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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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model-index: |
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- name: pretrain |
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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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# pretrain |
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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: 0.5260 |
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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: 5e-05 |
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- train_batch_size: 1024 |
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- eval_batch_size: 1024 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.95) 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_steps: 156250 |
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- num_epochs: 25 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:-----:|:---------------:| |
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| 0.4587 | 0.3774 | 500 | 1.7455 | |
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| 0.3676 | 0.7547 | 1000 | 1.3984 | |
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| 0.3343 | 1.1321 | 1500 | 1.2729 | |
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| 0.3118 | 1.5094 | 2000 | 1.1772 | |
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| 0.2953 | 1.8868 | 2500 | 1.0904 | |
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| 0.2771 | 2.2642 | 3000 | 1.0169 | |
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| 0.2605 | 2.6415 | 3500 | 0.9581 | |
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| 0.2501 | 3.0189 | 4000 | 0.8991 | |
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| 0.2351 | 3.3962 | 4500 | 0.8535 | |
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| 0.2245 | 3.7736 | 5000 | 0.8164 | |
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| 0.2168 | 4.1509 | 5500 | 0.7843 | |
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| 0.2121 | 4.5283 | 6000 | 0.7684 | |
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| 0.205 | 4.9057 | 6500 | 0.7447 | |
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| 0.1999 | 5.2830 | 7000 | 0.7284 | |
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| 0.196 | 5.6604 | 7500 | 0.7089 | |
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| 0.1894 | 6.0377 | 8000 | 0.7045 | |
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| 0.188 | 6.4151 | 8500 | 0.6867 | |
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| 0.1826 | 6.7925 | 9000 | 0.6750 | |
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| 0.1821 | 7.1698 | 9500 | 0.6672 | |
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| 0.1753 | 7.5472 | 10000 | 0.6650 | |
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| 0.1746 | 7.9245 | 10500 | 0.6485 | |
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| 0.1714 | 8.3019 | 11000 | 0.6420 | |
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| 0.1726 | 8.6792 | 11500 | 0.6365 | |
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| 0.169 | 9.0566 | 12000 | 0.6300 | |
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| 0.1659 | 9.4340 | 12500 | 0.6244 | |
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| 0.1653 | 9.8113 | 13000 | 0.6164 | |
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| 0.1646 | 10.1887 | 13500 | 0.6122 | |
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| 0.1623 | 10.5660 | 14000 | 0.6070 | |
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| 0.1629 | 10.9434 | 14500 | 0.6045 | |
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| 0.1603 | 11.3208 | 15000 | 0.5999 | |
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| 0.16 | 11.6981 | 15500 | 0.5948 | |
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| 0.1582 | 12.0755 | 16000 | 0.5898 | |
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| 0.1565 | 12.4528 | 16500 | 0.5868 | |
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| 0.1541 | 12.8302 | 17000 | 0.5844 | |
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| 0.1553 | 13.2075 | 17500 | 0.5798 | |
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| 0.152 | 13.5849 | 18000 | 0.5791 | |
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| 0.1536 | 13.9623 | 18500 | 0.5745 | |
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| 0.1525 | 14.3396 | 19000 | 0.5722 | |
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| 0.1516 | 14.7170 | 19500 | 0.5718 | |
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| 0.151 | 15.0943 | 20000 | 0.5675 | |
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| 0.1502 | 15.4717 | 20500 | 0.5672 | |
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| 0.1505 | 15.8491 | 21000 | 0.5639 | |
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| 0.1497 | 16.2264 | 21500 | 0.5607 | |
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| 0.1495 | 16.6038 | 22000 | 0.5583 | |
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| 0.1463 | 16.9811 | 22500 | 0.5547 | |
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| 0.1478 | 17.3585 | 23000 | 0.5556 | |
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| 0.1468 | 17.7358 | 23500 | 0.5534 | |
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| 0.1468 | 18.1132 | 24000 | 0.5509 | |
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| 0.1447 | 18.4906 | 24500 | 0.5480 | |
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| 0.1451 | 18.8679 | 25000 | 0.5479 | |
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| 0.1449 | 19.2453 | 25500 | 0.5453 | |
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| 0.1433 | 19.6226 | 26000 | 0.5449 | |
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| 0.1434 | 20.0 | 26500 | 0.5423 | |
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| 0.1434 | 20.3774 | 27000 | 0.5404 | |
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| 0.1428 | 20.7547 | 27500 | 0.5393 | |
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| 0.1435 | 21.1321 | 28000 | 0.5391 | |
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| 0.142 | 21.5094 | 28500 | 0.5371 | |
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| 0.142 | 21.8868 | 29000 | 0.5342 | |
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| 0.1418 | 22.2642 | 29500 | 0.5340 | |
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| 0.1417 | 22.6415 | 30000 | 0.5322 | |
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| 0.1405 | 23.0189 | 30500 | 0.5309 | |
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| 0.1412 | 23.3962 | 31000 | 0.5300 | |
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| 0.1395 | 23.7736 | 31500 | 0.5295 | |
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| 0.1383 | 24.1509 | 32000 | 0.5289 | |
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| 0.1373 | 24.5283 | 32500 | 0.5272 | |
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| 0.139 | 24.9057 | 33000 | 0.5260 | |
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### Framework versions |
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- Transformers 4.51.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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