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  1. cost_to_push_frequency_2128/README.md +179 -0
  2. cost_to_push_frequency_2128/all_results.json +16 -0
  3. cost_to_push_frequency_2128/checkpoint-100000/config.json +31 -0
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  9. cost_to_push_frequency_2128/checkpoint-100000/scaler.pt +3 -0
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  11. cost_to_push_frequency_2128/checkpoint-100000/special_tokens_map.json +5 -0
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  13. cost_to_push_frequency_2128/checkpoint-100000/tokenizer_config.json +20 -0
  14. cost_to_push_frequency_2128/checkpoint-100000/trainer_state.json +0 -0
  15. cost_to_push_frequency_2128/checkpoint-100000/training_args.bin +3 -0
  16. cost_to_push_frequency_2128/checkpoint-100000/vocab.json +0 -0
  17. cost_to_push_frequency_2128/checkpoint-110000/config.json +31 -0
  18. cost_to_push_frequency_2128/checkpoint-110000/generation_config.json +6 -0
  19. cost_to_push_frequency_2128/checkpoint-110000/merges.txt +0 -0
  20. cost_to_push_frequency_2128/checkpoint-110000/model.safetensors +3 -0
  21. cost_to_push_frequency_2128/checkpoint-110000/optimizer.pt +3 -0
  22. cost_to_push_frequency_2128/checkpoint-110000/rng_state.pth +3 -0
  23. cost_to_push_frequency_2128/checkpoint-110000/scaler.pt +3 -0
  24. cost_to_push_frequency_2128/checkpoint-110000/scheduler.pt +3 -0
  25. cost_to_push_frequency_2128/checkpoint-110000/special_tokens_map.json +5 -0
  26. cost_to_push_frequency_2128/checkpoint-110000/tokenizer.json +0 -0
  27. cost_to_push_frequency_2128/checkpoint-110000/tokenizer_config.json +20 -0
  28. cost_to_push_frequency_2128/checkpoint-110000/trainer_state.json +0 -0
  29. cost_to_push_frequency_2128/checkpoint-110000/training_args.bin +3 -0
  30. cost_to_push_frequency_2128/checkpoint-110000/vocab.json +0 -0
  31. cost_to_push_frequency_2128/checkpoint-40000/config.json +31 -0
  32. cost_to_push_frequency_2128/checkpoint-40000/generation_config.json +6 -0
  33. cost_to_push_frequency_2128/checkpoint-40000/merges.txt +0 -0
  34. cost_to_push_frequency_2128/checkpoint-40000/model.safetensors +3 -0
  35. cost_to_push_frequency_2128/checkpoint-40000/optimizer.pt +3 -0
  36. cost_to_push_frequency_2128/checkpoint-40000/rng_state.pth +3 -0
  37. cost_to_push_frequency_2128/checkpoint-40000/scaler.pt +3 -0
  38. cost_to_push_frequency_2128/checkpoint-40000/scheduler.pt +3 -0
  39. cost_to_push_frequency_2128/checkpoint-40000/special_tokens_map.json +5 -0
  40. cost_to_push_frequency_2128/checkpoint-40000/tokenizer.json +0 -0
  41. cost_to_push_frequency_2128/checkpoint-40000/tokenizer_config.json +20 -0
  42. cost_to_push_frequency_2128/checkpoint-40000/trainer_state.json +0 -0
  43. cost_to_push_frequency_2128/checkpoint-40000/training_args.bin +3 -0
  44. cost_to_push_frequency_2128/checkpoint-40000/vocab.json +0 -0
  45. cost_to_push_frequency_2128/checkpoint-80000/config.json +31 -0
  46. cost_to_push_frequency_2128/checkpoint-80000/generation_config.json +6 -0
  47. cost_to_push_frequency_2128/checkpoint-80000/merges.txt +0 -0
  48. cost_to_push_frequency_2128/checkpoint-80000/model.safetensors +3 -0
  49. cost_to_push_frequency_2128/checkpoint-80000/optimizer.pt +3 -0
  50. cost_to_push_frequency_2128/checkpoint-80000/rng_state.pth +3 -0
cost_to_push_frequency_2128/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_cost_to_push_frequency_2128
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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/4a8ra77z)
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+ # exceptions_exp2_cost_to_push_frequency_2128
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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: 3.5570
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+ - Accuracy: 0.3697
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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: 2128
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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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+ | 4.8181 | 0.2912 | 1000 | 0.2557 | 4.7451 |
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+ | 4.3438 | 0.5824 | 2000 | 0.2995 | 4.2828 |
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+ | 4.1483 | 0.8737 | 3000 | 0.3158 | 4.0949 |
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+ | 3.9794 | 1.1648 | 4000 | 0.3249 | 3.9897 |
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+ | 3.946 | 1.4561 | 5000 | 0.3316 | 3.9169 |
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+ | 3.8796 | 1.7473 | 6000 | 0.3367 | 3.8599 |
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+ | 3.7474 | 2.0384 | 7000 | 0.3414 | 3.8146 |
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+ | 3.7545 | 2.3297 | 8000 | 0.3444 | 3.7828 |
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+ | 3.7487 | 2.6209 | 9000 | 0.3470 | 3.7568 |
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+ | 3.7222 | 2.9121 | 10000 | 0.3495 | 3.7290 |
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+ | 3.6434 | 3.2033 | 11000 | 0.3514 | 3.7157 |
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+ | 3.6493 | 3.4945 | 12000 | 0.3529 | 3.7001 |
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+ | 3.6429 | 3.7857 | 13000 | 0.3546 | 3.6810 |
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+ | 3.5427 | 4.0769 | 14000 | 0.3562 | 3.6738 |
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+ | 3.5735 | 4.3681 | 15000 | 0.3571 | 3.6592 |
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+ | 3.5796 | 4.6593 | 16000 | 0.3587 | 3.6485 |
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+ | 3.5819 | 4.9506 | 17000 | 0.3599 | 3.6348 |
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+ | 3.5007 | 5.2417 | 18000 | 0.3602 | 3.6366 |
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+ | 3.5156 | 5.5329 | 19000 | 0.3610 | 3.6285 |
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+ | 3.5239 | 5.8242 | 20000 | 0.3623 | 3.6141 |
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+ | 3.4463 | 6.1153 | 21000 | 0.3623 | 3.6174 |
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+ | 3.4779 | 6.4065 | 22000 | 0.3629 | 3.6108 |
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+ | 3.4888 | 6.6978 | 23000 | 0.3640 | 3.5985 |
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+ | 3.4999 | 6.9890 | 24000 | 0.3648 | 3.5906 |
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+ | 3.428 | 7.2802 | 25000 | 0.3648 | 3.5975 |
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+ | 3.4552 | 7.5714 | 26000 | 0.3654 | 3.5906 |
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+ | 3.4646 | 7.8626 | 27000 | 0.3662 | 3.5806 |
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+ | 3.3921 | 8.1538 | 28000 | 0.3661 | 3.5872 |
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+ | 3.4086 | 8.4450 | 29000 | 0.3662 | 3.5858 |
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+ | 3.4342 | 8.7362 | 30000 | 0.3673 | 3.5716 |
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+ | 3.3258 | 9.0274 | 31000 | 0.3676 | 3.5779 |
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+ | 3.3831 | 9.3186 | 32000 | 0.3673 | 3.5778 |
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+ | 3.3965 | 9.6098 | 33000 | 0.3678 | 3.5716 |
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+ | 3.4187 | 9.9010 | 34000 | 0.3683 | 3.5611 |
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+ | 3.344 | 10.1922 | 35000 | 0.3684 | 3.5726 |
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+ | 3.3789 | 10.4834 | 36000 | 0.3683 | 3.5695 |
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+ | 3.4005 | 10.7747 | 37000 | 0.3693 | 3.5603 |
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+ | 3.2886 | 11.0658 | 38000 | 0.3693 | 3.5678 |
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+ | 3.339 | 11.3570 | 39000 | 0.3693 | 3.5660 |
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+ | 3.3622 | 11.6483 | 40000 | 0.3697 | 3.5570 |
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+ | 3.3645 | 11.9395 | 41000 | 0.3705 | 3.5469 |
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+ | 3.3026 | 12.2306 | 42000 | 0.3698 | 3.5663 |
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+ | 3.3243 | 12.5219 | 43000 | 0.3705 | 3.5546 |
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+ | 3.3648 | 12.8131 | 44000 | 0.3711 | 3.5451 |
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+ | 3.275 | 13.1043 | 45000 | 0.3701 | 3.5611 |
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+ | 3.3082 | 13.3955 | 46000 | 0.3704 | 3.5561 |
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+ | 3.3173 | 13.6867 | 47000 | 0.3710 | 3.5484 |
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+ | 3.3449 | 13.9779 | 48000 | 0.3717 | 3.5397 |
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+ | 3.2796 | 14.2691 | 49000 | 0.3709 | 3.5576 |
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+ | 3.3156 | 14.5603 | 50000 | 0.3712 | 3.5494 |
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+ | 3.3248 | 14.8515 | 51000 | 0.3720 | 3.5402 |
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+ | 3.2411 | 15.1427 | 52000 | 0.3713 | 3.5585 |
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+ | 3.2784 | 15.4339 | 53000 | 0.3721 | 3.5505 |
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+ | 3.2982 | 15.7251 | 54000 | 0.3724 | 3.5395 |
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+ | 3.2021 | 16.0163 | 55000 | 0.3719 | 3.5502 |
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+ | 3.2393 | 16.3075 | 56000 | 0.3722 | 3.5525 |
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+ | 3.2732 | 16.5988 | 57000 | 0.3726 | 3.5446 |
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+ | 3.2869 | 16.8900 | 58000 | 0.3729 | 3.5367 |
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+ | 3.2329 | 17.1811 | 59000 | 0.3723 | 3.5505 |
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+ | 3.2627 | 17.4724 | 60000 | 0.3728 | 3.5460 |
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+ | 3.2649 | 17.7636 | 61000 | 0.3734 | 3.5382 |
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+ | 3.1859 | 18.0547 | 62000 | 0.3727 | 3.5512 |
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+ | 3.2295 | 18.3460 | 63000 | 0.3724 | 3.5526 |
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+ | 3.253 | 18.6372 | 64000 | 0.3733 | 3.5378 |
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+ | 3.2776 | 18.9284 | 65000 | 0.3736 | 3.5300 |
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+ | 3.2092 | 19.2196 | 66000 | 0.3729 | 3.5506 |
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+ | 3.2554 | 19.5108 | 67000 | 0.3730 | 3.5416 |
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+ | 3.2672 | 19.8020 | 68000 | 0.3737 | 3.5347 |
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+ | 3.178 | 20.0932 | 69000 | 0.3727 | 3.5489 |
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+ | 3.22 | 20.3844 | 70000 | 0.3731 | 3.5458 |
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+ | 3.2396 | 20.6756 | 71000 | 0.3737 | 3.5377 |
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+ | 3.2587 | 20.9669 | 72000 | 0.3740 | 3.5309 |
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+ | 3.1993 | 21.2580 | 73000 | 0.3735 | 3.5477 |
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+ | 3.2144 | 21.5492 | 74000 | 0.3735 | 3.5399 |
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+ | 3.2363 | 21.8405 | 75000 | 0.3743 | 3.5319 |
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+ | 3.1602 | 22.1316 | 76000 | 0.3735 | 3.5505 |
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+ | 3.1957 | 22.4229 | 77000 | 0.3743 | 3.5408 |
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+ | 3.2258 | 22.7141 | 78000 | 0.3744 | 3.5372 |
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+ | 3.1934 | 23.0052 | 79000 | 0.3739 | 3.5436 |
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+ | 3.183 | 23.2965 | 80000 | 0.3742 | 3.5451 |
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+ | 3.182 | 23.5877 | 81000 | 3.5493 | 0.3740 |
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+ | 3.2035 | 23.8789 | 82000 | 3.5410 | 0.3740 |
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+ | 3.1533 | 24.1704 | 83000 | 3.5505 | 0.3739 |
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+ | 3.1841 | 24.4616 | 84000 | 3.5410 | 0.3742 |
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+ | 3.1957 | 24.7528 | 85000 | 3.5377 | 0.3745 |
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+ | 3.1198 | 25.0440 | 86000 | 3.5510 | 0.3742 |
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+ | 3.1721 | 25.3352 | 87000 | 3.5434 | 0.3745 |
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+ | 3.1847 | 25.6264 | 88000 | 3.5338 | 0.3750 |
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+ | 3.2055 | 25.9176 | 89000 | 3.5300 | 0.3751 |
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+ | 3.1387 | 26.2088 | 90000 | 3.5482 | 0.3743 |
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+ | 3.1767 | 26.5000 | 91000 | 3.5396 | 0.3746 |
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+ | 3.1893 | 26.7913 | 92000 | 3.5325 | 0.3753 |
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+ | 3.1194 | 27.0824 | 93000 | 3.5483 | 0.3744 |
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+ | 3.146 | 27.3736 | 94000 | 3.5451 | 0.3747 |
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+ | 3.1737 | 27.6649 | 95000 | 3.5385 | 0.3752 |
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+ | 3.1959 | 27.9561 | 96000 | 3.5291 | 0.3756 |
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+ | 3.1176 | 28.2472 | 97000 | 3.5491 | 0.3746 |
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+ | 3.1518 | 28.5385 | 98000 | 3.5420 | 0.3750 |
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+ | 3.1707 | 28.8297 | 99000 | 3.5332 | 0.3756 |
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+ | 3.1001 | 29.1209 | 100000 | 3.5496 | 0.3747 |
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+ | 3.1358 | 29.4121 | 101000 | 3.5422 | 0.3749 |
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+ | 3.1647 | 29.7033 | 102000 | 3.5381 | 0.3752 |
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+ | 3.1656 | 29.9945 | 103000 | 3.5307 | 0.3759 |
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+ | 3.1186 | 30.2857 | 104000 | 3.5478 | 0.3749 |
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+ | 3.146 | 30.5769 | 105000 | 3.5400 | 0.3752 |
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+ | 3.1564 | 30.8681 | 106000 | 3.5340 | 0.3756 |
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+ | 3.1052 | 31.1593 | 107000 | 3.5480 | 0.3749 |
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+ | 3.1158 | 31.4505 | 108000 | 3.5437 | 0.3751 |
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+ | 3.1372 | 31.7417 | 109000 | 3.5374 | 0.3757 |
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+ | 3.0647 | 32.0329 | 110000 | 3.5486 | 0.3751 |
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+ | 3.1064 | 32.3241 | 111000 | 3.5481 | 0.3752 |
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+ | 3.1198 | 32.6154 | 112000 | 3.5374 | 0.3758 |
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+ | 3.0848 | 33.1977 | 114000 | 3.5476 | 0.3755 |
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+ | 3.1165 | 33.7802 | 116000 | 3.5385 | 0.3758 |
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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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