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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-2-32-87
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-2-32-87
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: 1.0406
- Accuracy: 0.8438
## 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 | 2 | 1.2928 | 0.8438 |
| No log | 2.0 | 4 | 1.2923 | 0.8438 |
| No log | 3.0 | 6 | 1.2917 | 0.8438 |
| No log | 4.0 | 8 | 1.2902 | 0.8438 |
| 0.7235 | 5.0 | 10 | 1.2884 | 0.8438 |
| 0.7235 | 6.0 | 12 | 1.2856 | 0.8438 |
| 0.7235 | 7.0 | 14 | 1.2829 | 0.8438 |
| 0.7235 | 8.0 | 16 | 1.2800 | 0.8281 |
| 0.7235 | 9.0 | 18 | 1.2769 | 0.8281 |
| 0.5899 | 10.0 | 20 | 1.2742 | 0.8281 |
| 0.5899 | 11.0 | 22 | 1.2710 | 0.8281 |
| 0.5899 | 12.0 | 24 | 1.2662 | 0.8281 |
| 0.5899 | 13.0 | 26 | 1.2590 | 0.8281 |
| 0.5899 | 14.0 | 28 | 1.2466 | 0.8281 |
| 0.6318 | 15.0 | 30 | 1.2287 | 0.8281 |
| 0.6318 | 16.0 | 32 | 1.2138 | 0.8281 |
| 0.6318 | 17.0 | 34 | 1.2024 | 0.8281 |
| 0.6318 | 18.0 | 36 | 1.1924 | 0.8281 |
| 0.6318 | 19.0 | 38 | 1.1838 | 0.8281 |
| 0.4743 | 20.0 | 40 | 1.1729 | 0.8281 |
| 0.4743 | 21.0 | 42 | 1.1591 | 0.8281 |
| 0.4743 | 22.0 | 44 | 1.1527 | 0.8281 |
| 0.4743 | 23.0 | 46 | 1.1459 | 0.8281 |
| 0.4743 | 24.0 | 48 | 1.1407 | 0.8281 |
| 0.3414 | 25.0 | 50 | 1.1351 | 0.8281 |
| 0.3414 | 26.0 | 52 | 1.1305 | 0.8281 |
| 0.3414 | 27.0 | 54 | 1.1230 | 0.8281 |
| 0.3414 | 28.0 | 56 | 1.1087 | 0.8281 |
| 0.3414 | 29.0 | 58 | 1.0831 | 0.8281 |
| 0.3141 | 30.0 | 60 | 1.0555 | 0.8281 |
| 0.3141 | 31.0 | 62 | 1.0313 | 0.8438 |
| 0.3141 | 32.0 | 64 | 1.0141 | 0.8594 |
| 0.3141 | 33.0 | 66 | 1.0063 | 0.8438 |
| 0.3141 | 34.0 | 68 | 0.9990 | 0.8438 |
| 0.1594 | 35.0 | 70 | 0.9916 | 0.8438 |
| 0.1594 | 36.0 | 72 | 0.9884 | 0.8438 |
| 0.1594 | 37.0 | 74 | 0.9922 | 0.8438 |
| 0.1594 | 38.0 | 76 | 1.0013 | 0.8281 |
| 0.1594 | 39.0 | 78 | 1.0097 | 0.8281 |
| 0.1018 | 40.0 | 80 | 1.0209 | 0.8281 |
| 0.1018 | 41.0 | 82 | 1.0341 | 0.8281 |
| 0.1018 | 42.0 | 84 | 1.0352 | 0.8281 |
| 0.1018 | 43.0 | 86 | 1.0284 | 0.8281 |
| 0.1018 | 44.0 | 88 | 1.0236 | 0.8281 |
| 0.0404 | 45.0 | 90 | 1.0214 | 0.8438 |
| 0.0404 | 46.0 | 92 | 1.0237 | 0.8594 |
| 0.0404 | 47.0 | 94 | 1.0233 | 0.875 |
| 0.0404 | 48.0 | 96 | 1.0223 | 0.875 |
| 0.0404 | 49.0 | 98 | 1.0187 | 0.875 |
| 0.0052 | 50.0 | 100 | 1.0160 | 0.8594 |
| 0.0052 | 51.0 | 102 | 1.0134 | 0.8594 |
| 0.0052 | 52.0 | 104 | 1.0107 | 0.8438 |
| 0.0052 | 53.0 | 106 | 1.0083 | 0.8438 |
| 0.0052 | 54.0 | 108 | 1.0061 | 0.8438 |
| 0.0003 | 55.0 | 110 | 1.0043 | 0.8438 |
| 0.0003 | 56.0 | 112 | 1.0016 | 0.8438 |
| 0.0003 | 57.0 | 114 | 0.9994 | 0.8438 |
| 0.0003 | 58.0 | 116 | 0.9955 | 0.8438 |
| 0.0003 | 59.0 | 118 | 0.9902 | 0.8438 |
| 0.0003 | 60.0 | 120 | 0.9852 | 0.8438 |
| 0.0003 | 61.0 | 122 | 0.9806 | 0.8438 |
| 0.0003 | 62.0 | 124 | 0.9791 | 0.8438 |
| 0.0003 | 63.0 | 126 | 0.9794 | 0.8438 |
| 0.0003 | 64.0 | 128 | 0.9802 | 0.8438 |
| 0.0003 | 65.0 | 130 | 0.9809 | 0.8438 |
| 0.0003 | 66.0 | 132 | 0.9816 | 0.8438 |
| 0.0003 | 67.0 | 134 | 0.9821 | 0.8438 |
| 0.0003 | 68.0 | 136 | 0.9779 | 0.8438 |
| 0.0003 | 69.0 | 138 | 0.9746 | 0.8281 |
| 0.0003 | 70.0 | 140 | 0.9719 | 0.8281 |
| 0.0003 | 71.0 | 142 | 0.9699 | 0.8281 |
| 0.0003 | 72.0 | 144 | 0.9684 | 0.8438 |
| 0.0003 | 73.0 | 146 | 0.9673 | 0.8438 |
| 0.0003 | 74.0 | 148 | 0.9665 | 0.8438 |
| 0.0002 | 75.0 | 150 | 0.9660 | 0.8438 |
| 0.0002 | 76.0 | 152 | 0.9657 | 0.8438 |
| 0.0002 | 77.0 | 154 | 0.9605 | 0.8438 |
| 0.0002 | 78.0 | 156 | 0.9545 | 0.8438 |
| 0.0002 | 79.0 | 158 | 0.9485 | 0.8438 |
| 0.0004 | 80.0 | 160 | 0.9431 | 0.8438 |
| 0.0004 | 81.0 | 162 | 0.9384 | 0.8438 |
| 0.0004 | 82.0 | 164 | 0.9349 | 0.8438 |
| 0.0004 | 83.0 | 166 | 0.9324 | 0.8438 |
| 0.0004 | 84.0 | 168 | 0.9309 | 0.8438 |
| 0.0002 | 85.0 | 170 | 0.9309 | 0.8438 |
| 0.0002 | 86.0 | 172 | 0.9313 | 0.8438 |
| 0.0002 | 87.0 | 174 | 0.9331 | 0.8438 |
| 0.0002 | 88.0 | 176 | 0.9357 | 0.8438 |
| 0.0002 | 89.0 | 178 | 0.9380 | 0.8438 |
| 0.0002 | 90.0 | 180 | 0.9404 | 0.8438 |
| 0.0002 | 91.0 | 182 | 0.9428 | 0.8438 |
| 0.0002 | 92.0 | 184 | 0.9449 | 0.8438 |
| 0.0002 | 93.0 | 186 | 0.9472 | 0.8438 |
| 0.0002 | 94.0 | 188 | 0.9495 | 0.8438 |
| 0.0002 | 95.0 | 190 | 0.9521 | 0.8438 |
| 0.0002 | 96.0 | 192 | 0.9545 | 0.8438 |
| 0.0002 | 97.0 | 194 | 0.9576 | 0.8438 |
| 0.0002 | 98.0 | 196 | 0.9619 | 0.8438 |
| 0.0002 | 99.0 | 198 | 0.9658 | 0.8438 |
| 0.0002 | 100.0 | 200 | 0.9692 | 0.8438 |
| 0.0002 | 101.0 | 202 | 0.9723 | 0.8438 |
| 0.0002 | 102.0 | 204 | 0.9748 | 0.8438 |
| 0.0002 | 103.0 | 206 | 0.9781 | 0.8438 |
| 0.0002 | 104.0 | 208 | 0.9808 | 0.8438 |
| 0.0001 | 105.0 | 210 | 0.9832 | 0.8438 |
| 0.0001 | 106.0 | 212 | 0.9856 | 0.8438 |
| 0.0001 | 107.0 | 214 | 0.9884 | 0.8438 |
| 0.0001 | 108.0 | 216 | 0.9906 | 0.8438 |
| 0.0001 | 109.0 | 218 | 0.9903 | 0.8438 |
| 0.0002 | 110.0 | 220 | 0.9888 | 0.8438 |
| 0.0002 | 111.0 | 222 | 0.9874 | 0.8438 |
| 0.0002 | 112.0 | 224 | 0.9863 | 0.8438 |
| 0.0002 | 113.0 | 226 | 0.9854 | 0.8438 |
| 0.0002 | 114.0 | 228 | 0.9848 | 0.8438 |
| 0.0001 | 115.0 | 230 | 0.9878 | 0.8438 |
| 0.0001 | 116.0 | 232 | 0.9905 | 0.8438 |
| 0.0001 | 117.0 | 234 | 0.9926 | 0.8438 |
| 0.0001 | 118.0 | 236 | 0.9952 | 0.8438 |
| 0.0001 | 119.0 | 238 | 1.0010 | 0.8438 |
| 0.0001 | 120.0 | 240 | 1.0054 | 0.8438 |
| 0.0001 | 121.0 | 242 | 1.0086 | 0.8438 |
| 0.0001 | 122.0 | 244 | 1.0124 | 0.8438 |
| 0.0001 | 123.0 | 246 | 1.0155 | 0.8438 |
| 0.0001 | 124.0 | 248 | 1.0180 | 0.8438 |
| 0.0001 | 125.0 | 250 | 1.0201 | 0.8438 |
| 0.0001 | 126.0 | 252 | 1.0219 | 0.8438 |
| 0.0001 | 127.0 | 254 | 1.0235 | 0.8438 |
| 0.0001 | 128.0 | 256 | 1.0249 | 0.8438 |
| 0.0001 | 129.0 | 258 | 1.0261 | 0.8438 |
| 0.0001 | 130.0 | 260 | 1.0271 | 0.8438 |
| 0.0001 | 131.0 | 262 | 1.0279 | 0.8438 |
| 0.0001 | 132.0 | 264 | 1.0287 | 0.8438 |
| 0.0001 | 133.0 | 266 | 1.0293 | 0.8438 |
| 0.0001 | 134.0 | 268 | 1.0297 | 0.8438 |
| 0.0001 | 135.0 | 270 | 1.0301 | 0.8438 |
| 0.0001 | 136.0 | 272 | 1.0305 | 0.8438 |
| 0.0001 | 137.0 | 274 | 1.0309 | 0.8438 |
| 0.0001 | 138.0 | 276 | 1.0314 | 0.8438 |
| 0.0001 | 139.0 | 278 | 1.0324 | 0.8438 |
| 0.0001 | 140.0 | 280 | 1.0339 | 0.8438 |
| 0.0001 | 141.0 | 282 | 1.0352 | 0.8438 |
| 0.0001 | 142.0 | 284 | 1.0364 | 0.8438 |
| 0.0001 | 143.0 | 286 | 1.0373 | 0.8438 |
| 0.0001 | 144.0 | 288 | 1.0381 | 0.8438 |
| 0.0001 | 145.0 | 290 | 1.0388 | 0.8438 |
| 0.0001 | 146.0 | 292 | 1.0394 | 0.8438 |
| 0.0001 | 147.0 | 294 | 1.0401 | 0.8438 |
| 0.0001 | 148.0 | 296 | 1.0404 | 0.8438 |
| 0.0001 | 149.0 | 298 | 1.0404 | 0.8438 |
| 0.0001 | 150.0 | 300 | 1.0406 | 0.8438 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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