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bert-base-uncased-sst-2-32-42
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1901
- 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 | 1.0945 | 0.8438 |
| No log | 2.0 | 4 | 1.0962 | 0.8438 |
| No log | 3.0 | 6 | 1.0975 | 0.8438 |
| No log | 4.0 | 8 | 1.0969 | 0.8438 |
| 0.3232 | 5.0 | 10 | 1.0974 | 0.8438 |
| 0.3232 | 6.0 | 12 | 1.0999 | 0.8438 |
| 0.3232 | 7.0 | 14 | 1.1032 | 0.8438 |
| 0.3232 | 8.0 | 16 | 1.1075 | 0.8438 |
| 0.3232 | 9.0 | 18 | 1.1108 | 0.8438 |
| 0.2269 | 10.0 | 20 | 1.1140 | 0.8438 |
| 0.2269 | 11.0 | 22 | 1.1163 | 0.8438 |
| 0.2269 | 12.0 | 24 | 1.1175 | 0.8438 |
| 0.2269 | 13.0 | 26 | 1.1187 | 0.8438 |
| 0.2269 | 14.0 | 28 | 1.1226 | 0.8438 |
| 0.2217 | 15.0 | 30 | 1.1271 | 0.8438 |
| 0.2217 | 16.0 | 32 | 1.1355 | 0.8281 |
| 0.2217 | 17.0 | 34 | 1.1405 | 0.8281 |
| 0.2217 | 18.0 | 36 | 1.1476 | 0.8281 |
| 0.2217 | 19.0 | 38 | 1.1562 | 0.8281 |
| 0.1358 | 20.0 | 40 | 1.1569 | 0.8281 |
| 0.1358 | 21.0 | 42 | 1.1629 | 0.8281 |
| 0.1358 | 22.0 | 44 | 1.1726 | 0.8281 |
| 0.1358 | 23.0 | 46 | 1.1825 | 0.8281 |
| 0.1358 | 24.0 | 48 | 1.1896 | 0.8281 |
| 0.0945 | 25.0 | 50 | 1.1960 | 0.8281 |
| 0.0945 | 26.0 | 52 | 1.1980 | 0.8281 |
| 0.0945 | 27.0 | 54 | 1.2014 | 0.8281 |
| 0.0945 | 28.0 | 56 | 1.2004 | 0.8281 |
| 0.0945 | 29.0 | 58 | 1.1992 | 0.8281 |
| 0.0102 | 30.0 | 60 | 1.2000 | 0.8281 |
| 0.0102 | 31.0 | 62 | 1.1996 | 0.8281 |
| 0.0102 | 32.0 | 64 | 1.1994 | 0.8281 |
| 0.0102 | 33.0 | 66 | 1.1984 | 0.8281 |
| 0.0102 | 34.0 | 68 | 1.1975 | 0.8281 |
| 0.0004 | 35.0 | 70 | 1.1966 | 0.8281 |
| 0.0004 | 36.0 | 72 | 1.1971 | 0.8281 |
| 0.0004 | 37.0 | 74 | 1.1918 | 0.8281 |
| 0.0004 | 38.0 | 76 | 1.1864 | 0.8281 |
| 0.0004 | 39.0 | 78 | 1.1858 | 0.8281 |
| 0.0028 | 40.0 | 80 | 1.1883 | 0.8281 |
| 0.0028 | 41.0 | 82 | 1.1933 | 0.8281 |
| 0.0028 | 42.0 | 84 | 1.2003 | 0.8281 |
| 0.0028 | 43.0 | 86 | 1.2063 | 0.8281 |
| 0.0028 | 44.0 | 88 | 1.2014 | 0.8281 |
| 0.0092 | 45.0 | 90 | 1.1965 | 0.8281 |
| 0.0092 | 46.0 | 92 | 1.1912 | 0.8281 |
| 0.0092 | 47.0 | 94 | 1.1926 | 0.8281 |
| 0.0092 | 48.0 | 96 | 1.1938 | 0.8281 |
| 0.0092 | 49.0 | 98 | 1.1984 | 0.8281 |
| 0.0006 | 50.0 | 100 | 1.1990 | 0.8281 |
| 0.0006 | 51.0 | 102 | 1.2001 | 0.8281 |
| 0.0006 | 52.0 | 104 | 1.2040 | 0.8281 |
| 0.0006 | 53.0 | 106 | 1.2078 | 0.8281 |
| 0.0006 | 54.0 | 108 | 1.2113 | 0.8281 |
| 0.0003 | 55.0 | 110 | 1.2143 | 0.8281 |
| 0.0003 | 56.0 | 112 | 1.2169 | 0.8281 |
| 0.0003 | 57.0 | 114 | 1.2028 | 0.8281 |
| 0.0003 | 58.0 | 116 | 1.1896 | 0.8281 |
| 0.0003 | 59.0 | 118 | 1.1822 | 0.8281 |
| 0.0153 | 60.0 | 120 | 1.1789 | 0.8281 |
| 0.0153 | 61.0 | 122 | 1.1781 | 0.8281 |
| 0.0153 | 62.0 | 124 | 1.1776 | 0.8281 |
| 0.0153 | 63.0 | 126 | 1.1771 | 0.8281 |
| 0.0153 | 64.0 | 128 | 1.1767 | 0.8281 |
| 0.0003 | 65.0 | 130 | 1.1767 | 0.8281 |
| 0.0003 | 66.0 | 132 | 1.1771 | 0.8281 |
| 0.0003 | 67.0 | 134 | 1.1775 | 0.8281 |
| 0.0003 | 68.0 | 136 | 1.1780 | 0.8281 |
| 0.0003 | 69.0 | 138 | 1.1786 | 0.8281 |
| 0.0002 | 70.0 | 140 | 1.1807 | 0.8281 |
| 0.0002 | 71.0 | 142 | 1.1831 | 0.8281 |
| 0.0002 | 72.0 | 144 | 1.1852 | 0.8281 |
| 0.0002 | 73.0 | 146 | 1.1872 | 0.8281 |
| 0.0002 | 74.0 | 148 | 1.1889 | 0.8281 |
| 0.0068 | 75.0 | 150 | 1.2038 | 0.8281 |
| 0.0068 | 76.0 | 152 | 1.2080 | 0.8281 |
| 0.0068 | 77.0 | 154 | 1.2113 | 0.8281 |
| 0.0068 | 78.0 | 156 | 1.2141 | 0.8281 |
| 0.0068 | 79.0 | 158 | 1.2226 | 0.8281 |
| 0.0003 | 80.0 | 160 | 1.2292 | 0.8281 |
| 0.0003 | 81.0 | 162 | 1.2347 | 0.8281 |
| 0.0003 | 82.0 | 164 | 1.2393 | 0.8281 |
| 0.0003 | 83.0 | 166 | 1.2436 | 0.8281 |
| 0.0003 | 84.0 | 168 | 1.2472 | 0.8281 |
| 0.0002 | 85.0 | 170 | 1.2502 | 0.8281 |
| 0.0002 | 86.0 | 172 | 1.2531 | 0.8281 |
| 0.0002 | 87.0 | 174 | 1.2556 | 0.8281 |
| 0.0002 | 88.0 | 176 | 1.2579 | 0.8281 |
| 0.0002 | 89.0 | 178 | 1.2599 | 0.8281 |
| 0.0002 | 90.0 | 180 | 1.2614 | 0.8281 |
| 0.0002 | 91.0 | 182 | 1.2626 | 0.8281 |
| 0.0002 | 92.0 | 184 | 1.2639 | 0.8281 |
| 0.0002 | 93.0 | 186 | 1.2649 | 0.8281 |
| 0.0002 | 94.0 | 188 | 1.2657 | 0.8281 |
| 0.0002 | 95.0 | 190 | 1.2663 | 0.8281 |
| 0.0002 | 96.0 | 192 | 1.2670 | 0.8281 |
| 0.0002 | 97.0 | 194 | 1.2676 | 0.8281 |
| 0.0002 | 98.0 | 196 | 1.2682 | 0.8281 |
| 0.0002 | 99.0 | 198 | 1.2689 | 0.8281 |
| 0.0002 | 100.0 | 200 | 1.2691 | 0.8281 |
| 0.0002 | 101.0 | 202 | 1.2691 | 0.8281 |
| 0.0002 | 102.0 | 204 | 1.2692 | 0.8281 |
| 0.0002 | 103.0 | 206 | 1.2689 | 0.8281 |
| 0.0002 | 104.0 | 208 | 1.2687 | 0.8281 |
| 0.0002 | 105.0 | 210 | 1.2686 | 0.8281 |
| 0.0002 | 106.0 | 212 | 1.2684 | 0.8281 |
| 0.0002 | 107.0 | 214 | 1.2683 | 0.8281 |
| 0.0002 | 108.0 | 216 | 1.2680 | 0.8281 |
| 0.0002 | 109.0 | 218 | 1.2681 | 0.8281 |
| 0.0002 | 110.0 | 220 | 1.2683 | 0.8281 |
| 0.0002 | 111.0 | 222 | 1.2685 | 0.8281 |
| 0.0002 | 112.0 | 224 | 1.2688 | 0.8281 |
| 0.0002 | 113.0 | 226 | 1.2690 | 0.8281 |
| 0.0002 | 114.0 | 228 | 1.2694 | 0.8281 |
| 0.0001 | 115.0 | 230 | 1.2697 | 0.8281 |
| 0.0001 | 116.0 | 232 | 1.2700 | 0.8281 |
| 0.0001 | 117.0 | 234 | 1.2701 | 0.8281 |
| 0.0001 | 118.0 | 236 | 1.2702 | 0.8281 |
| 0.0001 | 119.0 | 238 | 1.2701 | 0.8281 |
| 0.0001 | 120.0 | 240 | 1.2699 | 0.8281 |
| 0.0001 | 121.0 | 242 | 1.2691 | 0.8281 |
| 0.0001 | 122.0 | 244 | 1.2680 | 0.8281 |
| 0.0001 | 123.0 | 246 | 1.2673 | 0.8281 |
| 0.0001 | 124.0 | 248 | 1.2668 | 0.8281 |
| 0.0001 | 125.0 | 250 | 1.2666 | 0.8281 |
| 0.0001 | 126.0 | 252 | 1.2665 | 0.8281 |
| 0.0001 | 127.0 | 254 | 1.2666 | 0.8281 |
| 0.0001 | 128.0 | 256 | 1.2669 | 0.8281 |
| 0.0001 | 129.0 | 258 | 1.2675 | 0.8281 |
| 0.0001 | 130.0 | 260 | 1.2682 | 0.8281 |
| 0.0001 | 131.0 | 262 | 1.2686 | 0.8281 |
| 0.0001 | 132.0 | 264 | 1.2690 | 0.8281 |
| 0.0001 | 133.0 | 266 | 1.2695 | 0.8281 |
| 0.0001 | 134.0 | 268 | 1.2701 | 0.8281 |
| 0.0001 | 135.0 | 270 | 1.2712 | 0.8281 |
| 0.0001 | 136.0 | 272 | 1.2735 | 0.8281 |
| 0.0001 | 137.0 | 274 | 1.2757 | 0.8281 |
| 0.0001 | 138.0 | 276 | 1.2675 | 0.8281 |
| 0.0001 | 139.0 | 278 | 1.2518 | 0.8281 |
| 0.0009 | 140.0 | 280 | 1.2009 | 0.8438 |
| 0.0009 | 141.0 | 282 | 1.1175 | 0.8594 |
| 0.0009 | 142.0 | 284 | 1.1144 | 0.8594 |
| 0.0009 | 143.0 | 286 | 1.1362 | 0.8594 |
| 0.0009 | 144.0 | 288 | 1.1667 | 0.8438 |
| 0.0002 | 145.0 | 290 | 1.1906 | 0.8438 |
| 0.0002 | 146.0 | 292 | 1.2067 | 0.8438 |
| 0.0002 | 147.0 | 294 | 1.2182 | 0.8438 |
| 0.0002 | 148.0 | 296 | 1.2263 | 0.8438 |
| 0.0002 | 149.0 | 298 | 1.2321 | 0.8438 |
| 0.0004 | 150.0 | 300 | 1.1901 | 0.8438 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
