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Teamim-IvritAI-large-v3-turbo-new_WeightDecay-0.005_Augmented_WithSRT_date-23-04-2025

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
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  1. README.md +117 -0
  2. generation_config.json +151 -0
  3. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: ivrit-ai/whisper-large-v3-turbo
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: Teamim-IvritAI-large-v3-turbo-new_WeightDecay-0.005_Augmented_WithSRT_date-23-04-2025
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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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+ # Teamim-IvritAI-large-v3-turbo-new_WeightDecay-0.005_Augmented_WithSRT_date-23-04-2025
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+
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+ This model is a fine-tuned version of [ivrit-ai/whisper-large-v3-turbo](https://huggingface.co/ivrit-ai/whisper-large-v3-turbo) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4107
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+ - Wer: 36.2146
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+ - Avg Precision Exact: 0.4891
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+ - Avg Recall Exact: 0.4995
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+ - Avg F1 Exact: 0.4932
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+ - Avg Precision Letter Shift: 0.5095
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+ - Avg Recall Letter Shift: 0.5218
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+ - Avg F1 Letter Shift: 0.5143
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+ - Avg Precision Word Level: 0.5211
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+ - Avg Recall Word Level: 0.5357
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+ - Avg F1 Word Level: 0.5263
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+ - Avg Precision Word Shift: 0.6939
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+ - Avg Recall Word Shift: 0.7234
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+ - Avg F1 Word Shift: 0.7047
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+ - Precision Median Exact: 0.4432
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+ - Recall Median Exact: 0.4756
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+ - F1 Median Exact: 0.4528
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+ - Precision Max Exact: 1.0
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+ - Recall Max Exact: 1.0
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+ - F1 Max Exact: 1.0
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+ - Precision Min Exact: 0.0
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+ - Recall Min Exact: 0.0
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+ - F1 Min Exact: 0.0
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+ - Precision Min Letter Shift: 0.0
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+ - Recall Min Letter Shift: 0.0
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+ - F1 Min Letter Shift: 0.0
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+ - Precision Min Word Level: 0.0
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+ - Recall Min Word Level: 0.0
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+ - F1 Min Word Level: 0.0
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+ - Precision Min Word Shift: 0.0
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+ - Recall Min Word Shift: 0.0
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+ - F1 Min Word Shift: 0.0
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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: 1e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 2
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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: linear
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+ - lr_scheduler_warmup_steps: 1000
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+ - training_steps: 60000
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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 | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
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+ | No log | 0.0001 | 1 | 7.1601 | 109.4023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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+ | 0.1361 | 0.2962 | 2500 | 0.6842 | 62.4024 | 0.2897 | 0.2982 | 0.2919 | 0.3222 | 0.3329 | 0.3249 | 0.3400 | 0.3543 | 0.3446 | 0.5235 | 0.5550 | 0.5339 | 0.1505 | 0.1562 | 0.1538 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0633 | 0.5925 | 5000 | 0.5766 | 51.9556 | 0.3589 | 0.3642 | 0.3604 | 0.3909 | 0.3982 | 0.3927 | 0.4080 | 0.4161 | 0.4098 | 0.6144 | 0.6383 | 0.6218 | 0.2 | 0.2185 | 0.2122 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0629 | 0.8887 | 7500 | 0.5497 | 53.1810 | 0.3695 | 0.3923 | 0.3759 | 0.3997 | 0.4249 | 0.4062 | 0.4143 | 0.4424 | 0.4213 | 0.5923 | 0.6461 | 0.6078 | 0.2222 | 0.2639 | 0.2381 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0375 | 1.1850 | 10000 | 0.5146 | 45.5717 | 0.4020 | 0.4168 | 0.4084 | 0.4303 | 0.4478 | 0.4374 | 0.4451 | 0.4634 | 0.4521 | 0.6430 | 0.6809 | 0.6579 | 0.2571 | 0.2857 | 0.2675 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0513 | 1.4812 | 12500 | 0.4789 | 47.3763 | 0.4082 | 0.4225 | 0.4140 | 0.4363 | 0.4528 | 0.4429 | 0.4515 | 0.4685 | 0.4581 | 0.6408 | 0.6728 | 0.6531 | 0.2698 | 0.2941 | 0.2798 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0407 | 1.7775 | 15000 | 0.4574 | 45.7832 | 0.4313 | 0.4414 | 0.4353 | 0.4612 | 0.4728 | 0.4654 | 0.4754 | 0.4873 | 0.4795 | 0.6548 | 0.6794 | 0.6639 | 0.3333 | 0.3438 | 0.3393 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0228 | 2.0737 | 17500 | 0.4692 | 45.7453 | 0.4131 | 0.4225 | 0.4167 | 0.4392 | 0.4496 | 0.4427 | 0.4549 | 0.4671 | 0.4590 | 0.6411 | 0.6686 | 0.6508 | 0.2587 | 0.2759 | 0.2659 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0203 | 2.3699 | 20000 | 0.4669 | 43.5424 | 0.4200 | 0.4337 | 0.4250 | 0.4432 | 0.4599 | 0.4493 | 0.4563 | 0.4741 | 0.4622 | 0.6356 | 0.6750 | 0.6493 | 0.2795 | 0.3077 | 0.2919 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0469 | 2.6662 | 22500 | 0.4451 | 42.7925 | 0.4403 | 0.4521 | 0.4447 | 0.4662 | 0.4808 | 0.4714 | 0.4814 | 0.4968 | 0.4867 | 0.6632 | 0.6933 | 0.6740 | 0.3118 | 0.3333 | 0.3144 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0224 | 2.9624 | 25000 | 0.4347 | 42.4672 | 0.4382 | 0.4533 | 0.4443 | 0.4603 | 0.4782 | 0.4674 | 0.4721 | 0.4924 | 0.4799 | 0.6405 | 0.6819 | 0.6567 | 0.3204 | 0.3536 | 0.3312 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0435 | 3.2587 | 27500 | 0.4208 | 40.8420 | 0.4435 | 0.4582 | 0.4493 | 0.4658 | 0.4829 | 0.4724 | 0.4791 | 0.4971 | 0.4860 | 0.6601 | 0.6978 | 0.6746 | 0.3333 | 0.3529 | 0.3352 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0379 | 3.5549 | 30000 | 0.4347 | 42.3782 | 0.4409 | 0.4491 | 0.4438 | 0.4652 | 0.4750 | 0.4684 | 0.4796 | 0.4898 | 0.4822 | 0.6691 | 0.6910 | 0.6760 | 0.3066 | 0.3295 | 0.3136 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0099 | 3.8512 | 32500 | 0.4303 | 43.9873 | 0.4492 | 0.4603 | 0.4534 | 0.4726 | 0.4869 | 0.4776 | 0.4849 | 0.5005 | 0.4902 | 0.6510 | 0.6820 | 0.6619 | 0.3333 | 0.3571 | 0.3388 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0185 | 4.1474 | 35000 | 0.4299 | 41.5102 | 0.4465 | 0.4562 | 0.4502 | 0.4709 | 0.4819 | 0.4749 | 0.4856 | 0.4980 | 0.4898 | 0.6627 | 0.6909 | 0.6726 | 0.3239 | 0.3483 | 0.3333 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0077 | 4.4437 | 37500 | 0.4209 | 41.2943 | 0.4518 | 0.4630 | 0.4559 | 0.4754 | 0.4885 | 0.4800 | 0.4885 | 0.5024 | 0.4932 | 0.6663 | 0.6955 | 0.6756 | 0.3429 | 0.3684 | 0.3552 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0126 | 4.7399 | 40000 | 0.4175 | 38.7048 | 0.4651 | 0.4746 | 0.4687 | 0.4893 | 0.5012 | 0.4936 | 0.5021 | 0.5152 | 0.5069 | 0.6841 | 0.7125 | 0.6943 | 0.3704 | 0.3974 | 0.3851 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0035 | 5.0361 | 42500 | 0.4141 | 37.2328 | 0.4746 | 0.4863 | 0.4793 | 0.4966 | 0.5105 | 0.5021 | 0.5085 | 0.5241 | 0.5143 | 0.6849 | 0.7159 | 0.6969 | 0.3974 | 0.4231 | 0.4114 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0085 | 5.3324 | 45000 | 0.4234 | 40.2366 | 0.4689 | 0.4787 | 0.4727 | 0.4922 | 0.5037 | 0.4962 | 0.5055 | 0.5182 | 0.5093 | 0.6792 | 0.7057 | 0.6880 | 0.375 | 0.4054 | 0.3885 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0141 | 5.6286 | 47500 | 0.4107 | 38.0483 | 0.4664 | 0.4765 | 0.4703 | 0.4871 | 0.4991 | 0.4916 | 0.4988 | 0.5122 | 0.5033 | 0.6747 | 0.7043 | 0.6856 | 0.3675 | 0.3921 | 0.3846 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0026 | 5.9249 | 50000 | 0.4120 | 37.9521 | 0.4745 | 0.4862 | 0.4788 | 0.4953 | 0.5094 | 0.5004 | 0.5071 | 0.5226 | 0.5127 | 0.6811 | 0.7128 | 0.6928 | 0.4 | 0.4167 | 0.4065 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0059 | 6.2211 | 52500 | 0.4140 | 36.1839 | 0.4890 | 0.4993 | 0.4931 | 0.5123 | 0.5244 | 0.5167 | 0.5241 | 0.5372 | 0.5288 | 0.6995 | 0.7273 | 0.7099 | 0.4444 | 0.4674 | 0.4528 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0069 | 6.5174 | 55000 | 0.4083 | 37.5086 | 0.4888 | 0.4982 | 0.4924 | 0.5093 | 0.5203 | 0.5133 | 0.5221 | 0.5340 | 0.5261 | 0.6960 | 0.7203 | 0.7042 | 0.4329 | 0.4545 | 0.4430 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0042 | 6.8136 | 57500 | 0.4116 | 36.6785 | 0.4906 | 0.5013 | 0.4948 | 0.5110 | 0.5233 | 0.5157 | 0.5225 | 0.5356 | 0.5274 | 0.6950 | 0.7236 | 0.7059 | 0.4336 | 0.4615 | 0.4444 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.004 | 7.1098 | 60000 | 0.4107 | 36.2146 | 0.4891 | 0.4995 | 0.4932 | 0.5095 | 0.5218 | 0.5143 | 0.5211 | 0.5357 | 0.5263 | 0.6939 | 0.7234 | 0.7047 | 0.4432 | 0.4756 | 0.4528 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 2.12.0
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+ - Tokenizers 0.20.1
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