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best_model-yelp_polarity-32-87
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5929
- Accuracy: 0.9375
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 | 0.4518 | 0.9531 |
| No log | 2.0 | 4 | 0.4575 | 0.9531 |
| No log | 3.0 | 6 | 0.4656 | 0.9531 |
| No log | 4.0 | 8 | 0.4755 | 0.9531 |
| 0.3146 | 5.0 | 10 | 0.5047 | 0.9375 |
| 0.3146 | 6.0 | 12 | 0.5491 | 0.9375 |
| 0.3146 | 7.0 | 14 | 0.5854 | 0.9375 |
| 0.3146 | 8.0 | 16 | 0.6060 | 0.9375 |
| 0.3146 | 9.0 | 18 | 0.6187 | 0.9375 |
| 0.2346 | 10.0 | 20 | 0.6304 | 0.9375 |
| 0.2346 | 11.0 | 22 | 0.6336 | 0.9375 |
| 0.2346 | 12.0 | 24 | 0.6359 | 0.9375 |
| 0.2346 | 13.0 | 26 | 0.6345 | 0.9375 |
| 0.2346 | 14.0 | 28 | 0.6333 | 0.9375 |
| 0.0633 | 15.0 | 30 | 0.6349 | 0.9375 |
| 0.0633 | 16.0 | 32 | 0.6359 | 0.9375 |
| 0.0633 | 17.0 | 34 | 0.6298 | 0.9375 |
| 0.0633 | 18.0 | 36 | 0.6191 | 0.9375 |
| 0.0633 | 19.0 | 38 | 0.6057 | 0.9375 |
| 0.0003 | 20.0 | 40 | 0.5963 | 0.9375 |
| 0.0003 | 21.0 | 42 | 0.5988 | 0.9375 |
| 0.0003 | 22.0 | 44 | 0.6050 | 0.9375 |
| 0.0003 | 23.0 | 46 | 0.6098 | 0.9375 |
| 0.0003 | 24.0 | 48 | 0.6134 | 0.9375 |
| 0.0 | 25.0 | 50 | 0.6160 | 0.9375 |
| 0.0 | 26.0 | 52 | 0.6177 | 0.9375 |
| 0.0 | 27.0 | 54 | 0.6187 | 0.9375 |
| 0.0 | 28.0 | 56 | 0.6190 | 0.9375 |
| 0.0 | 29.0 | 58 | 0.6189 | 0.9375 |
| 0.0 | 30.0 | 60 | 0.6186 | 0.9375 |
| 0.0 | 31.0 | 62 | 0.6179 | 0.9375 |
| 0.0 | 32.0 | 64 | 0.6172 | 0.9375 |
| 0.0 | 33.0 | 66 | 0.6161 | 0.9375 |
| 0.0 | 34.0 | 68 | 0.6151 | 0.9375 |
| 0.0 | 35.0 | 70 | 0.6140 | 0.9375 |
| 0.0 | 36.0 | 72 | 0.6128 | 0.9375 |
| 0.0 | 37.0 | 74 | 0.6116 | 0.9375 |
| 0.0 | 38.0 | 76 | 0.6104 | 0.9375 |
| 0.0 | 39.0 | 78 | 0.6091 | 0.9375 |
| 0.0 | 40.0 | 80 | 0.6079 | 0.9375 |
| 0.0 | 41.0 | 82 | 0.6066 | 0.9375 |
| 0.0 | 42.0 | 84 | 0.6054 | 0.9375 |
| 0.0 | 43.0 | 86 | 0.6041 | 0.9375 |
| 0.0 | 44.0 | 88 | 0.6029 | 0.9375 |
| 0.0 | 45.0 | 90 | 0.6018 | 0.9375 |
| 0.0 | 46.0 | 92 | 0.6006 | 0.9375 |
| 0.0 | 47.0 | 94 | 0.5996 | 0.9375 |
| 0.0 | 48.0 | 96 | 0.5986 | 0.9375 |
| 0.0 | 49.0 | 98 | 0.5977 | 0.9375 |
| 0.0 | 50.0 | 100 | 0.5968 | 0.9375 |
| 0.0 | 51.0 | 102 | 0.5959 | 0.9375 |
| 0.0 | 52.0 | 104 | 0.5951 | 0.9375 |
| 0.0 | 53.0 | 106 | 0.5943 | 0.9375 |
| 0.0 | 54.0 | 108 | 0.5937 | 0.9375 |
| 0.0 | 55.0 | 110 | 0.5931 | 0.9375 |
| 0.0 | 56.0 | 112 | 0.5925 | 0.9375 |
| 0.0 | 57.0 | 114 | 0.5918 | 0.9375 |
| 0.0 | 58.0 | 116 | 0.5912 | 0.9375 |
| 0.0 | 59.0 | 118 | 0.5906 | 0.9375 |
| 0.0 | 60.0 | 120 | 0.5899 | 0.9375 |
| 0.0 | 61.0 | 122 | 0.5894 | 0.9375 |
| 0.0 | 62.0 | 124 | 0.5890 | 0.9375 |
| 0.0 | 63.0 | 126 | 0.5886 | 0.9375 |
| 0.0 | 64.0 | 128 | 0.5881 | 0.9375 |
| 0.0 | 65.0 | 130 | 0.5878 | 0.9375 |
| 0.0 | 66.0 | 132 | 0.5874 | 0.9375 |
| 0.0 | 67.0 | 134 | 0.5872 | 0.9375 |
| 0.0 | 68.0 | 136 | 0.5871 | 0.9375 |
| 0.0 | 69.0 | 138 | 0.5872 | 0.9375 |
| 0.0 | 70.0 | 140 | 0.5871 | 0.9375 |
| 0.0 | 71.0 | 142 | 0.5872 | 0.9375 |
| 0.0 | 72.0 | 144 | 0.5872 | 0.9375 |
| 0.0 | 73.0 | 146 | 0.5872 | 0.9375 |
| 0.0 | 74.0 | 148 | 0.5873 | 0.9375 |
| 0.0 | 75.0 | 150 | 0.5873 | 0.9375 |
| 0.0 | 76.0 | 152 | 0.5875 | 0.9375 |
| 0.0 | 77.0 | 154 | 0.5875 | 0.9375 |
| 0.0 | 78.0 | 156 | 0.5876 | 0.9375 |
| 0.0 | 79.0 | 158 | 0.5877 | 0.9375 |
| 0.0 | 80.0 | 160 | 0.5879 | 0.9375 |
| 0.0 | 81.0 | 162 | 0.5881 | 0.9375 |
| 0.0 | 82.0 | 164 | 0.5883 | 0.9375 |
| 0.0 | 83.0 | 166 | 0.5884 | 0.9375 |
| 0.0 | 84.0 | 168 | 0.5885 | 0.9375 |
| 0.0 | 85.0 | 170 | 0.5887 | 0.9375 |
| 0.0 | 86.0 | 172 | 0.5887 | 0.9375 |
| 0.0 | 87.0 | 174 | 0.5886 | 0.9375 |
| 0.0 | 88.0 | 176 | 0.5888 | 0.9375 |
| 0.0 | 89.0 | 178 | 0.5887 | 0.9375 |
| 0.0 | 90.0 | 180 | 0.5885 | 0.9375 |
| 0.0 | 91.0 | 182 | 0.5884 | 0.9375 |
| 0.0 | 92.0 | 184 | 0.5882 | 0.9375 |
| 0.0 | 93.0 | 186 | 0.5880 | 0.9375 |
| 0.0 | 94.0 | 188 | 0.5879 | 0.9375 |
| 0.0 | 95.0 | 190 | 0.5878 | 0.9375 |
| 0.0 | 96.0 | 192 | 0.5876 | 0.9375 |
| 0.0 | 97.0 | 194 | 0.5874 | 0.9375 |
| 0.0 | 98.0 | 196 | 0.5873 | 0.9375 |
| 0.0 | 99.0 | 198 | 0.5872 | 0.9375 |
| 0.0 | 100.0 | 200 | 0.5870 | 0.9375 |
| 0.0 | 101.0 | 202 | 0.5870 | 0.9375 |
| 0.0 | 102.0 | 204 | 0.5870 | 0.9375 |
| 0.0 | 103.0 | 206 | 0.5868 | 0.9375 |
| 0.0 | 104.0 | 208 | 0.5866 | 0.9375 |
| 0.0 | 105.0 | 210 | 0.5866 | 0.9375 |
| 0.0 | 106.0 | 212 | 0.5867 | 0.9375 |
| 0.0 | 107.0 | 214 | 0.5867 | 0.9375 |
| 0.0 | 108.0 | 216 | 0.5867 | 0.9375 |
| 0.0 | 109.0 | 218 | 0.5867 | 0.9375 |
| 0.0 | 110.0 | 220 | 0.5869 | 0.9375 |
| 0.0 | 111.0 | 222 | 0.5870 | 0.9375 |
| 0.0 | 112.0 | 224 | 0.5870 | 0.9375 |
| 0.0 | 113.0 | 226 | 0.5872 | 0.9375 |
| 0.0 | 114.0 | 228 | 0.5877 | 0.9375 |
| 0.0 | 115.0 | 230 | 0.5881 | 0.9375 |
| 0.0 | 116.0 | 232 | 0.5885 | 0.9375 |
| 0.0 | 117.0 | 234 | 0.5888 | 0.9375 |
| 0.0 | 118.0 | 236 | 0.5891 | 0.9375 |
| 0.0 | 119.0 | 238 | 0.5894 | 0.9375 |
| 0.0 | 120.0 | 240 | 0.5897 | 0.9375 |
| 0.0 | 121.0 | 242 | 0.5899 | 0.9375 |
| 0.0 | 122.0 | 244 | 0.5900 | 0.9375 |
| 0.0 | 123.0 | 246 | 0.5901 | 0.9375 |
| 0.0 | 124.0 | 248 | 0.5903 | 0.9375 |
| 0.0 | 125.0 | 250 | 0.5903 | 0.9375 |
| 0.0 | 126.0 | 252 | 0.5906 | 0.9375 |
| 0.0 | 127.0 | 254 | 0.5908 | 0.9375 |
| 0.0 | 128.0 | 256 | 0.5909 | 0.9375 |
| 0.0 | 129.0 | 258 | 0.5911 | 0.9375 |
| 0.0 | 130.0 | 260 | 0.5914 | 0.9375 |
| 0.0 | 131.0 | 262 | 0.5916 | 0.9375 |
| 0.0 | 132.0 | 264 | 0.5918 | 0.9375 |
| 0.0 | 133.0 | 266 | 0.5922 | 0.9375 |
| 0.0 | 134.0 | 268 | 0.5922 | 0.9375 |
| 0.0 | 135.0 | 270 | 0.5924 | 0.9375 |
| 0.0 | 136.0 | 272 | 0.5925 | 0.9375 |
| 0.0 | 137.0 | 274 | 0.5923 | 0.9375 |
| 0.0 | 138.0 | 276 | 0.5923 | 0.9375 |
| 0.0 | 139.0 | 278 | 0.5922 | 0.9375 |
| 0.0 | 140.0 | 280 | 0.5920 | 0.9375 |
| 0.0 | 141.0 | 282 | 0.5921 | 0.9375 |
| 0.0 | 142.0 | 284 | 0.5922 | 0.9375 |
| 0.0 | 143.0 | 286 | 0.5921 | 0.9375 |
| 0.0 | 144.0 | 288 | 0.5921 | 0.9375 |
| 0.0 | 145.0 | 290 | 0.5922 | 0.9375 |
| 0.0 | 146.0 | 292 | 0.5924 | 0.9375 |
| 0.0 | 147.0 | 294 | 0.5926 | 0.9375 |
| 0.0 | 148.0 | 296 | 0.5927 | 0.9375 |
| 0.0 | 149.0 | 298 | 0.5929 | 0.9375 |
| 0.0 | 150.0 | 300 | 0.5929 | 0.9375 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
