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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-2-32-87
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. -->
# bert-base-uncased-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: 0.9995
- Accuracy: 0.875
## 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 | 1.3036 | 0.8281 |
| No log | 2.0 | 4 | 1.3032 | 0.8281 |
| No log | 3.0 | 6 | 1.3022 | 0.8281 |
| No log | 4.0 | 8 | 1.3002 | 0.8438 |
| 0.6888 | 5.0 | 10 | 1.2981 | 0.8438 |
| 0.6888 | 6.0 | 12 | 1.2958 | 0.8438 |
| 0.6888 | 7.0 | 14 | 1.2937 | 0.8438 |
| 0.6888 | 8.0 | 16 | 1.2916 | 0.8438 |
| 0.6888 | 9.0 | 18 | 1.2896 | 0.8281 |
| 0.6235 | 10.0 | 20 | 1.2880 | 0.8281 |
| 0.6235 | 11.0 | 22 | 1.2862 | 0.8281 |
| 0.6235 | 12.0 | 24 | 1.2847 | 0.8281 |
| 0.6235 | 13.0 | 26 | 1.2833 | 0.8281 |
| 0.6235 | 14.0 | 28 | 1.2827 | 0.8281 |
| 0.6224 | 15.0 | 30 | 1.2813 | 0.8281 |
| 0.6224 | 16.0 | 32 | 1.2788 | 0.8281 |
| 0.6224 | 17.0 | 34 | 1.2739 | 0.8281 |
| 0.6224 | 18.0 | 36 | 1.2670 | 0.8281 |
| 0.6224 | 19.0 | 38 | 1.2583 | 0.8281 |
| 0.5366 | 20.0 | 40 | 1.2501 | 0.8281 |
| 0.5366 | 21.0 | 42 | 1.2366 | 0.8281 |
| 0.5366 | 22.0 | 44 | 1.2258 | 0.8281 |
| 0.5366 | 23.0 | 46 | 1.2148 | 0.8281 |
| 0.5366 | 24.0 | 48 | 1.2069 | 0.8281 |
| 0.3634 | 25.0 | 50 | 1.1973 | 0.8281 |
| 0.3634 | 26.0 | 52 | 1.1888 | 0.8281 |
| 0.3634 | 27.0 | 54 | 1.1754 | 0.8281 |
| 0.3634 | 28.0 | 56 | 1.1583 | 0.8281 |
| 0.3634 | 29.0 | 58 | 1.1462 | 0.8281 |
| 0.3447 | 30.0 | 60 | 1.1399 | 0.8281 |
| 0.3447 | 31.0 | 62 | 1.1399 | 0.8281 |
| 0.3447 | 32.0 | 64 | 1.1328 | 0.8281 |
| 0.3447 | 33.0 | 66 | 1.1304 | 0.8281 |
| 0.3447 | 34.0 | 68 | 1.1275 | 0.8281 |
| 0.2231 | 35.0 | 70 | 1.1185 | 0.8281 |
| 0.2231 | 36.0 | 72 | 1.1059 | 0.8281 |
| 0.2231 | 37.0 | 74 | 1.0901 | 0.8281 |
| 0.2231 | 38.0 | 76 | 1.0711 | 0.8281 |
| 0.2231 | 39.0 | 78 | 1.0516 | 0.8281 |
| 0.0925 | 40.0 | 80 | 1.0339 | 0.8281 |
| 0.0925 | 41.0 | 82 | 1.0151 | 0.8281 |
| 0.0925 | 42.0 | 84 | 0.9910 | 0.8281 |
| 0.0925 | 43.0 | 86 | 0.9616 | 0.8281 |
| 0.0925 | 44.0 | 88 | 0.9422 | 0.8281 |
| 0.024 | 45.0 | 90 | 0.9346 | 0.8281 |
| 0.024 | 46.0 | 92 | 0.9374 | 0.8281 |
| 0.024 | 47.0 | 94 | 0.9413 | 0.8438 |
| 0.024 | 48.0 | 96 | 0.9460 | 0.8438 |
| 0.024 | 49.0 | 98 | 0.9470 | 0.8438 |
| 0.0161 | 50.0 | 100 | 0.9483 | 0.8438 |
| 0.0161 | 51.0 | 102 | 0.9505 | 0.8438 |
| 0.0161 | 52.0 | 104 | 0.9534 | 0.8438 |
| 0.0161 | 53.0 | 106 | 0.9565 | 0.8438 |
| 0.0161 | 54.0 | 108 | 0.9591 | 0.8438 |
| 0.0003 | 55.0 | 110 | 0.9613 | 0.8438 |
| 0.0003 | 56.0 | 112 | 0.9609 | 0.8438 |
| 0.0003 | 57.0 | 114 | 0.9606 | 0.8438 |
| 0.0003 | 58.0 | 116 | 0.9597 | 0.8438 |
| 0.0003 | 59.0 | 118 | 0.9582 | 0.8438 |
| 0.0003 | 60.0 | 120 | 0.9572 | 0.8438 |
| 0.0003 | 61.0 | 122 | 0.9557 | 0.8438 |
| 0.0003 | 62.0 | 124 | 0.9563 | 0.8438 |
| 0.0003 | 63.0 | 126 | 0.9514 | 0.8438 |
| 0.0003 | 64.0 | 128 | 0.9487 | 0.8438 |
| 0.0006 | 65.0 | 130 | 0.9472 | 0.8438 |
| 0.0006 | 66.0 | 132 | 0.9472 | 0.8438 |
| 0.0006 | 67.0 | 134 | 0.9486 | 0.8438 |
| 0.0006 | 68.0 | 136 | 0.9471 | 0.8438 |
| 0.0006 | 69.0 | 138 | 0.9569 | 0.8438 |
| 0.0008 | 70.0 | 140 | 0.9658 | 0.8438 |
| 0.0008 | 71.0 | 142 | 0.9732 | 0.8438 |
| 0.0008 | 72.0 | 144 | 0.9792 | 0.8438 |
| 0.0008 | 73.0 | 146 | 0.9836 | 0.8438 |
| 0.0008 | 74.0 | 148 | 0.9813 | 0.8438 |
| 0.0003 | 75.0 | 150 | 0.9750 | 0.8281 |
| 0.0003 | 76.0 | 152 | 0.9712 | 0.8281 |
| 0.0003 | 77.0 | 154 | 0.9636 | 0.8281 |
| 0.0003 | 78.0 | 156 | 0.9525 | 0.8281 |
| 0.0003 | 79.0 | 158 | 0.9410 | 0.8281 |
| 0.001 | 80.0 | 160 | 0.9323 | 0.8438 |
| 0.001 | 81.0 | 162 | 0.9256 | 0.8438 |
| 0.001 | 82.0 | 164 | 0.9293 | 0.8438 |
| 0.001 | 83.0 | 166 | 0.9429 | 0.8281 |
| 0.001 | 84.0 | 168 | 0.9565 | 0.8281 |
| 0.0002 | 85.0 | 170 | 0.9687 | 0.8281 |
| 0.0002 | 86.0 | 172 | 0.9796 | 0.8281 |
| 0.0002 | 87.0 | 174 | 0.9900 | 0.8281 |
| 0.0002 | 88.0 | 176 | 0.9985 | 0.8281 |
| 0.0002 | 89.0 | 178 | 1.0049 | 0.8281 |
| 0.0002 | 90.0 | 180 | 1.0099 | 0.8281 |
| 0.0002 | 91.0 | 182 | 1.0139 | 0.8281 |
| 0.0002 | 92.0 | 184 | 1.0170 | 0.8281 |
| 0.0002 | 93.0 | 186 | 1.0196 | 0.8281 |
| 0.0002 | 94.0 | 188 | 1.0218 | 0.8281 |
| 0.0002 | 95.0 | 190 | 1.0236 | 0.8281 |
| 0.0002 | 96.0 | 192 | 1.0250 | 0.8281 |
| 0.0002 | 97.0 | 194 | 1.0258 | 0.8281 |
| 0.0002 | 98.0 | 196 | 1.0262 | 0.8281 |
| 0.0002 | 99.0 | 198 | 1.0266 | 0.8281 |
| 0.0002 | 100.0 | 200 | 1.0274 | 0.8281 |
| 0.0002 | 101.0 | 202 | 1.0280 | 0.8281 |
| 0.0002 | 102.0 | 204 | 1.0286 | 0.8281 |
| 0.0002 | 103.0 | 206 | 1.0293 | 0.8281 |
| 0.0002 | 104.0 | 208 | 1.0298 | 0.8281 |
| 0.0001 | 105.0 | 210 | 1.0303 | 0.8281 |
| 0.0001 | 106.0 | 212 | 1.0309 | 0.8281 |
| 0.0001 | 107.0 | 214 | 1.0315 | 0.8281 |
| 0.0001 | 108.0 | 216 | 1.0318 | 0.8281 |
| 0.0001 | 109.0 | 218 | 1.0182 | 0.8281 |
| 0.0025 | 110.0 | 220 | 0.9797 | 0.8281 |
| 0.0025 | 111.0 | 222 | 0.9486 | 0.8438 |
| 0.0025 | 112.0 | 224 | 0.9379 | 0.8594 |
| 0.0025 | 113.0 | 226 | 0.9381 | 0.8594 |
| 0.0025 | 114.0 | 228 | 0.9421 | 0.8594 |
| 0.0002 | 115.0 | 230 | 0.9449 | 0.8594 |
| 0.0002 | 116.0 | 232 | 0.9477 | 0.8594 |
| 0.0002 | 117.0 | 234 | 0.9504 | 0.8594 |
| 0.0002 | 118.0 | 236 | 0.9531 | 0.8594 |
| 0.0002 | 119.0 | 238 | 0.9563 | 0.8594 |
| 0.0002 | 120.0 | 240 | 0.9597 | 0.8438 |
| 0.0002 | 121.0 | 242 | 0.9630 | 0.8438 |
| 0.0002 | 122.0 | 244 | 0.9902 | 0.8438 |
| 0.0002 | 123.0 | 246 | 0.9989 | 0.8438 |
| 0.0002 | 124.0 | 248 | 1.0010 | 0.8281 |
| 0.0007 | 125.0 | 250 | 1.0085 | 0.8438 |
| 0.0007 | 126.0 | 252 | 1.0163 | 0.8438 |
| 0.0007 | 127.0 | 254 | 1.0225 | 0.8438 |
| 0.0007 | 128.0 | 256 | 1.0279 | 0.8594 |
| 0.0007 | 129.0 | 258 | 1.0322 | 0.8594 |
| 0.0001 | 130.0 | 260 | 1.0336 | 0.8594 |
| 0.0001 | 131.0 | 262 | 1.0348 | 0.8594 |
| 0.0001 | 132.0 | 264 | 1.0358 | 0.8594 |
| 0.0001 | 133.0 | 266 | 1.0367 | 0.8594 |
| 0.0001 | 134.0 | 268 | 1.0300 | 0.8438 |
| 0.0005 | 135.0 | 270 | 1.0190 | 0.8438 |
| 0.0005 | 136.0 | 272 | 1.0185 | 0.8281 |
| 0.0005 | 137.0 | 274 | 1.0266 | 0.8438 |
| 0.0005 | 138.0 | 276 | 1.0311 | 0.8438 |
| 0.0005 | 139.0 | 278 | 1.0318 | 0.8438 |
| 0.0001 | 140.0 | 280 | 1.0306 | 0.8438 |
| 0.0001 | 141.0 | 282 | 1.0295 | 0.8281 |
| 0.0001 | 142.0 | 284 | 1.0286 | 0.8438 |
| 0.0001 | 143.0 | 286 | 1.0278 | 0.8438 |
| 0.0001 | 144.0 | 288 | 1.0272 | 0.8438 |
| 0.0001 | 145.0 | 290 | 1.0268 | 0.8438 |
| 0.0001 | 146.0 | 292 | 1.0266 | 0.8438 |
| 0.0001 | 147.0 | 294 | 1.0264 | 0.8438 |
| 0.0001 | 148.0 | 296 | 1.0265 | 0.8438 |
| 0.0001 | 149.0 | 298 | 0.9917 | 0.8594 |
| 0.0002 | 150.0 | 300 | 0.9995 | 0.875 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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