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---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-87
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. -->
# best_model-yelp_polarity-64-87
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5142
- 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 | 0.4813 | 0.9453 |
| No log | 2.0 | 8 | 0.4523 | 0.9531 |
| 0.996 | 3.0 | 12 | 0.4366 | 0.9453 |
| 0.996 | 4.0 | 16 | 0.4239 | 0.9531 |
| 0.8647 | 5.0 | 20 | 0.4191 | 0.9531 |
| 0.8647 | 6.0 | 24 | 0.4066 | 0.9531 |
| 0.8647 | 7.0 | 28 | 0.4268 | 0.9531 |
| 0.6876 | 8.0 | 32 | 0.5275 | 0.9453 |
| 0.6876 | 9.0 | 36 | 0.6025 | 0.9453 |
| 0.5833 | 10.0 | 40 | 0.6144 | 0.9453 |
| 0.5833 | 11.0 | 44 | 0.6062 | 0.9453 |
| 0.5833 | 12.0 | 48 | 0.5946 | 0.9453 |
| 0.4071 | 13.0 | 52 | 0.5677 | 0.9453 |
| 0.4071 | 14.0 | 56 | 0.5733 | 0.9453 |
| 0.2545 | 15.0 | 60 | 0.5830 | 0.9453 |
| 0.2545 | 16.0 | 64 | 0.5768 | 0.9453 |
| 0.2545 | 17.0 | 68 | 0.5639 | 0.9453 |
| 0.1255 | 18.0 | 72 | 0.5467 | 0.9453 |
| 0.1255 | 19.0 | 76 | 0.5185 | 0.9453 |
| 0.1119 | 20.0 | 80 | 0.4410 | 0.9453 |
| 0.1119 | 21.0 | 84 | 0.4174 | 0.9531 |
| 0.1119 | 22.0 | 88 | 0.4014 | 0.9453 |
| 0.0568 | 23.0 | 92 | 0.4155 | 0.9531 |
| 0.0568 | 24.0 | 96 | 0.4084 | 0.9375 |
| 0.0295 | 25.0 | 100 | 0.3999 | 0.9297 |
| 0.0295 | 26.0 | 104 | 0.4070 | 0.9219 |
| 0.0295 | 27.0 | 108 | 0.4131 | 0.9219 |
| 0.0226 | 28.0 | 112 | 0.4255 | 0.9219 |
| 0.0226 | 29.0 | 116 | 0.4287 | 0.9219 |
| 0.0197 | 30.0 | 120 | 0.4395 | 0.9297 |
| 0.0197 | 31.0 | 124 | 0.4473 | 0.9297 |
| 0.0197 | 32.0 | 128 | 0.4604 | 0.9297 |
| 0.0161 | 33.0 | 132 | 0.4653 | 0.9297 |
| 0.0161 | 34.0 | 136 | 0.4682 | 0.9297 |
| 0.0114 | 35.0 | 140 | 0.4805 | 0.9297 |
| 0.0114 | 36.0 | 144 | 0.4598 | 0.9297 |
| 0.0114 | 37.0 | 148 | 0.4290 | 0.9297 |
| 0.0054 | 38.0 | 152 | 0.4322 | 0.9297 |
| 0.0054 | 39.0 | 156 | 0.4623 | 0.9219 |
| 0.0039 | 40.0 | 160 | 0.4877 | 0.9297 |
| 0.0039 | 41.0 | 164 | 0.4887 | 0.9297 |
| 0.0039 | 42.0 | 168 | 0.4805 | 0.9297 |
| 0.0003 | 43.0 | 172 | 0.4766 | 0.9219 |
| 0.0003 | 44.0 | 176 | 0.4759 | 0.9297 |
| 0.0 | 45.0 | 180 | 0.4779 | 0.9297 |
| 0.0 | 46.0 | 184 | 0.4799 | 0.9219 |
| 0.0 | 47.0 | 188 | 0.4816 | 0.9219 |
| 0.0 | 48.0 | 192 | 0.4829 | 0.9219 |
| 0.0 | 49.0 | 196 | 0.4841 | 0.9219 |
| 0.0 | 50.0 | 200 | 0.4850 | 0.9219 |
| 0.0 | 51.0 | 204 | 0.4859 | 0.9219 |
| 0.0 | 52.0 | 208 | 0.4867 | 0.9219 |
| 0.0 | 53.0 | 212 | 0.4873 | 0.9219 |
| 0.0 | 54.0 | 216 | 0.4879 | 0.9219 |
| 0.0 | 55.0 | 220 | 0.4883 | 0.9219 |
| 0.0 | 56.0 | 224 | 0.4887 | 0.9219 |
| 0.0 | 57.0 | 228 | 0.4890 | 0.9219 |
| 0.0 | 58.0 | 232 | 0.4894 | 0.9219 |
| 0.0 | 59.0 | 236 | 0.4896 | 0.9219 |
| 0.0 | 60.0 | 240 | 0.4899 | 0.9219 |
| 0.0 | 61.0 | 244 | 0.4903 | 0.9219 |
| 0.0 | 62.0 | 248 | 0.4907 | 0.9219 |
| 0.0 | 63.0 | 252 | 0.4912 | 0.9219 |
| 0.0 | 64.0 | 256 | 0.4916 | 0.9219 |
| 0.0 | 65.0 | 260 | 0.4920 | 0.9219 |
| 0.0 | 66.0 | 264 | 0.4924 | 0.9219 |
| 0.0 | 67.0 | 268 | 0.4927 | 0.9219 |
| 0.0 | 68.0 | 272 | 0.4931 | 0.9219 |
| 0.0 | 69.0 | 276 | 0.4934 | 0.9219 |
| 0.0 | 70.0 | 280 | 0.4938 | 0.9219 |
| 0.0 | 71.0 | 284 | 0.4943 | 0.9219 |
| 0.0 | 72.0 | 288 | 0.4945 | 0.9219 |
| 0.0 | 73.0 | 292 | 0.4949 | 0.9219 |
| 0.0 | 74.0 | 296 | 0.4953 | 0.9219 |
| 0.0 | 75.0 | 300 | 0.4955 | 0.9219 |
| 0.0 | 76.0 | 304 | 0.4959 | 0.9219 |
| 0.0 | 77.0 | 308 | 0.4962 | 0.9219 |
| 0.0 | 78.0 | 312 | 0.4965 | 0.9219 |
| 0.0 | 79.0 | 316 | 0.4970 | 0.9219 |
| 0.0 | 80.0 | 320 | 0.4975 | 0.9219 |
| 0.0 | 81.0 | 324 | 0.4978 | 0.9219 |
| 0.0 | 82.0 | 328 | 0.4982 | 0.9219 |
| 0.0 | 83.0 | 332 | 0.4985 | 0.9219 |
| 0.0 | 84.0 | 336 | 0.4987 | 0.9219 |
| 0.0 | 85.0 | 340 | 0.4988 | 0.9219 |
| 0.0 | 86.0 | 344 | 0.4990 | 0.9219 |
| 0.0 | 87.0 | 348 | 0.4993 | 0.9219 |
| 0.0 | 88.0 | 352 | 0.4994 | 0.9219 |
| 0.0 | 89.0 | 356 | 0.4996 | 0.9219 |
| 0.0 | 90.0 | 360 | 0.4998 | 0.9219 |
| 0.0 | 91.0 | 364 | 0.5001 | 0.9219 |
| 0.0 | 92.0 | 368 | 0.5004 | 0.9219 |
| 0.0 | 93.0 | 372 | 0.5006 | 0.9219 |
| 0.0 | 94.0 | 376 | 0.5009 | 0.9219 |
| 0.0 | 95.0 | 380 | 0.5012 | 0.9219 |
| 0.0 | 96.0 | 384 | 0.5013 | 0.9219 |
| 0.0 | 97.0 | 388 | 0.5017 | 0.9219 |
| 0.0 | 98.0 | 392 | 0.5021 | 0.9219 |
| 0.0 | 99.0 | 396 | 0.5021 | 0.9219 |
| 0.0 | 100.0 | 400 | 0.5022 | 0.9219 |
| 0.0 | 101.0 | 404 | 0.5025 | 0.9219 |
| 0.0 | 102.0 | 408 | 0.5029 | 0.9219 |
| 0.0 | 103.0 | 412 | 0.5030 | 0.9219 |
| 0.0 | 104.0 | 416 | 0.5033 | 0.9219 |
| 0.0 | 105.0 | 420 | 0.5037 | 0.9219 |
| 0.0 | 106.0 | 424 | 0.5040 | 0.9219 |
| 0.0 | 107.0 | 428 | 0.5044 | 0.9219 |
| 0.0 | 108.0 | 432 | 0.5046 | 0.9219 |
| 0.0 | 109.0 | 436 | 0.5047 | 0.9219 |
| 0.0 | 110.0 | 440 | 0.5050 | 0.9219 |
| 0.0 | 111.0 | 444 | 0.5053 | 0.9219 |
| 0.0 | 112.0 | 448 | 0.5057 | 0.9219 |
| 0.0 | 113.0 | 452 | 0.5061 | 0.9219 |
| 0.0 | 114.0 | 456 | 0.5065 | 0.9219 |
| 0.0 | 115.0 | 460 | 0.5070 | 0.9219 |
| 0.0 | 116.0 | 464 | 0.5073 | 0.9219 |
| 0.0 | 117.0 | 468 | 0.5077 | 0.9219 |
| 0.0 | 118.0 | 472 | 0.5080 | 0.9219 |
| 0.0 | 119.0 | 476 | 0.5082 | 0.9219 |
| 0.0 | 120.0 | 480 | 0.5085 | 0.9219 |
| 0.0 | 121.0 | 484 | 0.5087 | 0.9219 |
| 0.0 | 122.0 | 488 | 0.5090 | 0.9219 |
| 0.0 | 123.0 | 492 | 0.5095 | 0.9219 |
| 0.0 | 124.0 | 496 | 0.5098 | 0.9219 |
| 0.0 | 125.0 | 500 | 0.5102 | 0.9219 |
| 0.0 | 126.0 | 504 | 0.5107 | 0.9219 |
| 0.0 | 127.0 | 508 | 0.5111 | 0.9219 |
| 0.0 | 128.0 | 512 | 0.5115 | 0.9219 |
| 0.0 | 129.0 | 516 | 0.5118 | 0.9219 |
| 0.0 | 130.0 | 520 | 0.5120 | 0.9219 |
| 0.0 | 131.0 | 524 | 0.5122 | 0.9219 |
| 0.0 | 132.0 | 528 | 0.5126 | 0.9219 |
| 0.0 | 133.0 | 532 | 0.5127 | 0.9219 |
| 0.0 | 134.0 | 536 | 0.5129 | 0.9219 |
| 0.0 | 135.0 | 540 | 0.5131 | 0.9219 |
| 0.0 | 136.0 | 544 | 0.5132 | 0.9219 |
| 0.0 | 137.0 | 548 | 0.5134 | 0.9219 |
| 0.0 | 138.0 | 552 | 0.5135 | 0.9219 |
| 0.0 | 139.0 | 556 | 0.5136 | 0.9219 |
| 0.0 | 140.0 | 560 | 0.5137 | 0.9219 |
| 0.0 | 141.0 | 564 | 0.5138 | 0.9219 |
| 0.0 | 142.0 | 568 | 0.5139 | 0.9219 |
| 0.0 | 143.0 | 572 | 0.5140 | 0.9219 |
| 0.0 | 144.0 | 576 | 0.5140 | 0.9219 |
| 0.0 | 145.0 | 580 | 0.5141 | 0.9219 |
| 0.0 | 146.0 | 584 | 0.5141 | 0.9219 |
| 0.0 | 147.0 | 588 | 0.5141 | 0.9219 |
| 0.0 | 148.0 | 592 | 0.5142 | 0.9219 |
| 0.0 | 149.0 | 596 | 0.5142 | 0.9219 |
| 0.0 | 150.0 | 600 | 0.5142 | 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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