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--- |
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license: apache-2.0 |
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base_model: albert-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: best_model-yelp_polarity-64-42 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# best_model-yelp_polarity-64-42 |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6069 |
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- Accuracy: 0.9375 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 150 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 4 | 0.7342 | 0.9219 | |
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| No log | 2.0 | 8 | 0.7290 | 0.9219 | |
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| 0.5102 | 3.0 | 12 | 0.7270 | 0.9219 | |
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| 0.5102 | 4.0 | 16 | 0.7253 | 0.9219 | |
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| 0.4089 | 5.0 | 20 | 0.7208 | 0.9219 | |
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| 0.4089 | 6.0 | 24 | 0.7191 | 0.9219 | |
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| 0.4089 | 7.0 | 28 | 0.7271 | 0.9297 | |
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| 0.3981 | 8.0 | 32 | 0.7192 | 0.9297 | |
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| 0.3981 | 9.0 | 36 | 0.7009 | 0.9219 | |
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| 0.1982 | 10.0 | 40 | 0.6963 | 0.9141 | |
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| 0.1982 | 11.0 | 44 | 0.6904 | 0.9219 | |
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| 0.1982 | 12.0 | 48 | 0.6924 | 0.9219 | |
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| 0.2128 | 13.0 | 52 | 0.6921 | 0.9297 | |
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| 0.2128 | 14.0 | 56 | 0.6866 | 0.9219 | |
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| 0.0935 | 15.0 | 60 | 0.6841 | 0.9219 | |
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| 0.0935 | 16.0 | 64 | 0.6494 | 0.9219 | |
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| 0.0935 | 17.0 | 68 | 0.6201 | 0.9219 | |
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| 0.0365 | 18.0 | 72 | 0.6122 | 0.9219 | |
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| 0.0365 | 19.0 | 76 | 0.6047 | 0.9219 | |
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| 0.026 | 20.0 | 80 | 0.5870 | 0.9219 | |
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| 0.026 | 21.0 | 84 | 0.5739 | 0.9219 | |
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| 0.026 | 22.0 | 88 | 0.5737 | 0.9219 | |
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| 0.0139 | 23.0 | 92 | 0.5677 | 0.9219 | |
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| 0.0139 | 24.0 | 96 | 0.5579 | 0.9219 | |
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| 0.0149 | 25.0 | 100 | 0.5468 | 0.9219 | |
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| 0.0149 | 26.0 | 104 | 0.5277 | 0.9219 | |
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| 0.0149 | 27.0 | 108 | 0.5168 | 0.9219 | |
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| 0.0085 | 28.0 | 112 | 0.5036 | 0.9141 | |
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| 0.0085 | 29.0 | 116 | 0.4960 | 0.9141 | |
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| 0.0 | 30.0 | 120 | 0.4941 | 0.9219 | |
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| 0.0 | 31.0 | 124 | 0.4956 | 0.9297 | |
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| 0.0 | 32.0 | 128 | 0.4987 | 0.9297 | |
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| 0.0 | 33.0 | 132 | 0.5018 | 0.9297 | |
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| 0.0 | 34.0 | 136 | 0.5053 | 0.9297 | |
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| 0.0 | 35.0 | 140 | 0.5081 | 0.9297 | |
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| 0.0 | 36.0 | 144 | 0.5107 | 0.9297 | |
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| 0.0 | 37.0 | 148 | 0.5125 | 0.9297 | |
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| 0.0 | 38.0 | 152 | 0.5135 | 0.9297 | |
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| 0.0 | 39.0 | 156 | 0.5146 | 0.9297 | |
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| 0.0 | 40.0 | 160 | 0.5157 | 0.9297 | |
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| 0.0 | 41.0 | 164 | 0.5168 | 0.9297 | |
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| 0.0 | 42.0 | 168 | 0.5182 | 0.9297 | |
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| 0.0 | 43.0 | 172 | 0.5197 | 0.9297 | |
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| 0.0 | 44.0 | 176 | 0.5209 | 0.9297 | |
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| 0.0 | 45.0 | 180 | 0.5224 | 0.9297 | |
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| 0.0 | 46.0 | 184 | 0.5240 | 0.9297 | |
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| 0.0 | 47.0 | 188 | 0.5257 | 0.9297 | |
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| 0.0 | 48.0 | 192 | 0.5272 | 0.9297 | |
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| 0.0 | 49.0 | 196 | 0.5286 | 0.9297 | |
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| 0.0 | 50.0 | 200 | 0.5300 | 0.9297 | |
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| 0.0 | 51.0 | 204 | 0.5313 | 0.9297 | |
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| 0.0 | 52.0 | 208 | 0.5329 | 0.9297 | |
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| 0.0 | 53.0 | 212 | 0.5343 | 0.9297 | |
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| 0.0 | 54.0 | 216 | 0.5355 | 0.9297 | |
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| 0.0 | 55.0 | 220 | 0.5369 | 0.9297 | |
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| 0.0 | 56.0 | 224 | 0.5382 | 0.9297 | |
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| 0.0 | 57.0 | 228 | 0.5395 | 0.9297 | |
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| 0.0 | 58.0 | 232 | 0.5407 | 0.9297 | |
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| 0.0 | 59.0 | 236 | 0.5419 | 0.9297 | |
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| 0.0 | 60.0 | 240 | 0.5431 | 0.9297 | |
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| 0.0 | 61.0 | 244 | 0.5444 | 0.9297 | |
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| 0.0 | 62.0 | 248 | 0.5455 | 0.9297 | |
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| 0.0 | 63.0 | 252 | 0.5466 | 0.9297 | |
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| 0.0 | 64.0 | 256 | 0.5478 | 0.9297 | |
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| 0.0 | 65.0 | 260 | 0.5489 | 0.9297 | |
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| 0.0 | 66.0 | 264 | 0.5501 | 0.9297 | |
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| 0.0 | 67.0 | 268 | 0.5513 | 0.9297 | |
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| 0.0 | 68.0 | 272 | 0.5524 | 0.9297 | |
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| 0.0 | 69.0 | 276 | 0.5535 | 0.9297 | |
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| 0.0 | 70.0 | 280 | 0.5548 | 0.9297 | |
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| 0.0 | 71.0 | 284 | 0.5559 | 0.9297 | |
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| 0.0 | 72.0 | 288 | 0.5570 | 0.9297 | |
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| 0.0 | 73.0 | 292 | 0.5581 | 0.9297 | |
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| 0.0 | 74.0 | 296 | 0.5592 | 0.9297 | |
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| 0.0 | 75.0 | 300 | 0.5601 | 0.9297 | |
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| 0.0 | 76.0 | 304 | 0.5610 | 0.9297 | |
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| 0.0 | 77.0 | 308 | 0.5620 | 0.9297 | |
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| 0.0 | 78.0 | 312 | 0.5630 | 0.9297 | |
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| 0.0 | 79.0 | 316 | 0.5640 | 0.9297 | |
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| 0.0 | 80.0 | 320 | 0.5648 | 0.9297 | |
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| 0.0 | 81.0 | 324 | 0.5658 | 0.9297 | |
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| 0.0 | 82.0 | 328 | 0.5667 | 0.9297 | |
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| 0.0 | 83.0 | 332 | 0.5675 | 0.9297 | |
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| 0.0 | 84.0 | 336 | 0.5684 | 0.9297 | |
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| 0.0 | 85.0 | 340 | 0.5693 | 0.9297 | |
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| 0.0 | 86.0 | 344 | 0.5701 | 0.9297 | |
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| 0.0 | 87.0 | 348 | 0.5710 | 0.9297 | |
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| 0.0 | 88.0 | 352 | 0.5719 | 0.9297 | |
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| 0.0 | 89.0 | 356 | 0.5728 | 0.9297 | |
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| 0.0 | 90.0 | 360 | 0.5736 | 0.9297 | |
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| 0.0 | 91.0 | 364 | 0.5745 | 0.9297 | |
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| 0.0 | 92.0 | 368 | 0.5754 | 0.9297 | |
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| 0.0 | 93.0 | 372 | 0.5762 | 0.9297 | |
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| 0.0 | 94.0 | 376 | 0.5771 | 0.9297 | |
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| 0.0 | 95.0 | 380 | 0.5779 | 0.9297 | |
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| 0.0 | 96.0 | 384 | 0.5788 | 0.9297 | |
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| 0.0 | 97.0 | 388 | 0.5796 | 0.9297 | |
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| 0.0 | 98.0 | 392 | 0.5804 | 0.9297 | |
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| 0.0 | 99.0 | 396 | 0.5812 | 0.9297 | |
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| 0.0 | 100.0 | 400 | 0.5820 | 0.9297 | |
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| 0.0 | 101.0 | 404 | 0.5828 | 0.9297 | |
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| 0.0 | 102.0 | 408 | 0.5836 | 0.9297 | |
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| 0.0 | 103.0 | 412 | 0.5843 | 0.9297 | |
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| 0.0 | 104.0 | 416 | 0.5851 | 0.9297 | |
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| 0.0 | 105.0 | 420 | 0.5859 | 0.9297 | |
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| 0.0 | 106.0 | 424 | 0.5866 | 0.9297 | |
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| 0.0 | 107.0 | 428 | 0.5874 | 0.9297 | |
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| 0.0 | 108.0 | 432 | 0.5881 | 0.9297 | |
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| 0.0 | 109.0 | 436 | 0.5889 | 0.9297 | |
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| 0.0 | 110.0 | 440 | 0.5896 | 0.9297 | |
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| 0.0 | 111.0 | 444 | 0.5902 | 0.9297 | |
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| 0.0 | 112.0 | 448 | 0.5910 | 0.9375 | |
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| 0.0 | 113.0 | 452 | 0.5916 | 0.9375 | |
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| 0.0 | 114.0 | 456 | 0.5924 | 0.9375 | |
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| 0.0 | 115.0 | 460 | 0.5931 | 0.9375 | |
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| 0.0 | 116.0 | 464 | 0.5938 | 0.9375 | |
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| 0.0 | 117.0 | 468 | 0.5945 | 0.9375 | |
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| 0.0 | 118.0 | 472 | 0.5952 | 0.9375 | |
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| 0.0 | 119.0 | 476 | 0.5958 | 0.9375 | |
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| 0.0 | 120.0 | 480 | 0.5964 | 0.9375 | |
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| 0.0 | 121.0 | 484 | 0.5971 | 0.9375 | |
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| 0.0 | 122.0 | 488 | 0.5978 | 0.9375 | |
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| 0.0 | 123.0 | 492 | 0.5985 | 0.9375 | |
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| 0.0 | 124.0 | 496 | 0.5991 | 0.9375 | |
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| 0.0 | 125.0 | 500 | 0.5997 | 0.9375 | |
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| 0.0 | 126.0 | 504 | 0.6004 | 0.9375 | |
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| 0.0 | 127.0 | 508 | 0.6009 | 0.9375 | |
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| 0.0 | 128.0 | 512 | 0.6015 | 0.9375 | |
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| 0.0 | 129.0 | 516 | 0.6020 | 0.9375 | |
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| 0.0 | 130.0 | 520 | 0.6025 | 0.9375 | |
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| 0.0 | 131.0 | 524 | 0.6029 | 0.9375 | |
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| 0.0 | 132.0 | 528 | 0.6034 | 0.9375 | |
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| 0.0 | 133.0 | 532 | 0.6038 | 0.9375 | |
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| 0.0 | 134.0 | 536 | 0.6042 | 0.9375 | |
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| 0.0 | 135.0 | 540 | 0.6045 | 0.9375 | |
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| 0.0 | 136.0 | 544 | 0.6048 | 0.9375 | |
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| 0.0 | 137.0 | 548 | 0.6051 | 0.9375 | |
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| 0.0 | 138.0 | 552 | 0.6054 | 0.9375 | |
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| 0.0 | 139.0 | 556 | 0.6056 | 0.9375 | |
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| 0.0 | 140.0 | 560 | 0.6058 | 0.9375 | |
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| 0.0 | 141.0 | 564 | 0.6061 | 0.9375 | |
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| 0.0 | 142.0 | 568 | 0.6062 | 0.9375 | |
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| 0.0 | 143.0 | 572 | 0.6064 | 0.9375 | |
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| 0.0 | 144.0 | 576 | 0.6065 | 0.9375 | |
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| 0.0 | 145.0 | 580 | 0.6066 | 0.9375 | |
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| 0.0 | 146.0 | 584 | 0.6067 | 0.9375 | |
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| 0.0 | 147.0 | 588 | 0.6068 | 0.9375 | |
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| 0.0 | 148.0 | 592 | 0.6068 | 0.9375 | |
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| 0.0 | 149.0 | 596 | 0.6069 | 0.9375 | |
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| 0.0 | 150.0 | 600 | 0.6069 | 0.9375 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.4.0 |
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- Tokenizers 0.13.3 |
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