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  1. last_to_push_frequency_3591/README.md +163 -0
  2. last_to_push_frequency_3591/all_results.json +16 -0
  3. last_to_push_frequency_3591/checkpoint-100000/config.json +31 -0
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  16. last_to_push_frequency_3591/checkpoint-100000/vocab.json +0 -0
  17. last_to_push_frequency_3591/checkpoint-30000/config.json +31 -0
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  31. last_to_push_frequency_3591/checkpoint-70000/config.json +31 -0
  32. last_to_push_frequency_3591/checkpoint-70000/generation_config.json +6 -0
  33. last_to_push_frequency_3591/checkpoint-70000/merges.txt +0 -0
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  49. last_to_push_frequency_3591/checkpoint-80000/optimizer.pt +3 -0
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last_to_push_frequency_3591/README.md ADDED
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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: exceptions_exp2_last_to_push_frequency_3591
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+ results: []
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+ ---
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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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+
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/craaaa/exceptions_exp2/runs/tkpnm5n1)
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+ # exceptions_exp2_last_to_push_frequency_3591
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+
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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: 3.5738
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+ - Accuracy: 0.3671
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0006
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 3591
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+ - gradient_accumulation_steps: 5
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+ - total_train_batch_size: 80
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 50.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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+ |:-------------:|:-------:|:------:|:--------:|:---------------:|
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+ | 4.8193 | 0.2912 | 1000 | 0.2556 | 4.7409 |
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+ | 4.3232 | 0.5824 | 2000 | 0.2995 | 4.2815 |
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+ | 4.1292 | 0.8737 | 3000 | 0.3162 | 4.0896 |
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+ | 3.9822 | 1.1648 | 4000 | 0.3258 | 3.9847 |
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+ | 3.9236 | 1.4561 | 5000 | 0.3319 | 3.9101 |
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+ | 3.8783 | 1.7473 | 6000 | 0.3376 | 3.8539 |
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+ | 3.7409 | 2.0384 | 7000 | 0.3419 | 3.8072 |
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+ | 3.7556 | 2.3297 | 8000 | 0.3449 | 3.7786 |
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+ | 3.7318 | 2.6209 | 9000 | 0.3477 | 3.7488 |
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+ | 3.7152 | 2.9121 | 10000 | 0.3496 | 3.7241 |
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+ | 3.6271 | 3.2033 | 11000 | 0.3520 | 3.7099 |
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+ | 3.637 | 3.4945 | 12000 | 0.3538 | 3.6923 |
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+ | 3.6368 | 3.7857 | 13000 | 0.3554 | 3.6732 |
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+ | 3.5417 | 4.0769 | 14000 | 0.3566 | 3.6686 |
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+ | 3.5573 | 4.3681 | 15000 | 0.3576 | 3.6567 |
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+ | 3.5738 | 4.6593 | 16000 | 0.3587 | 3.6428 |
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+ | 3.585 | 4.9506 | 17000 | 0.3602 | 3.6294 |
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+ | 3.5044 | 5.2417 | 18000 | 0.3603 | 3.6343 |
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+ | 3.5108 | 5.5329 | 19000 | 0.3617 | 3.6223 |
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+ | 3.5355 | 5.8242 | 20000 | 0.3623 | 3.6103 |
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+ | 3.4252 | 6.1153 | 21000 | 0.3629 | 3.6136 |
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+ | 3.4717 | 6.4065 | 22000 | 0.3633 | 3.6065 |
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+ | 3.4676 | 6.6978 | 23000 | 0.3642 | 3.5977 |
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+ | 3.4867 | 6.9890 | 24000 | 0.3650 | 3.5884 |
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+ | 3.4248 | 7.2802 | 25000 | 0.3649 | 3.5983 |
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+ | 3.4455 | 7.5714 | 26000 | 0.3656 | 3.5864 |
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+ | 3.4606 | 7.8626 | 27000 | 0.3667 | 3.5772 |
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+ | 3.3815 | 8.1538 | 28000 | 0.3664 | 3.5897 |
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+ | 3.4063 | 8.4450 | 29000 | 0.3664 | 3.5818 |
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+ | 3.4294 | 8.7362 | 30000 | 0.3671 | 3.5738 |
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+ | 3.3145 | 9.0274 | 31000 | 0.3673 | 3.5788 |
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+ | 3.3916 | 9.3186 | 32000 | 0.3675 | 3.5765 |
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+ | 3.3941 | 9.6098 | 33000 | 0.3682 | 3.5685 |
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+ | 3.3895 | 9.9010 | 34000 | 0.3690 | 3.5596 |
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+ | 3.3348 | 10.1922 | 35000 | 0.3682 | 3.5724 |
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+ | 3.3676 | 10.4834 | 36000 | 0.3689 | 3.5667 |
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+ | 3.3776 | 10.7747 | 37000 | 0.3697 | 3.5554 |
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+ | 3.2947 | 11.0658 | 38000 | 0.3693 | 3.5679 |
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+ | 3.3428 | 11.3570 | 39000 | 0.3693 | 3.5647 |
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+ | 3.3673 | 11.6483 | 40000 | 0.3697 | 3.5560 |
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+ | 3.3775 | 11.9395 | 41000 | 0.3705 | 3.5475 |
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+ | 3.3046 | 12.2306 | 42000 | 0.3699 | 3.5598 |
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+ | 3.3397 | 12.5219 | 43000 | 0.3705 | 3.5548 |
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+ | 3.3404 | 12.8131 | 44000 | 0.3711 | 3.5444 |
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+ | 3.2597 | 13.1043 | 45000 | 0.3705 | 3.5577 |
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+ | 3.3136 | 13.3955 | 46000 | 0.3709 | 3.5534 |
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+ | 3.326 | 13.6867 | 47000 | 0.3715 | 3.5448 |
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+ | 3.3423 | 13.9779 | 48000 | 0.3719 | 3.5405 |
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+ | 3.2747 | 14.2691 | 49000 | 0.3712 | 3.5534 |
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+ | 3.287 | 14.5603 | 50000 | 0.3717 | 3.5465 |
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+ | 3.3153 | 14.8515 | 51000 | 0.3723 | 3.5408 |
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+ | 3.2414 | 15.1427 | 52000 | 0.3716 | 3.5568 |
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+ | 3.2794 | 15.4339 | 53000 | 0.3719 | 3.5461 |
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+ | 3.2904 | 15.7251 | 54000 | 0.3724 | 3.5429 |
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+ | 3.1954 | 16.0163 | 55000 | 0.3722 | 3.5515 |
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+ | 3.2552 | 16.3075 | 56000 | 0.3718 | 3.5528 |
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+ | 3.2751 | 16.5988 | 57000 | 0.3728 | 3.5438 |
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+ | 3.2981 | 16.8900 | 58000 | 0.3730 | 3.5364 |
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+ | 3.2182 | 17.1811 | 59000 | 0.3722 | 3.5542 |
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+ | 3.261 | 17.4724 | 60000 | 0.3727 | 3.5467 |
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+ | 3.2801 | 17.7636 | 61000 | 0.3731 | 3.5363 |
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+ | 3.1875 | 18.0547 | 62000 | 0.3724 | 3.5549 |
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+ | 3.2276 | 18.3460 | 63000 | 0.3728 | 3.5480 |
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+ | 3.2535 | 18.6372 | 64000 | 0.3733 | 3.5393 |
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+ | 3.2763 | 18.9284 | 65000 | 0.3738 | 3.5290 |
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+ | 3.2052 | 19.2196 | 66000 | 0.3729 | 3.5498 |
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+ | 3.2232 | 19.5108 | 67000 | 0.3732 | 3.5431 |
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+ | 3.2532 | 19.8020 | 68000 | 0.3736 | 3.5360 |
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+ | 3.1732 | 20.0932 | 69000 | 0.3733 | 3.5459 |
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+ | 3.2108 | 20.3844 | 70000 | 0.3731 | 3.5489 |
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+ | 3.2267 | 20.6756 | 71000 | 0.3733 | 3.5392 |
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+ | 3.2515 | 20.9669 | 72000 | 0.3746 | 3.5281 |
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+ | 3.1939 | 21.2580 | 73000 | 0.3732 | 3.5509 |
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+ | 3.2191 | 21.5492 | 74000 | 0.3739 | 3.5387 |
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+ | 3.2375 | 21.8405 | 75000 | 0.3740 | 3.5342 |
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+ | 3.1643 | 22.1316 | 76000 | 0.3739 | 3.5459 |
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+ | 3.1882 | 22.4229 | 77000 | 0.3738 | 3.5426 |
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+ | 3.2184 | 22.7141 | 78000 | 0.3743 | 3.5364 |
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+ | 3.1907 | 23.0052 | 79000 | 0.3741 | 3.5416 |
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+ | 3.1733 | 23.2965 | 80000 | 0.3736 | 3.5454 |
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+ | 3.1709 | 23.5877 | 81000 | 3.5511 | 0.3736 |
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+ | 3.1826 | 23.8789 | 82000 | 3.5399 | 0.3738 |
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+ | 3.1678 | 24.1704 | 83000 | 3.5513 | 0.3738 |
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+ | 3.1804 | 24.4616 | 84000 | 3.5443 | 0.3744 |
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+ | 3.1961 | 24.7528 | 85000 | 3.5370 | 0.3745 |
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+ | 3.1263 | 25.0440 | 86000 | 3.5491 | 0.3741 |
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+ | 3.154 | 25.3352 | 87000 | 3.5454 | 0.3741 |
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+ | 3.1894 | 25.6264 | 88000 | 3.5389 | 0.3747 |
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+ | 3.1347 | 26.2088 | 90000 | 3.5523 | 0.3739 |
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+ | 3.1718 | 26.5000 | 91000 | 3.5452 | 0.3744 |
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+ | 3.1811 | 26.7913 | 92000 | 3.5325 | 0.3752 |
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+ | 3.1056 | 27.0824 | 93000 | 3.5502 | 0.3745 |
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+ | 3.1389 | 27.3736 | 94000 | 3.5447 | 0.3747 |
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+ | 3.1714 | 27.6649 | 95000 | 3.5432 | 0.3746 |
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+ | 3.1725 | 27.9561 | 96000 | 3.5296 | 0.3756 |
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+ | 3.1221 | 28.2472 | 97000 | 3.5502 | 0.3742 |
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+ | 3.1436 | 28.5385 | 98000 | 3.5440 | 0.3748 |
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+ | 3.1738 | 28.8297 | 99000 | 3.5368 | 0.3752 |
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+ | 3.1107 | 29.1209 | 100000 | 3.5490 | 0.3747 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.2
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+ - Pytorch 2.8.0+cu128
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
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