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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-32-100
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. -->
# bert-base-uncased-sst-2-32-100
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: 0.4379
- Accuracy: 0.9219
## 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 | 0.5385 | 0.9219 |
| No log | 2.0 | 4 | 0.5392 | 0.9219 |
| No log | 3.0 | 6 | 0.5398 | 0.9219 |
| No log | 4.0 | 8 | 0.5410 | 0.9219 |
| 0.733 | 5.0 | 10 | 0.5426 | 0.9219 |
| 0.733 | 6.0 | 12 | 0.5443 | 0.9062 |
| 0.733 | 7.0 | 14 | 0.5461 | 0.9062 |
| 0.733 | 8.0 | 16 | 0.5481 | 0.9062 |
| 0.733 | 9.0 | 18 | 0.5487 | 0.9062 |
| 0.6383 | 10.0 | 20 | 0.5495 | 0.9062 |
| 0.6383 | 11.0 | 22 | 0.5546 | 0.8906 |
| 0.6383 | 12.0 | 24 | 0.5643 | 0.9062 |
| 0.6383 | 13.0 | 26 | 0.5742 | 0.9062 |
| 0.6383 | 14.0 | 28 | 0.5875 | 0.9062 |
| 0.4993 | 15.0 | 30 | 0.5982 | 0.9062 |
| 0.4993 | 16.0 | 32 | 0.6100 | 0.9062 |
| 0.4993 | 17.0 | 34 | 0.6222 | 0.9062 |
| 0.4993 | 18.0 | 36 | 0.6263 | 0.9062 |
| 0.4993 | 19.0 | 38 | 0.6305 | 0.9062 |
| 0.4891 | 20.0 | 40 | 0.6335 | 0.9062 |
| 0.4891 | 21.0 | 42 | 0.6368 | 0.9062 |
| 0.4891 | 22.0 | 44 | 0.6351 | 0.9062 |
| 0.4891 | 23.0 | 46 | 0.6301 | 0.9062 |
| 0.4891 | 24.0 | 48 | 0.6212 | 0.9062 |
| 0.377 | 25.0 | 50 | 0.6100 | 0.9062 |
| 0.377 | 26.0 | 52 | 0.5999 | 0.9062 |
| 0.377 | 27.0 | 54 | 0.5852 | 0.9062 |
| 0.377 | 28.0 | 56 | 0.5737 | 0.9062 |
| 0.377 | 29.0 | 58 | 0.5606 | 0.9219 |
| 0.3369 | 30.0 | 60 | 0.5466 | 0.9062 |
| 0.3369 | 31.0 | 62 | 0.5319 | 0.9062 |
| 0.3369 | 32.0 | 64 | 0.5205 | 0.9062 |
| 0.3369 | 33.0 | 66 | 0.5074 | 0.9219 |
| 0.3369 | 34.0 | 68 | 0.5025 | 0.9219 |
| 0.19 | 35.0 | 70 | 0.4984 | 0.9219 |
| 0.19 | 36.0 | 72 | 0.4934 | 0.9219 |
| 0.19 | 37.0 | 74 | 0.4927 | 0.9375 |
| 0.19 | 38.0 | 76 | 0.4955 | 0.9375 |
| 0.19 | 39.0 | 78 | 0.4968 | 0.9375 |
| 0.0507 | 40.0 | 80 | 0.4956 | 0.9375 |
| 0.0507 | 41.0 | 82 | 0.4882 | 0.9375 |
| 0.0507 | 42.0 | 84 | 0.4784 | 0.9375 |
| 0.0507 | 43.0 | 86 | 0.4710 | 0.9219 |
| 0.0507 | 44.0 | 88 | 0.4650 | 0.9219 |
| 0.0102 | 45.0 | 90 | 0.4578 | 0.9219 |
| 0.0102 | 46.0 | 92 | 0.4540 | 0.9219 |
| 0.0102 | 47.0 | 94 | 0.4566 | 0.9062 |
| 0.0102 | 48.0 | 96 | 0.4682 | 0.9062 |
| 0.0102 | 49.0 | 98 | 0.4831 | 0.9219 |
| 0.0026 | 50.0 | 100 | 0.4922 | 0.9219 |
| 0.0026 | 51.0 | 102 | 0.4985 | 0.9219 |
| 0.0026 | 52.0 | 104 | 0.5029 | 0.9219 |
| 0.0026 | 53.0 | 106 | 0.5062 | 0.9219 |
| 0.0026 | 54.0 | 108 | 0.5087 | 0.9219 |
| 0.001 | 55.0 | 110 | 0.5100 | 0.9219 |
| 0.001 | 56.0 | 112 | 0.5110 | 0.9219 |
| 0.001 | 57.0 | 114 | 0.5112 | 0.9219 |
| 0.001 | 58.0 | 116 | 0.5112 | 0.9219 |
| 0.001 | 59.0 | 118 | 0.5110 | 0.9219 |
| 0.0004 | 60.0 | 120 | 0.5087 | 0.9219 |
| 0.0004 | 61.0 | 122 | 0.5028 | 0.9219 |
| 0.0004 | 62.0 | 124 | 0.4965 | 0.9219 |
| 0.0004 | 63.0 | 126 | 0.4903 | 0.9219 |
| 0.0004 | 64.0 | 128 | 0.4848 | 0.9219 |
| 0.0003 | 65.0 | 130 | 0.4802 | 0.9219 |
| 0.0003 | 66.0 | 132 | 0.4767 | 0.9219 |
| 0.0003 | 67.0 | 134 | 0.4739 | 0.9219 |
| 0.0003 | 68.0 | 136 | 0.4719 | 0.9219 |
| 0.0003 | 69.0 | 138 | 0.4707 | 0.9219 |
| 0.0024 | 70.0 | 140 | 0.4600 | 0.9219 |
| 0.0024 | 71.0 | 142 | 0.4439 | 0.9219 |
| 0.0024 | 72.0 | 144 | 0.4336 | 0.9062 |
| 0.0024 | 73.0 | 146 | 0.4283 | 0.9062 |
| 0.0024 | 74.0 | 148 | 0.4253 | 0.9219 |
| 0.0002 | 75.0 | 150 | 0.4237 | 0.9219 |
| 0.0002 | 76.0 | 152 | 0.4232 | 0.9375 |
| 0.0002 | 77.0 | 154 | 0.4230 | 0.9375 |
| 0.0002 | 78.0 | 156 | 0.4229 | 0.9375 |
| 0.0002 | 79.0 | 158 | 0.4228 | 0.9375 |
| 0.0002 | 80.0 | 160 | 0.4228 | 0.9375 |
| 0.0002 | 81.0 | 162 | 0.4225 | 0.9375 |
| 0.0002 | 82.0 | 164 | 0.4237 | 0.9062 |
| 0.0002 | 83.0 | 166 | 0.4384 | 0.9219 |
| 0.0002 | 84.0 | 168 | 0.4565 | 0.9219 |
| 0.0004 | 85.0 | 170 | 0.4717 | 0.9219 |
| 0.0004 | 86.0 | 172 | 0.4813 | 0.9219 |
| 0.0004 | 87.0 | 174 | 0.4858 | 0.9219 |
| 0.0004 | 88.0 | 176 | 0.4885 | 0.9219 |
| 0.0004 | 89.0 | 178 | 0.4897 | 0.9219 |
| 0.0002 | 90.0 | 180 | 0.4904 | 0.9219 |
| 0.0002 | 91.0 | 182 | 0.4865 | 0.9219 |
| 0.0002 | 92.0 | 184 | 0.4732 | 0.9219 |
| 0.0002 | 93.0 | 186 | 0.4557 | 0.9219 |
| 0.0002 | 94.0 | 188 | 0.4388 | 0.9219 |
| 0.0053 | 95.0 | 190 | 0.4254 | 0.9219 |
| 0.0053 | 96.0 | 192 | 0.4171 | 0.9219 |
| 0.0053 | 97.0 | 194 | 0.4132 | 0.9375 |
| 0.0053 | 98.0 | 196 | 0.4118 | 0.9375 |
| 0.0053 | 99.0 | 198 | 0.4115 | 0.9219 |
| 0.0002 | 100.0 | 200 | 0.4118 | 0.9219 |
| 0.0002 | 101.0 | 202 | 0.4122 | 0.9219 |
| 0.0002 | 102.0 | 204 | 0.4125 | 0.9219 |
| 0.0002 | 103.0 | 206 | 0.4128 | 0.9219 |
| 0.0002 | 104.0 | 208 | 0.4131 | 0.9219 |
| 0.0002 | 105.0 | 210 | 0.4133 | 0.9219 |
| 0.0002 | 106.0 | 212 | 0.4134 | 0.9219 |
| 0.0002 | 107.0 | 214 | 0.4140 | 0.9219 |
| 0.0002 | 108.0 | 216 | 0.4149 | 0.9219 |
| 0.0002 | 109.0 | 218 | 0.4158 | 0.9219 |
| 0.0002 | 110.0 | 220 | 0.4167 | 0.9219 |
| 0.0002 | 111.0 | 222 | 0.4175 | 0.9219 |
| 0.0002 | 112.0 | 224 | 0.4183 | 0.9375 |
| 0.0002 | 113.0 | 226 | 0.4190 | 0.9375 |
| 0.0002 | 114.0 | 228 | 0.4197 | 0.9375 |
| 0.0001 | 115.0 | 230 | 0.4203 | 0.9375 |
| 0.0001 | 116.0 | 232 | 0.4208 | 0.9375 |
| 0.0001 | 117.0 | 234 | 0.4218 | 0.9219 |
| 0.0001 | 118.0 | 236 | 0.4228 | 0.9219 |
| 0.0001 | 119.0 | 238 | 0.4237 | 0.9219 |
| 0.0002 | 120.0 | 240 | 0.4244 | 0.9219 |
| 0.0002 | 121.0 | 242 | 0.4251 | 0.9219 |
| 0.0002 | 122.0 | 244 | 0.4257 | 0.9219 |
| 0.0002 | 123.0 | 246 | 0.4263 | 0.9219 |
| 0.0002 | 124.0 | 248 | 0.4269 | 0.9219 |
| 0.0002 | 125.0 | 250 | 0.4273 | 0.9219 |
| 0.0002 | 126.0 | 252 | 0.4277 | 0.9219 |
| 0.0002 | 127.0 | 254 | 0.4280 | 0.9219 |
| 0.0002 | 128.0 | 256 | 0.4284 | 0.9219 |
| 0.0002 | 129.0 | 258 | 0.4287 | 0.9219 |
| 0.0008 | 130.0 | 260 | 0.4330 | 0.9219 |
| 0.0008 | 131.0 | 262 | 0.4554 | 0.9219 |
| 0.0008 | 132.0 | 264 | 0.4714 | 0.9219 |
| 0.0008 | 133.0 | 266 | 0.4845 | 0.9375 |
| 0.0008 | 134.0 | 268 | 0.5000 | 0.9219 |
| 0.0001 | 135.0 | 270 | 0.5167 | 0.9219 |
| 0.0001 | 136.0 | 272 | 0.5308 | 0.9062 |
| 0.0001 | 137.0 | 274 | 0.5417 | 0.9062 |
| 0.0001 | 138.0 | 276 | 0.5480 | 0.9062 |
| 0.0001 | 139.0 | 278 | 0.5529 | 0.9062 |
| 0.0001 | 140.0 | 280 | 0.5566 | 0.9062 |
| 0.0001 | 141.0 | 282 | 0.5570 | 0.9062 |
| 0.0001 | 142.0 | 284 | 0.5565 | 0.9062 |
| 0.0001 | 143.0 | 286 | 0.5555 | 0.9062 |
| 0.0001 | 144.0 | 288 | 0.5544 | 0.9062 |
| 0.0001 | 145.0 | 290 | 0.5511 | 0.9062 |
| 0.0001 | 146.0 | 292 | 0.5096 | 0.9219 |
| 0.0001 | 147.0 | 294 | 0.4811 | 0.9375 |
| 0.0001 | 148.0 | 296 | 0.4624 | 0.9219 |
| 0.0001 | 149.0 | 298 | 0.4488 | 0.9219 |
| 0.0002 | 150.0 | 300 | 0.4379 | 0.9219 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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