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best_model-yelp_polarity-64-21
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: 0.6300
- 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 | 4 | 0.6653 | 0.9297 |
| No log | 2.0 | 8 | 0.6599 | 0.9375 |
| 0.3506 | 3.0 | 12 | 0.6517 | 0.9375 |
| 0.3506 | 4.0 | 16 | 0.6448 | 0.9375 |
| 0.4992 | 5.0 | 20 | 0.6507 | 0.9375 |
| 0.4992 | 6.0 | 24 | 0.6967 | 0.9219 |
| 0.4992 | 7.0 | 28 | 0.7602 | 0.9141 |
| 0.3039 | 8.0 | 32 | 0.9351 | 0.8984 |
| 0.3039 | 9.0 | 36 | 0.9244 | 0.8984 |
| 0.2241 | 10.0 | 40 | 0.7974 | 0.9062 |
| 0.2241 | 11.0 | 44 | 0.7229 | 0.9219 |
| 0.2241 | 12.0 | 48 | 0.6981 | 0.9219 |
| 0.1025 | 13.0 | 52 | 0.6961 | 0.9219 |
| 0.1025 | 14.0 | 56 | 0.6819 | 0.9219 |
| 0.1057 | 15.0 | 60 | 0.6655 | 0.9219 |
| 0.1057 | 16.0 | 64 | 0.6463 | 0.9219 |
| 0.1057 | 17.0 | 68 | 0.6240 | 0.9219 |
| 0.0733 | 18.0 | 72 | 0.6086 | 0.9141 |
| 0.0733 | 19.0 | 76 | 0.6109 | 0.9141 |
| 0.0366 | 20.0 | 80 | 0.6219 | 0.9141 |
| 0.0366 | 21.0 | 84 | 0.6291 | 0.9141 |
| 0.0366 | 22.0 | 88 | 0.6227 | 0.9219 |
| 0.0449 | 23.0 | 92 | 0.6182 | 0.9219 |
| 0.0449 | 24.0 | 96 | 0.6148 | 0.9219 |
| 0.0188 | 25.0 | 100 | 0.5999 | 0.9219 |
| 0.0188 | 26.0 | 104 | 0.5537 | 0.9297 |
| 0.0188 | 27.0 | 108 | 0.5538 | 0.9297 |
| 0.0146 | 28.0 | 112 | 0.5492 | 0.9297 |
| 0.0146 | 29.0 | 116 | 0.5275 | 0.9297 |
| 0.0131 | 30.0 | 120 | 0.5212 | 0.9219 |
| 0.0131 | 31.0 | 124 | 0.5486 | 0.9219 |
| 0.0131 | 32.0 | 128 | 0.5641 | 0.9141 |
| 0.0074 | 33.0 | 132 | 0.5489 | 0.9219 |
| 0.0074 | 34.0 | 136 | 0.5426 | 0.9219 |
| 0.0042 | 35.0 | 140 | 0.5468 | 0.9141 |
| 0.0042 | 36.0 | 144 | 0.5411 | 0.9141 |
| 0.0042 | 37.0 | 148 | 0.5366 | 0.9219 |
| 0.0027 | 38.0 | 152 | 0.5306 | 0.9219 |
| 0.0027 | 39.0 | 156 | 0.5182 | 0.9219 |
| 0.0011 | 40.0 | 160 | 0.5096 | 0.9219 |
| 0.0011 | 41.0 | 164 | 0.5059 | 0.9219 |
| 0.0011 | 42.0 | 168 | 0.5130 | 0.9219 |
| 0.0007 | 43.0 | 172 | 0.5198 | 0.9219 |
| 0.0007 | 44.0 | 176 | 0.5172 | 0.9219 |
| 0.0007 | 45.0 | 180 | 0.5129 | 0.9219 |
| 0.0007 | 46.0 | 184 | 0.5337 | 0.9062 |
| 0.0007 | 47.0 | 188 | 0.5600 | 0.9141 |
| 0.0003 | 48.0 | 192 | 0.5687 | 0.9141 |
| 0.0003 | 49.0 | 196 | 0.5413 | 0.9141 |
| 0.0003 | 50.0 | 200 | 0.5270 | 0.9062 |
| 0.0003 | 51.0 | 204 | 0.5249 | 0.9141 |
| 0.0003 | 52.0 | 208 | 0.5315 | 0.9141 |
| 0.0002 | 53.0 | 212 | 0.5528 | 0.9141 |
| 0.0002 | 54.0 | 216 | 0.5732 | 0.9141 |
| 0.0001 | 55.0 | 220 | 0.5812 | 0.9141 |
| 0.0001 | 56.0 | 224 | 0.5871 | 0.9141 |
| 0.0001 | 57.0 | 228 | 0.5854 | 0.9141 |
| 0.0001 | 58.0 | 232 | 0.5846 | 0.9141 |
| 0.0001 | 59.0 | 236 | 0.5842 | 0.9141 |
| 0.0 | 60.0 | 240 | 0.5865 | 0.9141 |
| 0.0 | 61.0 | 244 | 0.5895 | 0.9141 |
| 0.0 | 62.0 | 248 | 0.5908 | 0.9141 |
| 0.0001 | 63.0 | 252 | 0.5911 | 0.9141 |
| 0.0001 | 64.0 | 256 | 0.5905 | 0.9141 |
| 0.0 | 65.0 | 260 | 0.5870 | 0.9141 |
| 0.0 | 66.0 | 264 | 0.5859 | 0.9141 |
| 0.0 | 67.0 | 268 | 0.5863 | 0.9141 |
| 0.0 | 68.0 | 272 | 0.5881 | 0.9141 |
| 0.0 | 69.0 | 276 | 0.5888 | 0.9141 |
| 0.0 | 70.0 | 280 | 0.5902 | 0.9141 |
| 0.0 | 71.0 | 284 | 0.5926 | 0.9141 |
| 0.0 | 72.0 | 288 | 0.5945 | 0.9141 |
| 0.0 | 73.0 | 292 | 0.5949 | 0.9141 |
| 0.0 | 74.0 | 296 | 0.5962 | 0.9141 |
| 0.0 | 75.0 | 300 | 0.5982 | 0.9141 |
| 0.0 | 76.0 | 304 | 0.6003 | 0.9141 |
| 0.0 | 77.0 | 308 | 0.6014 | 0.9141 |
| 0.0 | 78.0 | 312 | 0.6018 | 0.9219 |
| 0.0 | 79.0 | 316 | 0.6024 | 0.9219 |
| 0.0 | 80.0 | 320 | 0.6037 | 0.9219 |
| 0.0 | 81.0 | 324 | 0.6041 | 0.9219 |
| 0.0 | 82.0 | 328 | 0.6052 | 0.9219 |
| 0.0 | 83.0 | 332 | 0.6064 | 0.9219 |
| 0.0 | 84.0 | 336 | 0.6069 | 0.9219 |
| 0.0 | 85.0 | 340 | 0.6069 | 0.9219 |
| 0.0 | 86.0 | 344 | 0.6074 | 0.9219 |
| 0.0 | 87.0 | 348 | 0.6089 | 0.9219 |
| 0.0 | 88.0 | 352 | 0.6098 | 0.9219 |
| 0.0 | 89.0 | 356 | 0.6098 | 0.9219 |
| 0.0 | 90.0 | 360 | 0.6100 | 0.9219 |
| 0.0 | 91.0 | 364 | 0.6098 | 0.9219 |
| 0.0 | 92.0 | 368 | 0.6098 | 0.9219 |
| 0.0 | 93.0 | 372 | 0.6101 | 0.9219 |
| 0.0 | 94.0 | 376 | 0.6111 | 0.9219 |
| 0.0 | 95.0 | 380 | 0.6122 | 0.9219 |
| 0.0 | 96.0 | 384 | 0.6131 | 0.9219 |
| 0.0 | 97.0 | 388 | 0.6122 | 0.9219 |
| 0.0 | 98.0 | 392 | 0.6127 | 0.9219 |
| 0.0 | 99.0 | 396 | 0.6124 | 0.9219 |
| 0.0 | 100.0 | 400 | 0.6120 | 0.9219 |
| 0.0 | 101.0 | 404 | 0.6127 | 0.9219 |
| 0.0 | 102.0 | 408 | 0.6132 | 0.9219 |
| 0.0 | 103.0 | 412 | 0.6140 | 0.9219 |
| 0.0 | 104.0 | 416 | 0.6150 | 0.9219 |
| 0.0 | 105.0 | 420 | 0.6158 | 0.9219 |
| 0.0 | 106.0 | 424 | 0.6160 | 0.9219 |
| 0.0 | 107.0 | 428 | 0.6161 | 0.9219 |
| 0.0 | 108.0 | 432 | 0.6166 | 0.9219 |
| 0.0 | 109.0 | 436 | 0.6168 | 0.9219 |
| 0.0 | 110.0 | 440 | 0.6170 | 0.9219 |
| 0.0 | 111.0 | 444 | 0.6178 | 0.9219 |
| 0.0 | 112.0 | 448 | 0.6184 | 0.9219 |
| 0.0 | 113.0 | 452 | 0.6189 | 0.9219 |
| 0.0 | 114.0 | 456 | 0.6197 | 0.9219 |
| 0.0 | 115.0 | 460 | 0.6213 | 0.9219 |
| 0.0 | 116.0 | 464 | 0.6220 | 0.9219 |
| 0.0 | 117.0 | 468 | 0.6226 | 0.9219 |
| 0.0 | 118.0 | 472 | 0.6229 | 0.9219 |
| 0.0 | 119.0 | 476 | 0.6235 | 0.9219 |
| 0.0 | 120.0 | 480 | 0.6219 | 0.9219 |
| 0.0 | 121.0 | 484 | 0.6219 | 0.9219 |
| 0.0 | 122.0 | 488 | 0.6223 | 0.9219 |
| 0.0 | 123.0 | 492 | 0.6236 | 0.9219 |
| 0.0 | 124.0 | 496 | 0.6246 | 0.9219 |
| 0.0 | 125.0 | 500 | 0.6259 | 0.9219 |
| 0.0 | 126.0 | 504 | 0.6265 | 0.9219 |
| 0.0 | 127.0 | 508 | 0.6270 | 0.9219 |
| 0.0 | 128.0 | 512 | 0.6272 | 0.9219 |
| 0.0 | 129.0 | 516 | 0.6271 | 0.9219 |
| 0.0 | 130.0 | 520 | 0.6262 | 0.9219 |
| 0.0 | 131.0 | 524 | 0.6257 | 0.9219 |
| 0.0 | 132.0 | 528 | 0.6255 | 0.9219 |
| 0.0 | 133.0 | 532 | 0.6258 | 0.9219 |
| 0.0 | 134.0 | 536 | 0.6262 | 0.9219 |
| 0.0 | 135.0 | 540 | 0.6272 | 0.9219 |
| 0.0 | 136.0 | 544 | 0.6277 | 0.9219 |
| 0.0 | 137.0 | 548 | 0.6286 | 0.9219 |
| 0.0 | 138.0 | 552 | 0.6288 | 0.9219 |
| 0.0 | 139.0 | 556 | 0.6292 | 0.9219 |
| 0.0 | 140.0 | 560 | 0.6295 | 0.9219 |
| 0.0 | 141.0 | 564 | 0.6293 | 0.9219 |
| 0.0 | 142.0 | 568 | 0.6294 | 0.9219 |
| 0.0 | 143.0 | 572 | 0.6296 | 0.9219 |
| 0.0 | 144.0 | 576 | 0.6299 | 0.9219 |
| 0.0 | 145.0 | 580 | 0.6297 | 0.9219 |
| 0.0 | 146.0 | 584 | 0.6299 | 0.9219 |
| 0.0 | 147.0 | 588 | 0.6300 | 0.9219 |
| 0.0 | 148.0 | 592 | 0.6300 | 0.9219 |
| 0.0 | 149.0 | 596 | 0.6300 | 0.9219 |
| 0.0 | 150.0 | 600 | 0.6300 | 0.9219 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
