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--- |
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library_name: transformers |
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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: reverseadd_grad_lr5e-4_batch128_train1-16_eval1-16 |
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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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# reverseadd_grad_lr5e-4_batch128_train1-16_eval1-16 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Accuracy: 1.0 |
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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: 0.0005 |
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- train_batch_size: 128 |
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- eval_batch_size: 512 |
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- seed: 23452399 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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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 | 0 | 0 | 2.7484 | 0.0 | |
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| 2.3112 | 0.0064 | 100 | 2.3389 | 0.0 | |
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| 2.2107 | 0.0128 | 200 | 2.2704 | 0.0 | |
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| 2.1761 | 0.0192 | 300 | 2.2076 | 0.0 | |
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| 2.139 | 0.0256 | 400 | 2.1694 | 0.0 | |
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| 2.0698 | 0.032 | 500 | 2.1565 | 0.0 | |
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| 2.0106 | 0.0384 | 600 | 2.1037 | 0.0 | |
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| 1.8663 | 0.0448 | 700 | 1.9948 | 0.0 | |
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| 1.6168 | 0.0512 | 800 | 1.7241 | 0.0 | |
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| 1.4174 | 0.0576 | 900 | 1.5429 | 0.0 | |
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| 1.5624 | 0.064 | 1000 | 1.5470 | 0.0004 | |
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| 1.5001 | 0.0704 | 1100 | 1.5073 | 0.0002 | |
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| 1.3973 | 0.0768 | 1200 | 1.4818 | 0.0002 | |
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| 1.3687 | 0.0832 | 1300 | 1.4266 | 0.0002 | |
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| 1.4476 | 0.0896 | 1400 | 1.4444 | 0.0 | |
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| 1.2652 | 0.096 | 1500 | 1.3937 | 0.0002 | |
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| 1.1528 | 0.1024 | 1600 | 1.4116 | 0.0007 | |
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| 1.3924 | 0.1088 | 1700 | 1.3425 | 0.0 | |
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| 1.3028 | 0.1152 | 1800 | 1.4500 | 0.0007 | |
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| 1.235 | 0.1216 | 1900 | 1.3035 | 0.001 | |
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| 1.1744 | 0.128 | 2000 | 1.2916 | 0.0009 | |
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| 1.2147 | 0.1344 | 2100 | 1.4271 | 0.0011 | |
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| 1.1417 | 0.1408 | 2200 | 1.2525 | 0.0024 | |
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| 1.2137 | 0.1472 | 2300 | 1.2886 | 0.0009 | |
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| 1.1376 | 0.1536 | 2400 | 1.2136 | 0.0056 | |
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| 1.1871 | 0.16 | 2500 | 1.2387 | 0.0008 | |
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| 1.1067 | 0.1664 | 2600 | 1.2139 | 0.002 | |
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| 1.1927 | 0.1728 | 2700 | 1.1976 | 0.0015 | |
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| 1.2498 | 0.1792 | 2800 | 1.2482 | 0.0038 | |
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| 0.9922 | 0.1856 | 2900 | 1.1533 | 0.0051 | |
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| 1.1107 | 0.192 | 3000 | 1.1871 | 0.0052 | |
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| 0.6429 | 0.1984 | 3100 | 0.9327 | 0.0094 | |
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| 0.6347 | 0.2048 | 3200 | 0.7828 | 0.0112 | |
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| 0.708 | 0.2112 | 3300 | 0.8326 | 0.0142 | |
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| 0.4057 | 0.2176 | 3400 | 0.4360 | 0.0254 | |
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| 0.2523 | 0.224 | 3500 | 0.5024 | 0.0281 | |
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| 0.2944 | 0.2304 | 3600 | 0.5327 | 0.0281 | |
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| 0.3447 | 0.2368 | 3700 | 0.3765 | 0.0374 | |
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| 0.7797 | 0.2432 | 3800 | 0.5927 | 0.026 | |
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| 0.3069 | 0.2496 | 3900 | 0.3702 | 0.0587 | |
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| 0.1809 | 0.256 | 4000 | 0.1090 | 0.6355 | |
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| 0.1374 | 0.2624 | 4100 | 0.1284 | 0.5887 | |
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| 0.1126 | 0.2688 | 4200 | 0.3041 | 0.4273 | |
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| 0.0358 | 0.2752 | 4300 | 0.0365 | 0.8795 | |
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| 0.1105 | 0.2816 | 4400 | 0.2670 | 0.2483 | |
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| 1.0684 | 0.288 | 4500 | 0.1955 | 0.5228 | |
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| 0.0261 | 0.2944 | 4600 | 0.0319 | 0.8704 | |
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| 0.1009 | 0.3008 | 4700 | 0.0474 | 0.8191 | |
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| 0.0215 | 0.3072 | 4800 | 0.1205 | 0.5488 | |
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| 0.164 | 0.3136 | 4900 | 0.1775 | 0.5252 | |
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| 0.0364 | 0.32 | 5000 | 0.1316 | 0.7096 | |
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| 0.0648 | 0.3264 | 5100 | 0.0333 | 0.8458 | |
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| 0.3891 | 0.3328 | 5200 | 0.8005 | 0.2254 | |
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| 0.019 | 0.3392 | 5300 | 0.0727 | 0.7597 | |
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| 0.0223 | 0.3456 | 5400 | 0.0686 | 0.7467 | |
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| 0.004 | 0.352 | 5500 | 0.0330 | 0.8568 | |
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| 0.0432 | 0.3584 | 5600 | 0.0591 | 0.7919 | |
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| 0.0348 | 0.3648 | 5700 | 0.0442 | 0.8438 | |
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| 0.0132 | 0.3712 | 5800 | 0.0163 | 0.9315 | |
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| 0.0084 | 0.3776 | 5900 | 0.3657 | 0.4157 | |
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| 0.074 | 0.384 | 6000 | 0.0294 | 0.8761 | |
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| 0.0604 | 0.3904 | 6100 | 0.2308 | 0.5677 | |
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| 0.0008 | 0.3968 | 6200 | 0.0263 | 0.8894 | |
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| 0.0641 | 0.4032 | 6300 | 0.0533 | 0.7816 | |
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| 0.1339 | 0.4096 | 6400 | 0.1043 | 0.6466 | |
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| 0.0062 | 0.416 | 6500 | 0.0159 | 0.9337 | |
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| 0.015 | 0.4224 | 6600 | 0.0429 | 0.8474 | |
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| 0.0372 | 0.4288 | 6700 | 0.1193 | 0.7112 | |
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| 0.0016 | 0.4352 | 6800 | 0.0463 | 0.8627 | |
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| 0.0172 | 0.4416 | 6900 | 0.1528 | 0.636 | |
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| 0.0003 | 0.448 | 7000 | 0.3467 | 0.5288 | |
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| 0.0465 | 0.4544 | 7100 | 0.2030 | 0.5105 | |
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| 0.0021 | 0.4608 | 7200 | 0.0092 | 0.9654 | |
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| 0.0005 | 0.4672 | 7300 | 0.0219 | 0.9183 | |
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| 0.0522 | 0.4736 | 7400 | 0.1057 | 0.677 | |
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| 0.003 | 0.48 | 7500 | 0.0040 | 0.985 | |
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| 0.0143 | 0.4864 | 7600 | 0.0418 | 0.8423 | |
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| 0.0029 | 0.4928 | 7700 | 0.0010 | 0.9974 | |
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| 0.007 | 0.4992 | 7800 | 0.1149 | 0.7892 | |
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| 0.0076 | 0.5056 | 7900 | 0.0060 | 0.9773 | |
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| 0.001 | 0.512 | 8000 | 0.0533 | 0.8267 | |
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| 0.0109 | 0.5184 | 8100 | 0.0125 | 0.9476 | |
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| 0.0493 | 0.5248 | 8200 | 0.1947 | 0.7369 | |
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| 0.0013 | 0.5312 | 8300 | 0.0126 | 0.9443 | |
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| 0.0138 | 0.5376 | 8400 | 0.0105 | 0.9577 | |
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| 0.0007 | 0.544 | 8500 | 0.0031 | 0.9869 | |
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| 0.0008 | 0.5504 | 8600 | 0.0018 | 0.9921 | |
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| 0.0014 | 0.5568 | 8700 | 0.0015 | 0.9946 | |
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| 0.0392 | 0.5632 | 8800 | 0.0011 | 0.996 | |
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| 0.0004 | 0.5696 | 8900 | 0.0621 | 0.8463 | |
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| 0.0091 | 0.576 | 9000 | 0.0005 | 0.9986 | |
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| 0.0 | 0.5824 | 9100 | 0.0000 | 1.0 | |
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| 0.0 | 0.5888 | 9200 | 0.0000 | 1.0 | |
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| 0.0 | 0.5952 | 9300 | 0.0000 | 1.0 | |
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| 0.0 | 0.6016 | 9400 | 0.0000 | 1.0 | |
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| 0.0 | 0.608 | 9500 | 0.0000 | 1.0 | |
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| 0.0 | 0.6144 | 9600 | 0.0000 | 1.0 | |
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| 0.0 | 0.6208 | 9700 | 0.0000 | 1.0 | |
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| 0.0 | 0.6272 | 9800 | 0.0000 | 1.0 | |
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| 0.0 | 0.6336 | 9900 | 0.0000 | 1.0 | |
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| 0.0 | 0.64 | 10000 | 0.0000 | 1.0 | |
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| 0.0 | 0.6464 | 10100 | 0.0000 | 1.0 | |
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| 0.0 | 0.6528 | 10200 | 0.0000 | 1.0 | |
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| 0.0 | 0.6592 | 10300 | 0.0000 | 1.0 | |
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| 0.0 | 0.6656 | 10400 | 0.0000 | 1.0 | |
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| 0.0 | 0.672 | 10500 | 0.0000 | 1.0 | |
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| 0.0 | 0.6784 | 10600 | 0.0000 | 1.0 | |
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| 0.0 | 0.6848 | 10700 | 0.0000 | 1.0 | |
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| 0.0 | 0.6912 | 10800 | 0.0000 | 1.0 | |
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| 0.0 | 0.6976 | 10900 | 0.0000 | 1.0 | |
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| 0.0 | 0.704 | 11000 | 0.0000 | 1.0 | |
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| 0.0 | 0.7104 | 11100 | 0.0000 | 1.0 | |
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| 0.0 | 0.7168 | 11200 | 0.0000 | 1.0 | |
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| 0.0 | 0.7232 | 11300 | 0.0000 | 1.0 | |
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| 0.0 | 0.7296 | 11400 | 0.0000 | 1.0 | |
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| 0.0 | 0.736 | 11500 | 0.0000 | 1.0 | |
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| 0.0 | 0.7424 | 11600 | 0.0000 | 1.0 | |
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| 0.0 | 0.7488 | 11700 | 0.0000 | 1.0 | |
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| 0.0 | 0.7552 | 11800 | 0.0000 | 1.0 | |
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| 0.0 | 0.7616 | 11900 | 0.0000 | 1.0 | |
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| 0.0 | 0.768 | 12000 | 0.0000 | 1.0 | |
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| 0.0 | 0.7744 | 12100 | 0.0000 | 1.0 | |
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| 0.0 | 0.7808 | 12200 | 0.0000 | 1.0 | |
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| 0.0 | 0.7872 | 12300 | 0.0000 | 1.0 | |
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| 0.0 | 0.7936 | 12400 | 0.0000 | 1.0 | |
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| 0.0 | 0.8 | 12500 | 0.0000 | 1.0 | |
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| 0.0 | 0.8064 | 12600 | 0.0000 | 1.0 | |
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| 0.0 | 0.8128 | 12700 | 0.0000 | 1.0 | |
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| 0.0 | 0.8192 | 12800 | 0.0000 | 1.0 | |
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| 0.0 | 0.8256 | 12900 | 0.0000 | 1.0 | |
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| 0.0 | 0.832 | 13000 | 0.0000 | 1.0 | |
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| 0.0 | 0.8384 | 13100 | 0.0000 | 1.0 | |
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| 0.0 | 0.8448 | 13200 | 0.0000 | 1.0 | |
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| 0.0 | 0.8512 | 13300 | 0.0000 | 1.0 | |
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| 0.0 | 0.8576 | 13400 | 0.0001 | 0.9996 | |
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| 0.0 | 0.864 | 13500 | 0.0000 | 1.0 | |
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| 0.0 | 0.8704 | 13600 | 0.0000 | 1.0 | |
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| 0.0 | 0.8768 | 13700 | 0.0000 | 1.0 | |
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| 0.0 | 0.8832 | 13800 | 0.0000 | 1.0 | |
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| 0.0 | 0.8896 | 13900 | 0.0000 | 1.0 | |
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| 0.0 | 0.896 | 14000 | 0.0000 | 1.0 | |
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| 0.0 | 0.9024 | 14100 | 0.0000 | 1.0 | |
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| 0.0 | 0.9088 | 14200 | 0.0000 | 1.0 | |
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| 0.0 | 0.9152 | 14300 | 0.0000 | 1.0 | |
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| 0.0 | 0.9216 | 14400 | 0.0000 | 1.0 | |
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| 0.0 | 0.928 | 14500 | 0.0000 | 1.0 | |
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| 0.0 | 0.9344 | 14600 | 0.0000 | 1.0 | |
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| 0.0 | 0.9408 | 14700 | 0.0000 | 1.0 | |
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| 0.0 | 0.9472 | 14800 | 0.0000 | 1.0 | |
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| 0.0 | 0.9536 | 14900 | 0.0000 | 1.0 | |
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| 0.0 | 0.96 | 15000 | 0.0000 | 1.0 | |
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| 0.0 | 0.9664 | 15100 | 0.0000 | 1.0 | |
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| 0.0 | 0.9728 | 15200 | 0.0000 | 1.0 | |
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| 0.0 | 0.9792 | 15300 | 0.0000 | 1.0 | |
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| 0.0 | 0.9856 | 15400 | 0.0000 | 1.0 | |
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| 0.0 | 0.992 | 15500 | 0.0000 | 1.0 | |
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| 0.0 | 0.9984 | 15600 | 0.0000 | 1.0 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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