Bert_Text_Classification_v4 / README.md

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	---
	license: apache-2.0
	base_model: bert-base-multilingual-uncased
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: Bert_Text_Classification_v4
	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. -->

	# Bert_Text_Classification_v4

	This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0376
	- Accuracy: 0.9964
	- F1: 0.9963
	- Precision: 0.9963
	- Recall: 0.9963

	## 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: 5e-05
	- train_batch_size: 16
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 0.0043 \| 0.36 \| 50 \| 0.0399 \| 0.9955 \| 0.9953 \| 0.9954 \| 0.9951 \|
	\| 0.0001 \| 0.72 \| 100 \| 0.0226 \| 0.9964 \| 0.9961 \| 0.9962 \| 0.9961 \|
	\| 0.0193 \| 1.09 \| 150 \| 0.0668 \| 0.9900 \| 0.9893 \| 0.9905 \| 0.9884 \|
	\| 0.0555 \| 1.45 \| 200 \| 0.0504 \| 0.9927 \| 0.9927 \| 0.9934 \| 0.9922 \|
	\| 0.0465 \| 1.81 \| 250 \| 0.0017 \| 0.9991 \| 0.9990 \| 0.9990 \| 0.9991 \|
	\| 0.048 \| 2.17 \| 300 \| 0.0348 \| 0.9936 \| 0.9934 \| 0.9937 \| 0.9932 \|
	\| 0.0513 \| 2.54 \| 350 \| 0.0699 \| 0.9873 \| 0.9870 \| 0.9878 \| 0.9865 \|
	\| 0.0213 \| 2.9 \| 400 \| 0.0495 \| 0.9927 \| 0.9926 \| 0.9925 \| 0.9928 \|
	\| 0.0427 \| 3.26 \| 450 \| 0.0587 \| 0.9936 \| 0.9933 \| 0.9939 \| 0.9928 \|
	\| 0.0097 \| 3.62 \| 500 \| 0.0236 \| 0.9964 \| 0.9961 \| 0.9963 \| 0.9959 \|
	\| 0.0001 \| 3.99 \| 550 \| 0.0279 \| 0.9964 \| 0.9962 \| 0.9964 \| 0.9959 \|
	\| 0.0001 \| 4.35 \| 600 \| 0.0259 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0 \| 4.71 \| 650 \| 0.0260 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0091 \| 5.07 \| 700 \| 0.0216 \| 0.9964 \| 0.9962 \| 0.9964 \| 0.9959 \|
	\| 0.0014 \| 5.43 \| 750 \| 0.0268 \| 0.9973 \| 0.9972 \| 0.9971 \| 0.9972 \|
	\| 0.0 \| 5.8 \| 800 \| 0.0383 \| 0.9955 \| 0.9952 \| 0.9957 \| 0.9947 \|
	\| 0.0 \| 6.16 \| 850 \| 0.0362 \| 0.9964 \| 0.9962 \| 0.9966 \| 0.9958 \|
	\| 0.0003 \| 6.52 \| 900 \| 0.0956 \| 0.9909 \| 0.9904 \| 0.9900 \| 0.9910 \|
	\| 0.0247 \| 6.88 \| 950 \| 0.0285 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0003 \| 7.25 \| 1000 \| 0.0333 \| 0.9964 \| 0.9962 \| 0.9967 \| 0.9958 \|
	\| 0.0001 \| 7.61 \| 1050 \| 0.0334 \| 0.9964 \| 0.9962 \| 0.9967 \| 0.9958 \|
	\| 0.0003 \| 7.97 \| 1100 \| 0.0285 \| 0.9973 \| 0.9972 \| 0.9971 \| 0.9972 \|
	\| 0.0001 \| 8.33 \| 1150 \| 0.0294 \| 0.9964 \| 0.9962 \| 0.9962 \| 0.9962 \|
	\| 0.0 \| 8.7 \| 1200 \| 0.0298 \| 0.9964 \| 0.9962 \| 0.9962 \| 0.9962 \|
	\| 0.0045 \| 9.06 \| 1250 \| 0.0376 \| 0.9955 \| 0.9953 \| 0.9954 \| 0.9951 \|
	\| 0.0004 \| 9.42 \| 1300 \| 0.0450 \| 0.9946 \| 0.9943 \| 0.9943 \| 0.9942 \|
	\| 0.0322 \| 9.78 \| 1350 \| 0.0492 \| 0.9936 \| 0.9932 \| 0.9939 \| 0.9926 \|
	\| 0.003 \| 10.14 \| 1400 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0001 \| 10.51 \| 1450 \| 0.0112 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0001 \| 10.87 \| 1500 \| 0.0124 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 11.23 \| 1550 \| 0.0112 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 11.59 \| 1600 \| 0.0111 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 11.96 \| 1650 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 12.32 \| 1700 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 12.68 \| 1750 \| 0.0109 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 13.04 \| 1800 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 13.41 \| 1850 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 13.77 \| 1900 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.13 \| 1950 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.49 \| 2000 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.86 \| 2050 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.22 \| 2100 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.58 \| 2150 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.94 \| 2200 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 16.3 \| 2250 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 16.67 \| 2300 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.03 \| 2350 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.39 \| 2400 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.75 \| 2450 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 18.12 \| 2500 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 18.48 \| 2550 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0099 \| 18.84 \| 2600 \| 0.0175 \| 0.9973 \| 0.9973 \| 0.9973 \| 0.9973 \|
	\| 0.0 \| 19.2 \| 2650 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 19.57 \| 2700 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 19.93 \| 2750 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 20.29 \| 2800 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 20.65 \| 2850 \| 0.0132 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.01 \| 2900 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.38 \| 2950 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.74 \| 3000 \| 0.0124 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.1 \| 3050 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.46 \| 3100 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.83 \| 3150 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.19 \| 3200 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.55 \| 3250 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.91 \| 3300 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 24.28 \| 3350 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 24.64 \| 3400 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.0 \| 3450 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.36 \| 3500 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.72 \| 3550 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.09 \| 3600 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.45 \| 3650 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.81 \| 3700 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.17 \| 3750 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.54 \| 3800 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.9 \| 3850 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.26 \| 3900 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.62 \| 3950 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.99 \| 4000 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 29.35 \| 4050 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 29.71 \| 4100 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|


	### Framework versions

	- Transformers 4.37.2
	- Pytorch 2.3.0+cu121
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	base_model: bert-base-multilingual-uncased
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: Bert_Text_Classification_v4
	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. -->

	# Bert_Text_Classification_v4

	This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0376
	- Accuracy: 0.9964
	- F1: 0.9963
	- Precision: 0.9963
	- Recall: 0.9963

	## 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: 5e-05
	- train_batch_size: 16
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 0.0043 \| 0.36 \| 50 \| 0.0399 \| 0.9955 \| 0.9953 \| 0.9954 \| 0.9951 \|
	\| 0.0001 \| 0.72 \| 100 \| 0.0226 \| 0.9964 \| 0.9961 \| 0.9962 \| 0.9961 \|
	\| 0.0193 \| 1.09 \| 150 \| 0.0668 \| 0.9900 \| 0.9893 \| 0.9905 \| 0.9884 \|
	\| 0.0555 \| 1.45 \| 200 \| 0.0504 \| 0.9927 \| 0.9927 \| 0.9934 \| 0.9922 \|
	\| 0.0465 \| 1.81 \| 250 \| 0.0017 \| 0.9991 \| 0.9990 \| 0.9990 \| 0.9991 \|
	\| 0.048 \| 2.17 \| 300 \| 0.0348 \| 0.9936 \| 0.9934 \| 0.9937 \| 0.9932 \|
	\| 0.0513 \| 2.54 \| 350 \| 0.0699 \| 0.9873 \| 0.9870 \| 0.9878 \| 0.9865 \|
	\| 0.0213 \| 2.9 \| 400 \| 0.0495 \| 0.9927 \| 0.9926 \| 0.9925 \| 0.9928 \|
	\| 0.0427 \| 3.26 \| 450 \| 0.0587 \| 0.9936 \| 0.9933 \| 0.9939 \| 0.9928 \|
	\| 0.0097 \| 3.62 \| 500 \| 0.0236 \| 0.9964 \| 0.9961 \| 0.9963 \| 0.9959 \|
	\| 0.0001 \| 3.99 \| 550 \| 0.0279 \| 0.9964 \| 0.9962 \| 0.9964 \| 0.9959 \|
	\| 0.0001 \| 4.35 \| 600 \| 0.0259 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0 \| 4.71 \| 650 \| 0.0260 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0091 \| 5.07 \| 700 \| 0.0216 \| 0.9964 \| 0.9962 \| 0.9964 \| 0.9959 \|
	\| 0.0014 \| 5.43 \| 750 \| 0.0268 \| 0.9973 \| 0.9972 \| 0.9971 \| 0.9972 \|
	\| 0.0 \| 5.8 \| 800 \| 0.0383 \| 0.9955 \| 0.9952 \| 0.9957 \| 0.9947 \|
	\| 0.0 \| 6.16 \| 850 \| 0.0362 \| 0.9964 \| 0.9962 \| 0.9966 \| 0.9958 \|
	\| 0.0003 \| 6.52 \| 900 \| 0.0956 \| 0.9909 \| 0.9904 \| 0.9900 \| 0.9910 \|
	\| 0.0247 \| 6.88 \| 950 \| 0.0285 \| 0.9973 \| 0.9972 \| 0.9975 \| 0.9968 \|
	\| 0.0003 \| 7.25 \| 1000 \| 0.0333 \| 0.9964 \| 0.9962 \| 0.9967 \| 0.9958 \|
	\| 0.0001 \| 7.61 \| 1050 \| 0.0334 \| 0.9964 \| 0.9962 \| 0.9967 \| 0.9958 \|
	\| 0.0003 \| 7.97 \| 1100 \| 0.0285 \| 0.9973 \| 0.9972 \| 0.9971 \| 0.9972 \|
	\| 0.0001 \| 8.33 \| 1150 \| 0.0294 \| 0.9964 \| 0.9962 \| 0.9962 \| 0.9962 \|
	\| 0.0 \| 8.7 \| 1200 \| 0.0298 \| 0.9964 \| 0.9962 \| 0.9962 \| 0.9962 \|
	\| 0.0045 \| 9.06 \| 1250 \| 0.0376 \| 0.9955 \| 0.9953 \| 0.9954 \| 0.9951 \|
	\| 0.0004 \| 9.42 \| 1300 \| 0.0450 \| 0.9946 \| 0.9943 \| 0.9943 \| 0.9942 \|
	\| 0.0322 \| 9.78 \| 1350 \| 0.0492 \| 0.9936 \| 0.9932 \| 0.9939 \| 0.9926 \|
	\| 0.003 \| 10.14 \| 1400 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0001 \| 10.51 \| 1450 \| 0.0112 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0001 \| 10.87 \| 1500 \| 0.0124 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 11.23 \| 1550 \| 0.0112 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 11.59 \| 1600 \| 0.0111 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 11.96 \| 1650 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 12.32 \| 1700 \| 0.0110 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 12.68 \| 1750 \| 0.0109 \| 0.9991 \| 0.9991 \| 0.9992 \| 0.9989 \|
	\| 0.0 \| 13.04 \| 1800 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 13.41 \| 1850 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 13.77 \| 1900 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.13 \| 1950 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.49 \| 2000 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 14.86 \| 2050 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.22 \| 2100 \| 0.0109 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.58 \| 2150 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 15.94 \| 2200 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 16.3 \| 2250 \| 0.0110 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 16.67 \| 2300 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.03 \| 2350 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.39 \| 2400 \| 0.0111 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 17.75 \| 2450 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 18.12 \| 2500 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0 \| 18.48 \| 2550 \| 0.0112 \| 0.9991 \| 0.9990 \| 0.9991 \| 0.9989 \|
	\| 0.0099 \| 18.84 \| 2600 \| 0.0175 \| 0.9973 \| 0.9973 \| 0.9973 \| 0.9973 \|
	\| 0.0 \| 19.2 \| 2650 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 19.57 \| 2700 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 19.93 \| 2750 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 20.29 \| 2800 \| 0.0135 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 20.65 \| 2850 \| 0.0132 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.01 \| 2900 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.38 \| 2950 \| 0.0133 \| 0.9982 \| 0.9981 \| 0.9983 \| 0.9979 \|
	\| 0.0 \| 21.74 \| 3000 \| 0.0124 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.1 \| 3050 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.46 \| 3100 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 22.83 \| 3150 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.19 \| 3200 \| 0.0125 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.55 \| 3250 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 23.91 \| 3300 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 24.28 \| 3350 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 24.64 \| 3400 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.0 \| 3450 \| 0.0126 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.36 \| 3500 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 25.72 \| 3550 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.09 \| 3600 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.45 \| 3650 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 26.81 \| 3700 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.17 \| 3750 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.54 \| 3800 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 27.9 \| 3850 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.26 \| 3900 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.62 \| 3950 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 28.99 \| 4000 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 29.35 \| 4050 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|
	\| 0.0 \| 29.71 \| 4100 \| 0.0127 \| 0.9982 \| 0.9981 \| 0.9981 \| 0.9980 \|


	### Framework versions

	- Transformers 4.37.2
	- Pytorch 2.3.0+cu121
	- Tokenizers 0.15.2