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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: lge_tests_prelim |
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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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# lge_tests_prelim |
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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: 0.5307 |
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- Accuracy: 0.34 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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 | 2.6404 | 0.0 | |
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| 2.5674 | 0.0128 | 100 | 2.5647 | 0.0 | |
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| 2.5021 | 0.0256 | 200 | 2.4965 | 0.0 | |
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| 2.4106 | 0.0384 | 300 | 2.4111 | 0.0 | |
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| 2.3432 | 0.0512 | 400 | 2.3422 | 0.0 | |
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| 2.32 | 0.0640 | 500 | 2.3004 | 0.0 | |
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| 2.2412 | 0.0768 | 600 | 2.2452 | 0.0 | |
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| 2.1721 | 0.0896 | 700 | 2.1679 | 0.0 | |
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| 1.9939 | 0.1024 | 800 | 1.9887 | 0.0 | |
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| 1.9089 | 0.1152 | 900 | 1.9041 | 0.0 | |
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| 2.0517 | 0.1280 | 1000 | 1.8690 | 0.0 | |
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| 1.854 | 0.1408 | 1100 | 1.7567 | 0.0 | |
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| 1.7972 | 0.1536 | 1200 | 1.7314 | 0.0 | |
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| 1.6798 | 0.1665 | 1300 | 1.7170 | 0.0 | |
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| 1.6579 | 0.1793 | 1400 | 1.6576 | 0.0 | |
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| 1.6968 | 0.1921 | 1500 | 1.6208 | 0.005 | |
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| 1.5677 | 0.2049 | 1600 | 1.6667 | 0.0 | |
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| 1.5288 | 0.2177 | 1700 | 1.5156 | 0.005 | |
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| 1.5954 | 0.2305 | 1800 | 1.5904 | 0.0 | |
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| 1.473 | 0.2433 | 1900 | 1.5063 | 0.01 | |
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| 1.4783 | 0.2561 | 2000 | 1.4800 | 0.01 | |
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| 1.5276 | 0.2689 | 2100 | 1.4590 | 0.01 | |
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| 1.3354 | 0.2817 | 2200 | 1.4401 | 0.02 | |
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| 1.4443 | 0.2945 | 2300 | 1.3868 | 0.0 | |
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| 1.3269 | 0.3073 | 2400 | 1.3720 | 0.025 | |
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| 1.3306 | 0.3201 | 2500 | 1.3052 | 0.015 | |
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| 1.274 | 0.3329 | 2600 | 1.3153 | 0.015 | |
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| 1.2331 | 0.3457 | 2700 | 1.2486 | 0.02 | |
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| 1.2947 | 0.3585 | 2800 | 1.2650 | 0.01 | |
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| 1.1635 | 0.3713 | 2900 | 1.1717 | 0.03 | |
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| 1.112 | 0.3841 | 3000 | 1.1700 | 0.045 | |
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| 1.1343 | 0.3969 | 3100 | 1.1362 | 0.04 | |
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| 1.072 | 0.4097 | 3200 | 1.1037 | 0.055 | |
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| 1.0831 | 0.4225 | 3300 | 1.0751 | 0.02 | |
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| 1.0762 | 0.4353 | 3400 | 1.0773 | 0.035 | |
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| 0.9965 | 0.4481 | 3500 | 1.0021 | 0.015 | |
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| 0.9867 | 0.4609 | 3600 | 0.9721 | 0.065 | |
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| 0.9194 | 0.4738 | 3700 | 0.9881 | 0.08 | |
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| 1.1577 | 0.4866 | 3800 | 1.1223 | 0.05 | |
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| 0.9286 | 0.4994 | 3900 | 0.9181 | 0.065 | |
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| 0.932 | 0.5122 | 4000 | 0.9695 | 0.035 | |
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| 0.907 | 0.5250 | 4100 | 0.9809 | 0.085 | |
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| 0.8528 | 0.5378 | 4200 | 0.8546 | 0.07 | |
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| 0.8456 | 0.5506 | 4300 | 0.8779 | 0.095 | |
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| 0.7858 | 0.5634 | 4400 | 0.8470 | 0.08 | |
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| 0.8417 | 0.5762 | 4500 | 0.8280 | 0.09 | |
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| 0.8261 | 0.5890 | 4600 | 0.8270 | 0.11 | |
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| 0.8291 | 0.6018 | 4700 | 0.8272 | 0.07 | |
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| 0.782 | 0.6146 | 4800 | 0.7997 | 0.07 | |
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| 0.7449 | 0.6274 | 4900 | 0.7533 | 0.06 | |
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| 0.7362 | 0.6402 | 5000 | 0.7722 | 0.1 | |
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| 0.7751 | 0.6530 | 5100 | 0.7441 | 0.11 | |
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| 0.7249 | 0.6658 | 5200 | 0.7591 | 0.08 | |
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| 0.7121 | 0.6786 | 5300 | 0.7160 | 0.17 | |
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| 0.704 | 0.6914 | 5400 | 0.7142 | 0.1 | |
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| 0.6699 | 0.7042 | 5500 | 0.6914 | 0.09 | |
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| 0.6853 | 0.7170 | 5600 | 0.6954 | 0.105 | |
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| 0.6638 | 0.7298 | 5700 | 0.6716 | 0.165 | |
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| 0.6862 | 0.7426 | 5800 | 0.6623 | 0.12 | |
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| 0.655 | 0.7554 | 5900 | 0.6549 | 0.145 | |
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| 0.6251 | 0.7682 | 6000 | 0.6537 | 0.125 | |
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| 0.637 | 0.7810 | 6100 | 0.6379 | 0.155 | |
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| 0.625 | 0.7939 | 6200 | 0.6188 | 0.17 | |
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| 0.6114 | 0.8067 | 6300 | 0.6036 | 0.205 | |
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| 0.6303 | 0.8195 | 6400 | 0.6004 | 0.19 | |
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| 0.5983 | 0.8323 | 6500 | 0.5845 | 0.225 | |
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| 0.6014 | 0.8451 | 6600 | 0.5766 | 0.245 | |
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| 0.5785 | 0.8579 | 6700 | 0.5765 | 0.24 | |
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| 0.5804 | 0.8707 | 6800 | 0.5620 | 0.28 | |
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| 0.5633 | 0.8835 | 6900 | 0.5518 | 0.3 | |
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| 0.5533 | 0.8963 | 7000 | 0.5489 | 0.305 | |
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| 0.5551 | 0.9091 | 7100 | 0.5481 | 0.305 | |
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| 0.569 | 0.9219 | 7200 | 0.5398 | 0.3 | |
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| 0.5583 | 0.9347 | 7300 | 0.5389 | 0.31 | |
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| 0.5357 | 0.9475 | 7400 | 0.5369 | 0.325 | |
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| 0.5453 | 0.9603 | 7500 | 0.5328 | 0.34 | |
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| 0.5472 | 0.9731 | 7600 | 0.5309 | 0.345 | |
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| 0.5349 | 0.9859 | 7700 | 0.5307 | 0.345 | |
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| 0.5309 | 0.9987 | 7800 | 0.5307 | 0.34 | |
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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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