marcelovidigal/bert-base-multilingual-cased-2-contract-sections-classification-v4-50

24b3271 verified 6 months ago

31.9 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model: google-bert/bert-base-multilingual-cased
	tags:
	- generated_from_trainer
	model-index:
	- name: bert-base-multilingual-cased-2-contract-sections-classification-v4-50
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	[<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/mvgdr/classificacao-secoes-contratos-v4-bert-base/runs/3f73rktu)
	[<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/mvgdr/classificacao-secoes-contratos-v4-bert-base/runs/il9rngdd)
	# bert-base-multilingual-cased-2-contract-sections-classification-v4-50

	This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2447
	- Accuracy Evaluate: 0.9625
	- Precision Evaluate: 0.9524
	- Recall Evaluate: 0.9635
	- F1 Evaluate: 0.9558
	- Accuracy Sklearn: 0.9625
	- Precision Sklearn: 0.9657
	- Recall Sklearn: 0.9625
	- F1 Sklearn: 0.9631
	- Acuracia Rotulo Objeto: 0.9917
	- Acuracia Rotulo Obrigacoes: 0.9663
	- Acuracia Rotulo Valor: 0.9427
	- Acuracia Rotulo Vigencia: 0.9869
	- Acuracia Rotulo Rescisao: 0.9114
	- Acuracia Rotulo Foro: 0.9423
	- Acuracia Rotulo Reajuste: 0.9715
	- Acuracia Rotulo Fiscalizacao: 0.9306
	- Acuracia Rotulo Publicacao: 1.0
	- Acuracia Rotulo Pagamento: 0.9746
	- Acuracia Rotulo Casos Omissos: 0.9261
	- Acuracia Rotulo Sancoes: 0.9817
	- Acuracia Rotulo Dotacao Orcamentaria: 1.0

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1e-06
	- train_batch_size: 2
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 16
	- total_train_batch_size: 32
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy Evaluate \| Precision Evaluate \| Recall Evaluate \| F1 Evaluate \| Accuracy Sklearn \| Precision Sklearn \| Recall Sklearn \| F1 Sklearn \| Acuracia Rotulo Objeto \| Acuracia Rotulo Obrigacoes \| Acuracia Rotulo Valor \| Acuracia Rotulo Vigencia \| Acuracia Rotulo Rescisao \| Acuracia Rotulo Foro \| Acuracia Rotulo Reajuste \| Acuracia Rotulo Fiscalizacao \| Acuracia Rotulo Publicacao \| Acuracia Rotulo Pagamento \| Acuracia Rotulo Casos Omissos \| Acuracia Rotulo Sancoes \| Acuracia Rotulo Dotacao Orcamentaria \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:------------------:\|:---------------:\|:-----------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------------:\|:--------------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:--------------------:\|:------------------------:\|:----------------------------:\|:--------------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------------------------:\|
	\| 2.2161 \| 1.0 \| 500 \| 1.8294 \| 0.568 \| 0.6269 \| 0.5325 \| 0.4985 \| 0.568 \| 0.6153 \| 0.568 \| 0.5258 \| 0.8740 \| 0.8822 \| 0.6361 \| 0.5932 \| 0.3823 \| 0.4385 \| 0.3950 \| 0.2397 \| 0.9163 \| 0.0 \| 0.8621 \| 0.6972 \| 0.0055 \|
	\| 1.2279 \| 2.0 \| 1000 \| 0.8815 \| 0.8842 \| 0.9052 \| 0.8856 \| 0.8882 \| 0.8842 \| 0.8958 \| 0.8842 \| 0.8825 \| 0.9607 \| 0.9394 \| 0.9284 \| 0.7375 \| 0.9280 \| 0.9385 \| 0.9573 \| 0.7319 \| 0.9803 \| 0.6087 \| 0.9310 \| 0.9266 \| 0.9451 \|
	\| 0.5798 \| 3.0 \| 1500 \| 0.4362 \| 0.9345 \| 0.9364 \| 0.9424 \| 0.9383 \| 0.9345 \| 0.9376 \| 0.9345 \| 0.9346 \| 0.9649 \| 0.8485 \| 0.9284 \| 0.9764 \| 0.9529 \| 0.9385 \| 0.9893 \| 0.9022 \| 0.9852 \| 0.8841 \| 0.9360 \| 0.9450 \| 1.0 \|
	\| 0.3013 \| 4.0 \| 2000 \| 0.2795 \| 0.9415 \| 0.9357 \| 0.9502 \| 0.9407 \| 0.9415 \| 0.9458 \| 0.9415 \| 0.9420 \| 0.9731 \| 0.8603 \| 0.9312 \| 0.9948 \| 0.9335 \| 0.9385 \| 0.9929 \| 0.9148 \| 0.9852 \| 0.9058 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.1844 \| 5.0 \| 2500 \| 0.2176 \| 0.949 \| 0.9413 \| 0.9549 \| 0.9462 \| 0.949 \| 0.9521 \| 0.949 \| 0.9495 \| 0.9835 \| 0.9040 \| 0.9484 \| 0.9895 \| 0.9197 \| 0.9385 \| 0.9680 \| 0.9148 \| 0.9951 \| 0.9203 \| 0.9409 \| 0.9908 \| 1.0 \|
	\| 0.1245 \| 6.0 \| 3000 \| 0.1780 \| 0.9605 \| 0.9536 \| 0.9630 \| 0.9572 \| 0.9605 \| 0.9619 \| 0.9605 \| 0.9606 \| 0.9814 \| 0.9545 \| 0.9312 \| 0.9869 \| 0.9335 \| 0.9385 \| 0.9964 \| 0.9148 \| 0.9951 \| 0.9638 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0911 \| 7.0 \| 3500 \| 0.1572 \| 0.9643 \| 0.9550 \| 0.9658 \| 0.9582 \| 0.9643 \| 0.9675 \| 0.9643 \| 0.9649 \| 0.9938 \| 0.9697 \| 0.9427 \| 0.9816 \| 0.9169 \| 0.9385 \| 0.9964 \| 0.9180 \| 0.9951 \| 0.9710 \| 0.9409 \| 0.9908 \| 1.0 \|
	\| 0.0708 \| 8.0 \| 4000 \| 0.1551 \| 0.963 \| 0.9530 \| 0.9661 \| 0.9572 \| 0.963 \| 0.9664 \| 0.963 \| 0.9637 \| 0.9855 \| 0.9596 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9346 \| 0.9964 \| 0.9338 \| 0.9951 \| 0.9710 \| 0.9458 \| 1.0 \| 1.0 \|
	\| 0.0591 \| 9.0 \| 4500 \| 0.1480 \| 0.9617 \| 0.9528 \| 0.9620 \| 0.9556 \| 0.9617 \| 0.9644 \| 0.9617 \| 0.9622 \| 0.9917 \| 0.9697 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9680 \| 0.9180 \| 0.9951 \| 0.9710 \| 0.9310 \| 0.9725 \| 1.0 \|
	\| 0.0489 \| 10.0 \| 5000 \| 0.1541 \| 0.962 \| 0.9528 \| 0.9627 \| 0.9556 \| 0.962 \| 0.9652 \| 0.962 \| 0.9626 \| 0.9917 \| 0.9697 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9680 \| 0.9180 \| 0.9951 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0427 \| 11.0 \| 5500 \| 0.1543 \| 0.9635 \| 0.9538 \| 0.9656 \| 0.9576 \| 0.9635 \| 0.9665 \| 0.9635 \| 0.9641 \| 0.9917 \| 0.9714 \| 0.9456 \| 0.9843 \| 0.9114 \| 0.9423 \| 0.9644 \| 0.9180 \| 0.9951 \| 0.9783 \| 0.9507 \| 1.0 \| 1.0 \|
	\| 0.0351 \| 12.0 \| 6000 \| 0.1583 \| 0.964 \| 0.9552 \| 0.9660 \| 0.9587 \| 0.964 \| 0.9666 \| 0.964 \| 0.9645 \| 0.9917 \| 0.9714 \| 0.9513 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9644 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9458 \| 1.0 \| 1.0 \|
	\| 0.03 \| 13.0 \| 6500 \| 0.1687 \| 0.961 \| 0.9512 \| 0.9623 \| 0.9546 \| 0.961 \| 0.9642 \| 0.961 \| 0.9617 \| 0.9917 \| 0.9663 \| 0.9513 \| 0.9843 \| 0.9114 \| 0.9423 \| 0.9466 \| 0.9180 \| 1.0 \| 0.9710 \| 0.9458 \| 0.9817 \| 1.0 \|
	\| 0.029 \| 14.0 \| 7000 \| 0.1652 \| 0.9627 \| 0.9544 \| 0.9639 \| 0.9578 \| 0.9627 \| 0.9647 \| 0.9627 \| 0.9631 \| 0.9938 \| 0.9697 \| 0.9513 \| 0.9843 \| 0.9114 \| 0.9423 \| 0.9466 \| 0.9211 \| 1.0 \| 0.9783 \| 0.9507 \| 0.9817 \| 1.0 \|
	\| 0.0239 \| 15.0 \| 7500 \| 0.1638 \| 0.9665 \| 0.9566 \| 0.9687 \| 0.9606 \| 0.9665 \| 0.9695 \| 0.9665 \| 0.9671 \| 0.9938 \| 0.9697 \| 0.9513 \| 0.9869 \| 0.9114 \| 0.9462 \| 0.9786 \| 0.9306 \| 1.0 \| 0.9783 \| 0.9458 \| 1.0 \| 1.0 \|
	\| 0.0219 \| 16.0 \| 8000 \| 0.1640 \| 0.964 \| 0.9542 \| 0.9654 \| 0.9578 \| 0.964 \| 0.9670 \| 0.964 \| 0.9646 \| 0.9897 \| 0.9697 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9786 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9458 \| 0.9817 \| 1.0 \|
	\| 0.0204 \| 17.0 \| 8500 \| 0.1653 \| 0.9657 \| 0.9560 \| 0.9676 \| 0.9600 \| 0.9657 \| 0.9683 \| 0.9657 \| 0.9662 \| 0.9938 \| 0.9663 \| 0.9513 \| 0.9869 \| 0.9114 \| 0.9462 \| 0.9786 \| 0.9338 \| 1.0 \| 0.9783 \| 0.9409 \| 0.9908 \| 1.0 \|
	\| 0.0187 \| 18.0 \| 9000 \| 0.1824 \| 0.9647 \| 0.9552 \| 0.9661 \| 0.9590 \| 0.9647 \| 0.9671 \| 0.9647 \| 0.9652 \| 0.9938 \| 0.9646 \| 0.9513 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9786 \| 0.9338 \| 1.0 \| 0.9783 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0165 \| 19.0 \| 9500 \| 0.1762 \| 0.9653 \| 0.9558 \| 0.9660 \| 0.9587 \| 0.9653 \| 0.9686 \| 0.9653 \| 0.9659 \| 0.9917 \| 0.9764 \| 0.9542 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9715 \| 0.9211 \| 1.0 \| 0.9746 \| 0.9458 \| 0.9817 \| 1.0 \|
	\| 0.0147 \| 20.0 \| 10000 \| 0.1952 \| 0.9653 \| 0.9578 \| 0.9664 \| 0.9610 \| 0.9653 \| 0.9669 \| 0.9653 \| 0.9655 \| 0.9917 \| 0.9680 \| 0.9542 \| 0.9869 \| 0.9141 \| 0.9385 \| 0.9786 \| 0.9338 \| 1.0 \| 0.9746 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0134 \| 21.0 \| 10500 \| 0.2101 \| 0.964 \| 0.9556 \| 0.9648 \| 0.9588 \| 0.964 \| 0.9660 \| 0.964 \| 0.9643 \| 0.9917 \| 0.9680 \| 0.9570 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9680 \| 0.9338 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0117 \| 22.0 \| 11000 \| 0.1971 \| 0.9647 \| 0.9560 \| 0.9656 \| 0.9593 \| 0.9647 \| 0.9669 \| 0.9647 \| 0.9651 \| 0.9938 \| 0.9697 \| 0.9513 \| 0.9843 \| 0.9114 \| 0.9423 \| 0.9786 \| 0.9338 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0113 \| 23.0 \| 11500 \| 0.2037 \| 0.9645 \| 0.9545 \| 0.9655 \| 0.9579 \| 0.9645 \| 0.9677 \| 0.9645 \| 0.9652 \| 0.9917 \| 0.9697 \| 0.9542 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9338 \| 1.0 \| 0.9710 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0108 \| 24.0 \| 12000 \| 0.2033 \| 0.9645 \| 0.9542 \| 0.9657 \| 0.9579 \| 0.9645 \| 0.9677 \| 0.9645 \| 0.9652 \| 0.9917 \| 0.9663 \| 0.9542 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0098 \| 25.0 \| 12500 \| 0.1980 \| 0.9647 \| 0.9555 \| 0.9655 \| 0.9585 \| 0.9647 \| 0.9678 \| 0.9647 \| 0.9654 \| 0.9917 \| 0.9731 \| 0.9484 \| 0.9843 \| 0.9114 \| 0.9538 \| 0.9715 \| 0.9338 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0096 \| 26.0 \| 13000 \| 0.2104 \| 0.9627 \| 0.9534 \| 0.9642 \| 0.9567 \| 0.9627 \| 0.9659 \| 0.9627 \| 0.9634 \| 0.9938 \| 0.9613 \| 0.9484 \| 0.9816 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0083 \| 27.0 \| 13500 \| 0.2138 \| 0.9645 \| 0.9548 \| 0.9665 \| 0.9582 \| 0.9645 \| 0.9681 \| 0.9645 \| 0.9652 \| 0.9917 \| 0.9680 \| 0.9542 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9680 \| 0.9338 \| 1.0 \| 0.9710 \| 0.9409 \| 1.0 \| 1.0 \|
	\| 0.0084 \| 28.0 \| 14000 \| 0.2180 \| 0.9633 \| 0.9542 \| 0.9646 \| 0.9578 \| 0.9633 \| 0.9657 \| 0.9633 \| 0.9637 \| 0.9917 \| 0.9646 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9680 \| 0.9369 \| 1.0 \| 0.9710 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0076 \| 29.0 \| 14500 \| 0.2196 \| 0.9623 \| 0.9529 \| 0.9635 \| 0.9565 \| 0.9623 \| 0.9648 \| 0.9623 \| 0.9627 \| 0.9917 \| 0.9630 \| 0.9398 \| 0.9869 \| 0.9141 \| 0.9385 \| 0.9715 \| 0.9369 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.006 \| 30.0 \| 15000 \| 0.2320 \| 0.961 \| 0.9511 \| 0.9626 \| 0.9545 \| 0.961 \| 0.9645 \| 0.961 \| 0.9617 \| 0.9917 \| 0.9663 \| 0.9226 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9338 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9908 \| 1.0 \|
	\| 0.0067 \| 31.0 \| 15500 \| 0.2285 \| 0.9627 \| 0.9527 \| 0.9654 \| 0.9567 \| 0.9627 \| 0.9663 \| 0.9627 \| 0.9634 \| 0.9917 \| 0.9630 \| 0.9284 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9786 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9409 \| 1.0 \| 1.0 \|
	\| 0.0068 \| 32.0 \| 16000 \| 0.2252 \| 0.9625 \| 0.9530 \| 0.9640 \| 0.9566 \| 0.9625 \| 0.9652 \| 0.9625 \| 0.9630 \| 0.9917 \| 0.9646 \| 0.9370 \| 0.9869 \| 0.9141 \| 0.9385 \| 0.9715 \| 0.9338 \| 1.0 \| 0.9710 \| 0.9409 \| 0.9817 \| 1.0 \|
	\| 0.0062 \| 33.0 \| 16500 \| 0.2291 \| 0.963 \| 0.9530 \| 0.9641 \| 0.9563 \| 0.963 \| 0.9664 \| 0.963 \| 0.9637 \| 0.9917 \| 0.9663 \| 0.9427 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9786 \| 0.9338 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.005 \| 34.0 \| 17000 \| 0.2376 \| 0.9627 \| 0.9529 \| 0.9641 \| 0.9563 \| 0.9627 \| 0.9660 \| 0.9627 \| 0.9634 \| 0.9917 \| 0.9630 \| 0.9456 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9369 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0055 \| 35.0 \| 17500 \| 0.2371 \| 0.9627 \| 0.9519 \| 0.9640 \| 0.9557 \| 0.9627 \| 0.9662 \| 0.9627 \| 0.9635 \| 0.9917 \| 0.9646 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9369 \| 1.0 \| 0.9783 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0059 \| 36.0 \| 18000 \| 0.2300 \| 0.9623 \| 0.9532 \| 0.9633 \| 0.9562 \| 0.9623 \| 0.9653 \| 0.9623 \| 0.9628 \| 0.9917 \| 0.9663 \| 0.9427 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9751 \| 0.9274 \| 1.0 \| 0.9710 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0045 \| 37.0 \| 18500 \| 0.2400 \| 0.9625 \| 0.9525 \| 0.9639 \| 0.9561 \| 0.9625 \| 0.9656 \| 0.9625 \| 0.9631 \| 0.9917 \| 0.9630 \| 0.9370 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9786 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0042 \| 38.0 \| 19000 \| 0.2514 \| 0.9627 \| 0.9532 \| 0.9638 \| 0.9564 \| 0.9627 \| 0.9660 \| 0.9627 \| 0.9634 \| 0.9917 \| 0.9646 \| 0.9456 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9338 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0044 \| 39.0 \| 19500 \| 0.2389 \| 0.9635 \| 0.9533 \| 0.9646 \| 0.9570 \| 0.9635 \| 0.9665 \| 0.9635 \| 0.9641 \| 0.9917 \| 0.9646 \| 0.9513 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0045 \| 40.0 \| 20000 \| 0.2412 \| 0.9613 \| 0.9509 \| 0.9625 \| 0.9544 \| 0.9613 \| 0.9647 \| 0.9613 \| 0.9619 \| 0.9917 \| 0.9663 \| 0.9226 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9751 \| 0.9306 \| 1.0 \| 0.9783 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0039 \| 41.0 \| 20500 \| 0.2447 \| 0.9617 \| 0.9516 \| 0.9629 \| 0.9550 \| 0.9617 \| 0.9652 \| 0.9617 \| 0.9625 \| 0.9917 \| 0.9663 \| 0.9370 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9715 \| 0.9274 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0033 \| 42.0 \| 21000 \| 0.2442 \| 0.962 \| 0.9515 \| 0.9633 \| 0.9551 \| 0.962 \| 0.9655 \| 0.962 \| 0.9627 \| 0.9917 \| 0.9646 \| 0.9370 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9306 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0042 \| 43.0 \| 21500 \| 0.2431 \| 0.963 \| 0.9527 \| 0.9640 \| 0.9561 \| 0.963 \| 0.9665 \| 0.963 \| 0.9637 \| 0.9917 \| 0.9663 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9306 \| 1.0 \| 0.9710 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0031 \| 44.0 \| 22000 \| 0.2500 \| 0.9625 \| 0.9521 \| 0.9636 \| 0.9556 \| 0.9625 \| 0.9661 \| 0.9625 \| 0.9632 \| 0.9917 \| 0.9646 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.004 \| 45.0 \| 22500 \| 0.2495 \| 0.9627 \| 0.9520 \| 0.9640 \| 0.9557 \| 0.9627 \| 0.9663 \| 0.9627 \| 0.9635 \| 0.9917 \| 0.9646 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9751 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9310 \| 0.9817 \| 1.0 \|
	\| 0.0028 \| 46.0 \| 23000 \| 0.2459 \| 0.9627 \| 0.9525 \| 0.9639 \| 0.9561 \| 0.9627 \| 0.9660 \| 0.9627 \| 0.9634 \| 0.9917 \| 0.9646 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9751 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0028 \| 47.0 \| 23500 \| 0.2476 \| 0.963 \| 0.9527 \| 0.9640 \| 0.9562 \| 0.963 \| 0.9663 \| 0.963 \| 0.9637 \| 0.9917 \| 0.9646 \| 0.9484 \| 0.9869 \| 0.9114 \| 0.9385 \| 0.9715 \| 0.9369 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.003 \| 48.0 \| 24000 \| 0.2437 \| 0.9625 \| 0.9524 \| 0.9641 \| 0.9559 \| 0.9625 \| 0.9659 \| 0.9625 \| 0.9632 \| 0.9917 \| 0.9663 \| 0.9370 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9751 \| 0.9306 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9908 \| 1.0 \|
	\| 0.0034 \| 49.0 \| 24500 \| 0.2444 \| 0.9623 \| 0.9524 \| 0.9632 \| 0.9556 \| 0.9623 \| 0.9655 \| 0.9623 \| 0.9629 \| 0.9917 \| 0.9680 \| 0.9398 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9751 \| 0.9243 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|
	\| 0.0027 \| 50.0 \| 25000 \| 0.2447 \| 0.9625 \| 0.9524 \| 0.9635 \| 0.9558 \| 0.9625 \| 0.9657 \| 0.9625 \| 0.9631 \| 0.9917 \| 0.9663 \| 0.9427 \| 0.9869 \| 0.9114 \| 0.9423 \| 0.9715 \| 0.9306 \| 1.0 \| 0.9746 \| 0.9261 \| 0.9817 \| 1.0 \|


	### Framework versions

	- Transformers 4.51.3
	- Pytorch 2.7.0+cu126
	- Datasets 3.5.1
	- Tokenizers 0.21.1