0.0001 | 87.0 | 348 | 0.9999 | 0.8906 |
| 0.0001 | 88.0 | 352 | 0.9973 | 0.8906 |
| 0.0001 | 89.0 | 356 | 0.9943 | 0.8984 |
| 0.0 | 90.0 | 360 | 0.9929 | 0.8984 |
| 0.0 | 91.0 | 364 | 0.9921 | 0.8984 |
| 0.0 | 92.0 | 368 | 0.9915 | 0.8984 |
| 0.0 | 93.0 | 372 | 0.9916 | 0.8984 |
| 0.0 | 94.0 | 376 | 0.9924 | 0.8984 |
| 0.0 | 95.0 | 380 | 0.9930 | 0.8984 |
| 0.0 | 96.0 | 384 | 0.9936 | 0.8984 |
| 0.0 | 97.0 | 388 | 0.9940 | 0.8984 |
| 0.0 | 98.0 | 392 | 0.9946 | 0.8984 |
| 0.0 | 99.0 | 396 | 0.9950 | 0.8984 |
| 0.0006 | 100.0 | 400 | 0.9869 | 0.8984 |
| 0.0006 | 101.0 | 404 | 0.8625 | 0.8984 |
| 0.0006 | 102.0 | 408 | 0.7755 | 0.9219 |
| 0.0 | 103.0 | 412 | 0.7887 | 0.8984 |
| 0.0 | 104.0 | 416 | 0.7844 | 0.9062 |
| 0.0062 | 105.0 | 420 | 0.8504 | 0.8984 |
| 0.0062 | 106.0 | 424 | 0.9449 | 0.8984 |
| 0.0062 | 107.0 | 428 | 0.9568 | 0.8906 |
| 0.0 | 108.0 | 432 | 0.9504 | 0.8984 |
| 0.0 | 109.0 | 436 | 0.9700 | 0.8984 |
| 0.0 | 110.0 | 440 | 0.9875 | 0.8906 |
| 0.0 | 111.0 | 444 | 1.0002 | 0.8906 |
| 0.0 | 112.0 | 448 | 1.0095 | 0.8828 |
| 0.0 | 113.0 | 452 | 1.0156 | 0.8828 |
| 0.0 | 114.0 | 456 | 0.8995 | 0.8984 |
| 0.0144 | 115.0 | 460 | 0.8017 | 0.8984 |
| 0.0144 | 116.0 | 464 | 0.7774 | 0.9062 |
| 0.0144 | 117.0 | 468 | 0.7913 | 0.9062 |
| 0.0 | 118.0 | 472 | 0.8033 | 0.8984 |
| 0.0 | 119.0 | 476 | 0.8244 | 0.8906 |
| 0.0001 | 120.0 | 480 | 0.9148 | 0.8984 |
| 0.0001 | 121.0 | 484 | 1.0038 | 0.8828 |
| 0.0001 | 122.0 | 488 | 1.1128 | 0.875 |
| 0.0 | 123.0 | 492 | 1.1276 | 0.875 |
| 0.0 | 124.0 | 496 | 1.1209 | 0.8828 |
| 0.0 | 125.0 | 500 | 1.1161 | 0.8828 |
| 0.0 | 126.0 | 504 | 1.1119 | 0.8828 |
| 0.0 | 127.0 | 508 | 1.1037 | 0.8828 |
| 0.0 | 128.0 | 512 | 1.0644 | 0.8828 |
| 0.0 | 129.0 | 516 | 1.0175 | 0.875 |
| 0.0 | 130.0 | 520 | 0.9819 | 0.8828 |
| 0.0 | 131.0 | 524 | 0.9613 | 0.8906 |
| 0.0 | 132.0 | 528 | 0.9509 | 0.8906 |
| 0.0 | 133.0 | 532 | 0.9463 | 0.8906 |
| 0.0 | 134.0 | 536 | 0.9441 | 0.875 |
| 0.0 | 135.0 | 540 | 0.9432 | 0.875 |
| 0.0 | 136.0 | 544 | 0.9429 | 0.875 |
| 0.0 | 137.0 | 548 | 0.9429 | 0.8828 |
| 0.0 | 138.0 | 552 | 0.9430 | 0.8828 |
| 0.0 | 139.0 | 556 | 0.9432 | 0.8828 |
| 0.0 | 140.0 | 560 | 0.9434 | 0.8828 |
| 0.0 | 141.0 | 564 | 0.9436 | 0.8828 |
| 0.0 | 142.0 | 568 | 0.9438 | 0.8906 |
| 0.0 | 143.0 | 572 | 0.9439 | 0.8906 |
| 0.0 | 144.0 | 576 | 0.9448 | 0.8906 |
| 0.0 | 145.0 | 580 | 0.9461 | 0.8906 |
| 0.0 | 146.0 | 584 | 0.9470 | 0.8906 |
| 0.0 | 147.0 | 588 | 0.9476 | 0.8906 |
| 0.0 | 148.0 | 592 | 0.9478 | 0.8906 |
| 0.0 | 149.0 | 596 | 0.9480 | 0.8906 |
| 0.0 | 150.0 | 600 | 0.9480 | 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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