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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-64-13 |
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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-64-13 |
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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: 0.9118 |
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- Accuracy: 0.9062 |
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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 | 0.9825 | 0.8828 | |
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| No log | 2.0 | 8 | 0.9391 | 0.8906 | |
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| 0.0791 | 3.0 | 12 | 0.8979 | 0.8984 | |
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| 0.0791 | 4.0 | 16 | 0.8416 | 0.875 | |
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| 0.0238 | 5.0 | 20 | 0.8260 | 0.8906 | |
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| 0.0238 | 6.0 | 24 | 0.8079 | 0.8984 | |
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| 0.0238 | 7.0 | 28 | 0.7782 | 0.8906 | |
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| 0.0015 | 8.0 | 32 | 0.7635 | 0.8984 | |
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| 0.0015 | 9.0 | 36 | 0.7694 | 0.9062 | |
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| 0.0001 | 10.0 | 40 | 0.7757 | 0.9062 | |
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| 0.0001 | 11.0 | 44 | 0.7786 | 0.9141 | |
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| 0.0001 | 12.0 | 48 | 0.7749 | 0.9141 | |
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| 0.0 | 13.0 | 52 | 0.7730 | 0.9141 | |
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| 0.0 | 14.0 | 56 | 0.7692 | 0.9141 | |
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| 0.0 | 15.0 | 60 | 0.7662 | 0.9141 | |
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| 0.0 | 16.0 | 64 | 0.7640 | 0.9141 | |
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| 0.0 | 17.0 | 68 | 0.7616 | 0.9141 | |
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| 0.0 | 18.0 | 72 | 0.7600 | 0.9141 | |
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| 0.0 | 19.0 | 76 | 0.7608 | 0.9141 | |
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| 0.0 | 20.0 | 80 | 0.7625 | 0.9141 | |
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| 0.0 | 21.0 | 84 | 0.7641 | 0.9141 | |
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| 0.0 | 22.0 | 88 | 0.7656 | 0.9141 | |
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| 0.0 | 23.0 | 92 | 0.7670 | 0.9141 | |
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| 0.0 | 24.0 | 96 | 0.7692 | 0.9141 | |
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| 0.0 | 25.0 | 100 | 0.7709 | 0.9141 | |
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| 0.0 | 26.0 | 104 | 0.7737 | 0.9141 | |
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| 0.0 | 27.0 | 108 | 0.7763 | 0.9141 | |
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| 0.0 | 28.0 | 112 | 0.7774 | 0.9141 | |
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| 0.0 | 29.0 | 116 | 0.7802 | 0.9141 | |
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| 0.0 | 30.0 | 120 | 0.7819 | 0.9141 | |
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| 0.0 | 31.0 | 124 | 0.7846 | 0.9141 | |
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| 0.0 | 32.0 | 128 | 0.7864 | 0.9141 | |
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| 0.0 | 33.0 | 132 | 0.7891 | 0.9141 | |
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| 0.0 | 34.0 | 136 | 0.7923 | 0.9141 | |
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| 0.0 | 35.0 | 140 | 0.7953 | 0.9141 | |
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| 0.0 | 36.0 | 144 | 0.7967 | 0.9141 | |
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| 0.0 | 37.0 | 148 | 0.7973 | 0.9141 | |
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| 0.0 | 38.0 | 152 | 0.7987 | 0.9141 | |
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| 0.0 | 39.0 | 156 | 0.8002 | 0.9141 | |
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| 0.0 | 40.0 | 160 | 0.8022 | 0.9141 | |
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| 0.0 | 41.0 | 164 | 0.8030 | 0.9141 | |
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| 0.0 | 42.0 | 168 | 0.8043 | 0.9141 | |
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| 0.0 | 43.0 | 172 | 0.8048 | 0.9141 | |
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| 0.0 | 44.0 | 176 | 0.8057 | 0.9141 | |
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| 0.0 | 45.0 | 180 | 0.8068 | 0.9141 | |
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| 0.0 | 46.0 | 184 | 0.8080 | 0.9141 | |
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| 0.0 | 47.0 | 188 | 0.8104 | 0.9141 | |
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| 0.0 | 48.0 | 192 | 0.8121 | 0.9141 | |
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| 0.0 | 49.0 | 196 | 0.8122 | 0.9141 | |
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| 0.0 | 50.0 | 200 | 0.8133 | 0.9141 | |
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| 0.0 | 51.0 | 204 | 0.8146 | 0.9141 | |
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| 0.0 | 52.0 | 208 | 0.8154 | 0.9141 | |
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| 0.0 | 53.0 | 212 | 0.8160 | 0.9141 | |
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| 0.0 | 54.0 | 216 | 0.8182 | 0.9141 | |
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| 0.0 | 55.0 | 220 | 0.8204 | 0.9141 | |
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| 0.0 | 56.0 | 224 | 0.8226 | 0.9141 | |
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| 0.0 | 57.0 | 228 | 0.8228 | 0.9141 | |
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| 0.0 | 58.0 | 232 | 0.8241 | 0.9141 | |
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| 0.0 | 59.0 | 236 | 0.8263 | 0.9141 | |
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| 0.0 | 60.0 | 240 | 0.8284 | 0.9062 | |
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| 0.0 | 61.0 | 244 | 0.8287 | 0.9062 | |
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| 0.0 | 62.0 | 248 | 0.8300 | 0.9062 | |
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| 0.0 | 63.0 | 252 | 0.8317 | 0.9062 | |
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| 0.0 | 64.0 | 256 | 0.8327 | 0.9062 | |
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| 0.0 | 65.0 | 260 | 0.8342 | 0.9062 | |
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| 0.0 | 66.0 | 264 | 0.8353 | 0.9062 | |
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| 0.0 | 67.0 | 268 | 0.8369 | 0.9062 | |
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| 0.0 | 68.0 | 272 | 0.8378 | 0.9062 | |
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| 0.0 | 69.0 | 276 | 0.8386 | 0.9062 | |
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| 0.0 | 70.0 | 280 | 0.8394 | 0.9062 | |
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| 0.0 | 71.0 | 284 | 0.8403 | 0.9062 | |
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| 0.0 | 72.0 | 288 | 0.8413 | 0.9062 | |
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| 0.0 | 73.0 | 292 | 0.8414 | 0.9062 | |
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| 0.0 | 74.0 | 296 | 0.8430 | 0.9062 | |
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| 0.0 | 75.0 | 300 | 0.8439 | 0.9062 | |
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| 0.0 | 76.0 | 304 | 0.8452 | 0.9062 | |
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| 0.0 | 77.0 | 308 | 0.8469 | 0.9062 | |
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| 0.0 | 78.0 | 312 | 0.8484 | 0.9062 | |
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| 0.0 | 79.0 | 316 | 0.8499 | 0.9062 | |
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| 0.0 | 80.0 | 320 | 0.8517 | 0.9062 | |
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| 0.0 | 81.0 | 324 | 0.8533 | 0.9062 | |
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| 0.0 | 82.0 | 328 | 0.8538 | 0.9062 | |
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| 0.0 | 83.0 | 332 | 0.8549 | 0.9062 | |
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| 0.0 | 84.0 | 336 | 0.8565 | 0.9062 | |
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| 0.0 | 85.0 | 340 | 0.8575 | 0.9062 | |
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| 0.0 | 86.0 | 344 | 0.8585 | 0.9062 | |
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| 0.0 | 87.0 | 348 | 0.8596 | 0.9062 | |
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| 0.0 | 88.0 | 352 | 0.8609 | 0.9062 | |
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| 0.0 | 89.0 | 356 | 0.8623 | 0.9062 | |
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| 0.0 | 90.0 | 360 | 0.8641 | 0.9062 | |
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| 0.0 | 91.0 | 364 | 0.8653 | 0.9062 | |
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| 0.0 | 92.0 | 368 | 0.8664 | 0.9062 | |
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| 0.0 | 93.0 | 372 | 0.8674 | 0.9062 | |
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| 0.0 | 94.0 | 376 | 0.8695 | 0.9062 | |
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| 0.0 | 95.0 | 380 | 0.8711 | 0.9062 | |
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| 0.0 | 96.0 | 384 | 0.8715 | 0.9062 | |
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| 0.0 | 97.0 | 388 | 0.8713 | 0.9062 | |
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| 0.0 | 98.0 | 392 | 0.8725 | 0.9062 | |
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| 0.0 | 99.0 | 396 | 0.8725 | 0.9062 | |
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| 0.0 | 100.0 | 400 | 0.8730 | 0.9062 | |
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| 0.0 | 101.0 | 404 | 0.8730 | 0.9062 | |
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| 0.0 | 102.0 | 408 | 0.8738 | 0.9062 | |
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| 0.0 | 103.0 | 412 | 0.8750 | 0.9062 | |
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| 0.0 | 104.0 | 416 | 0.8756 | 0.9062 | |
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| 0.0 | 105.0 | 420 | 0.8757 | 0.9062 | |
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| 0.0 | 106.0 | 424 | 0.8772 | 0.9062 | |
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| 0.0 | 107.0 | 428 | 0.8785 | 0.9062 | |
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| 0.0 | 108.0 | 432 | 0.8795 | 0.9062 | |
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| 0.0 | 109.0 | 436 | 0.8806 | 0.9062 | |
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| 0.0 | 110.0 | 440 | 0.8815 | 0.9062 | |
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| 0.0 | 111.0 | 444 | 0.8826 | 0.9062 | |
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| 0.0 | 112.0 | 448 | 0.8837 | 0.9062 | |
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| 0.0 | 113.0 | 452 | 0.8846 | 0.9062 | |
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| 0.0 | 114.0 | 456 | 0.8859 | 0.9062 | |
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| 0.0 | 115.0 | 460 | 0.8877 | 0.9062 | |
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| 0.0 | 116.0 | 464 | 0.8891 | 0.9062 | |
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| 0.0 | 117.0 | 468 | 0.8913 | 0.9062 | |
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| 0.0 | 118.0 | 472 | 0.8926 | 0.9062 | |
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| 0.0 | 119.0 | 476 | 0.8940 | 0.9062 | |
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| 0.0 | 120.0 | 480 | 0.8959 | 0.9062 | |
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| 0.0 | 121.0 | 484 | 0.8978 | 0.9062 | |
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| 0.0 | 122.0 | 488 | 0.8987 | 0.9062 | |
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| 0.0 | 123.0 | 492 | 0.8999 | 0.9062 | |
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| 0.0 | 124.0 | 496 | 0.8998 | 0.9062 | |
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| 0.0 | 125.0 | 500 | 0.9010 | 0.9062 | |
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| 0.0 | 126.0 | 504 | 0.9019 | 0.9062 | |
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| 0.0 | 127.0 | 508 | 0.9031 | 0.9062 | |
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| 0.0 | 128.0 | 512 | 0.9036 | 0.9062 | |
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| 0.0 | 129.0 | 516 | 0.9039 | 0.9062 | |
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| 0.0 | 130.0 | 520 | 0.9043 | 0.9062 | |
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| 0.0 | 131.0 | 524 | 0.9043 | 0.9062 | |
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| 0.0 | 132.0 | 528 | 0.9052 | 0.9062 | |
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| 0.0 | 133.0 | 532 | 0.9052 | 0.9062 | |
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| 0.0 | 134.0 | 536 | 0.9060 | 0.9062 | |
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| 0.0 | 135.0 | 540 | 0.9071 | 0.9062 | |
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| 0.0 | 136.0 | 544 | 0.9078 | 0.9062 | |
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| 0.0 | 137.0 | 548 | 0.9085 | 0.9062 | |
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| 0.0 | 138.0 | 552 | 0.9087 | 0.9062 | |
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| 0.0 | 139.0 | 556 | 0.9094 | 0.9062 | |
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| 0.0 | 140.0 | 560 | 0.9097 | 0.9062 | |
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| 0.0 | 141.0 | 564 | 0.9101 | 0.9062 | |
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| 0.0 | 142.0 | 568 | 0.9105 | 0.9062 | |
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| 0.0 | 143.0 | 572 | 0.9108 | 0.9062 | |
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| 0.0 | 144.0 | 576 | 0.9110 | 0.9062 | |
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| 0.0 | 145.0 | 580 | 0.9112 | 0.9062 | |
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| 0.0 | 146.0 | 584 | 0.9115 | 0.9062 | |
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| 0.0 | 147.0 | 588 | 0.9116 | 0.9062 | |
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| 0.0 | 148.0 | 592 | 0.9117 | 0.9062 | |
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| 0.0 | 149.0 | 596 | 0.9118 | 0.9062 | |
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| 0.0 | 150.0 | 600 | 0.9118 | 0.9062 | |
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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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