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  1. last_to_hit_frequency_3591/README.md +163 -0
  2. last_to_hit_frequency_3591/all_results.json +16 -0
  3. last_to_hit_frequency_3591/checkpoint-100000/config.json +31 -0
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  16. last_to_hit_frequency_3591/checkpoint-100000/vocab.json +0 -0
  17. last_to_hit_frequency_3591/checkpoint-30000/config.json +31 -0
  18. last_to_hit_frequency_3591/checkpoint-30000/generation_config.json +6 -0
  19. last_to_hit_frequency_3591/checkpoint-30000/merges.txt +0 -0
  20. last_to_hit_frequency_3591/checkpoint-30000/model.safetensors +3 -0
  21. last_to_hit_frequency_3591/checkpoint-30000/optimizer.pt +3 -0
  22. last_to_hit_frequency_3591/checkpoint-30000/rng_state.pth +3 -0
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  25. last_to_hit_frequency_3591/checkpoint-30000/special_tokens_map.json +5 -0
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  27. last_to_hit_frequency_3591/checkpoint-30000/tokenizer_config.json +20 -0
  28. last_to_hit_frequency_3591/checkpoint-30000/trainer_state.json +0 -0
  29. last_to_hit_frequency_3591/checkpoint-30000/training_args.bin +3 -0
  30. last_to_hit_frequency_3591/checkpoint-30000/vocab.json +0 -0
  31. last_to_hit_frequency_3591/checkpoint-70000/config.json +31 -0
  32. last_to_hit_frequency_3591/checkpoint-70000/generation_config.json +6 -0
  33. last_to_hit_frequency_3591/checkpoint-70000/merges.txt +0 -0
  34. last_to_hit_frequency_3591/checkpoint-70000/model.safetensors +3 -0
  35. last_to_hit_frequency_3591/checkpoint-70000/optimizer.pt +3 -0
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  39. last_to_hit_frequency_3591/checkpoint-70000/special_tokens_map.json +5 -0
  40. last_to_hit_frequency_3591/checkpoint-70000/tokenizer.json +0 -0
  41. last_to_hit_frequency_3591/checkpoint-70000/tokenizer_config.json +20 -0
  42. last_to_hit_frequency_3591/checkpoint-70000/trainer_state.json +0 -0
  43. last_to_hit_frequency_3591/checkpoint-70000/training_args.bin +3 -0
  44. last_to_hit_frequency_3591/checkpoint-70000/vocab.json +0 -0
  45. last_to_hit_frequency_3591/checkpoint-80000/config.json +31 -0
  46. last_to_hit_frequency_3591/checkpoint-80000/generation_config.json +6 -0
  47. last_to_hit_frequency_3591/checkpoint-80000/merges.txt +0 -0
  48. last_to_hit_frequency_3591/checkpoint-80000/model.safetensors +3 -0
  49. last_to_hit_frequency_3591/checkpoint-80000/optimizer.pt +3 -0
  50. last_to_hit_frequency_3591/checkpoint-80000/rng_state.pth +3 -0
last_to_hit_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_hit_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/if6hk380)
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+ # exceptions_exp2_last_to_hit_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: 3.5747
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+ - Accuracy: 0.3673
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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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+ | 4.8516 | 0.2913 | 1000 | 0.2507 | 4.7821 |
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+ | 4.342 | 0.5826 | 2000 | 0.2995 | 4.2843 |
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+ | 4.1349 | 0.8739 | 3000 | 0.3162 | 4.0937 |
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+ | 3.9879 | 1.1652 | 4000 | 0.3260 | 3.9845 |
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+ | 3.9229 | 1.4565 | 5000 | 0.3325 | 3.9095 |
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+ | 3.8805 | 1.7478 | 6000 | 0.3377 | 3.8516 |
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+ | 3.7313 | 2.0390 | 7000 | 0.3422 | 3.8093 |
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+ | 3.7565 | 2.3303 | 8000 | 0.3449 | 3.7804 |
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+ | 3.7287 | 2.6216 | 9000 | 0.3479 | 3.7482 |
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+ | 3.7117 | 2.9130 | 10000 | 0.3501 | 3.7232 |
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+ | 3.6328 | 3.2042 | 11000 | 0.3520 | 3.7118 |
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+ | 3.6393 | 3.4955 | 12000 | 0.3538 | 3.6953 |
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+ | 3.6428 | 3.7868 | 13000 | 0.3553 | 3.6764 |
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+ | 3.5319 | 4.0781 | 14000 | 0.3565 | 3.6683 |
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+ | 3.5417 | 4.3694 | 15000 | 0.3574 | 3.6595 |
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+ | 3.5679 | 4.6607 | 16000 | 0.3588 | 3.6458 |
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+ | 3.5802 | 4.9520 | 17000 | 0.3600 | 3.6297 |
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+ | 3.4929 | 5.2432 | 18000 | 0.3606 | 3.6342 |
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+ | 3.5125 | 5.5345 | 19000 | 0.3612 | 3.6249 |
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+ | 3.5162 | 5.8259 | 20000 | 0.3622 | 3.6125 |
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+ | 3.4304 | 6.1171 | 21000 | 0.3630 | 3.6169 |
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+ | 3.4737 | 6.4084 | 22000 | 0.3632 | 3.6076 |
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+ | 3.4775 | 6.6997 | 23000 | 0.3641 | 3.5995 |
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+ | 3.49 | 6.9910 | 24000 | 0.3652 | 3.5868 |
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+ | 3.4352 | 7.2823 | 25000 | 0.3647 | 3.5988 |
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+ | 3.4508 | 7.5736 | 26000 | 0.3654 | 3.5895 |
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+ | 3.4523 | 7.8649 | 27000 | 0.3661 | 3.5810 |
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+ | 3.3762 | 8.1561 | 28000 | 0.3663 | 3.5881 |
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+ | 3.4033 | 8.4474 | 29000 | 0.3669 | 3.5829 |
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+ | 3.4225 | 8.7388 | 30000 | 0.3673 | 3.5747 |
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+ | 3.3258 | 9.0300 | 31000 | 0.3673 | 3.5808 |
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+ | 3.3753 | 9.3213 | 32000 | 0.3678 | 3.5774 |
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+ | 3.397 | 9.6126 | 33000 | 0.3679 | 3.5698 |
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+ | 3.4104 | 9.9039 | 34000 | 0.3687 | 3.5594 |
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+ | 3.3356 | 10.1952 | 35000 | 0.3685 | 3.5712 |
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+ | 3.3582 | 10.4865 | 36000 | 0.3688 | 3.5660 |
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+ | 3.3867 | 10.7778 | 37000 | 0.3696 | 3.5585 |
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+ | 3.2826 | 11.0690 | 38000 | 0.3693 | 3.5663 |
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+ | 3.3467 | 11.3603 | 39000 | 0.3692 | 3.5653 |
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+ | 3.3574 | 11.6517 | 40000 | 0.3698 | 3.5586 |
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+ | 3.3856 | 11.9430 | 41000 | 0.3708 | 3.5460 |
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+ | 3.3073 | 12.2342 | 42000 | 0.3700 | 3.5638 |
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+ | 3.338 | 12.5255 | 43000 | 0.3706 | 3.5550 |
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+ | 3.3361 | 12.8168 | 44000 | 0.3707 | 3.5477 |
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+ | 3.2665 | 13.1081 | 45000 | 0.3704 | 3.5618 |
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+ | 3.2949 | 13.3994 | 46000 | 0.3708 | 3.5553 |
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+ | 3.3255 | 13.6907 | 47000 | 0.3710 | 3.5473 |
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+ | 3.3499 | 13.9820 | 48000 | 0.3718 | 3.5397 |
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+ | 3.2776 | 14.2732 | 49000 | 0.3711 | 3.5552 |
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+ | 3.2981 | 14.5646 | 50000 | 0.3717 | 3.5478 |
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+ | 3.3145 | 14.8559 | 51000 | 0.3721 | 3.5404 |
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+ | 3.2624 | 15.1471 | 52000 | 0.3711 | 3.5562 |
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+ | 3.2688 | 15.4384 | 53000 | 0.3717 | 3.5516 |
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+ | 3.2895 | 15.7297 | 54000 | 0.3725 | 3.5416 |
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+ | 3.2005 | 16.0210 | 55000 | 0.3721 | 3.5526 |
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+ | 3.2617 | 16.3123 | 56000 | 0.3717 | 3.5525 |
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+ | 3.2742 | 16.6036 | 57000 | 0.3723 | 3.5431 |
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+ | 3.288 | 16.8949 | 58000 | 0.3729 | 3.5355 |
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+ | 3.2174 | 17.1861 | 59000 | 0.3721 | 3.5521 |
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+ | 3.2585 | 17.4775 | 60000 | 0.3727 | 3.5461 |
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+ | 3.279 | 17.7688 | 61000 | 0.3733 | 3.5366 |
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+ | 3.1786 | 18.0600 | 62000 | 0.3729 | 3.5506 |
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+ | 3.225 | 18.3513 | 63000 | 0.3724 | 3.5525 |
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+ | 3.2611 | 18.6426 | 64000 | 0.3732 | 3.5377 |
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+ | 3.2804 | 18.9339 | 65000 | 0.3739 | 3.5315 |
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+ | 3.2131 | 19.2252 | 66000 | 0.3733 | 3.5475 |
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+ | 3.237 | 19.5165 | 67000 | 0.3730 | 3.5423 |
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+ | 3.2455 | 19.8078 | 68000 | 0.3738 | 3.5354 |
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+ | 3.1751 | 20.0990 | 69000 | 0.3727 | 3.5514 |
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+ | 3.2249 | 20.3904 | 70000 | 0.3732 | 3.5473 |
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+ | 3.233 | 20.6817 | 71000 | 0.3739 | 3.5383 |
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+ | 3.2472 | 20.9730 | 72000 | 0.3743 | 3.5278 |
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+ | 3.1848 | 21.2642 | 73000 | 0.3733 | 3.5501 |
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+ | 3.2036 | 21.5555 | 74000 | 0.3738 | 3.5434 |
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+ | 3.2344 | 21.8468 | 75000 | 0.3745 | 3.5325 |
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+ | 3.162 | 22.1381 | 76000 | 0.3733 | 3.5484 |
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+ | 3.198 | 22.4294 | 77000 | 0.3736 | 3.5453 |
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+ | 3.2291 | 22.7207 | 78000 | 0.3742 | 3.5389 |
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+ | 3.16 | 23.0119 | 79000 | 0.3738 | 3.5488 |
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+ | 3.1854 | 23.3033 | 80000 | 0.3741 | 3.5452 |
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+ | 3.1779 | 23.5946 | 81000 | 3.5499 | 0.3735 |
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+ | 3.2077 | 23.8859 | 82000 | 3.5420 | 0.3742 |
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+ | 3.1569 | 24.1774 | 83000 | 3.5533 | 0.3739 |
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+ | 3.182 | 24.4687 | 84000 | 3.5435 | 0.3745 |
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+ | 3.2022 | 24.7600 | 85000 | 3.5368 | 0.3746 |
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+ | 3.1186 | 25.0513 | 86000 | 3.5508 | 0.3741 |
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+ | 3.1696 | 25.3426 | 87000 | 3.5458 | 0.3742 |
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+ | 3.1863 | 25.6339 | 88000 | 3.5432 | 0.3743 |
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+ | 3.1981 | 25.9252 | 89000 | 3.5322 | 0.3751 |
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+ | 3.1484 | 26.2164 | 90000 | 3.5460 | 0.3742 |
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+ | 3.1698 | 26.5077 | 91000 | 3.5457 | 0.3743 |
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+ | 3.1977 | 26.7991 | 92000 | 3.5354 | 0.3750 |
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+ | 3.1125 | 27.0903 | 93000 | 3.5501 | 0.3741 |
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+ | 3.1546 | 27.3816 | 94000 | 3.5468 | 0.3745 |
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+ | 3.1704 | 27.6729 | 95000 | 3.5382 | 0.3749 |
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+ | 3.1875 | 27.9642 | 96000 | 3.5307 | 0.3754 |
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+ | 3.1274 | 28.2555 | 97000 | 3.5460 | 0.3749 |
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+ | 3.1492 | 28.5468 | 98000 | 3.5448 | 0.3747 |
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+ | 3.1687 | 28.8381 | 99000 | 3.5363 | 0.3753 |
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+ | 3.0999 | 29.1293 | 100000 | 3.5502 | 0.3746 |
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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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