Trail_run_final_roberta / README.md

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	---
	base_model: ManojAlexender/roberta-base_MLM
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: Trail_run_final_roberta
	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. -->

	# Trail_run_final_roberta

	This model is a fine-tuned version of [ManojAlexender/roberta-base_MLM](https://huggingface.co/ManojAlexender/roberta-base_MLM) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2322
	- Accuracy: 0.9163
	- F1: 0.9159
	- Precision: 0.9181
	- Recall: 0.9163

	## 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: 64
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 0.4057 \| 0.01 \| 100 \| 0.3391 \| 0.8808 \| 0.8795 \| 0.8865 \| 0.8808 \|
	\| 0.3447 \| 0.01 \| 200 \| 0.3667 \| 0.8470 \| 0.8417 \| 0.8728 \| 0.8470 \|
	\| 0.3202 \| 0.02 \| 300 \| 0.3871 \| 0.8689 \| 0.8656 \| 0.8869 \| 0.8689 \|
	\| 0.2354 \| 0.03 \| 400 \| 0.4020 \| 0.8597 \| 0.8557 \| 0.8805 \| 0.8597 \|
	\| 0.3268 \| 0.04 \| 500 \| 0.3679 \| 0.8318 \| 0.8252 \| 0.8610 \| 0.8318 \|
	\| 0.2771 \| 0.04 \| 600 \| 0.2474 \| 0.8924 \| 0.8912 \| 0.8982 \| 0.8924 \|
	\| 0.2288 \| 0.05 \| 700 \| 0.2297 \| 0.9103 \| 0.9103 \| 0.9103 \| 0.9103 \|
	\| 0.2307 \| 0.06 \| 800 \| 0.2633 \| 0.8944 \| 0.8939 \| 0.8957 \| 0.8944 \|
	\| 0.3375 \| 0.06 \| 900 \| 0.2458 \| 0.8988 \| 0.8979 \| 0.9024 \| 0.8988 \|
	\| 0.26 \| 0.07 \| 1000 \| 0.2428 \| 0.9071 \| 0.9065 \| 0.9099 \| 0.9071 \|
	\| 0.274 \| 0.08 \| 1100 \| 0.2395 \| 0.9035 \| 0.9036 \| 0.9036 \| 0.9035 \|
	\| 0.2513 \| 0.09 \| 1200 \| 0.4167 \| 0.8569 \| 0.8532 \| 0.8751 \| 0.8569 \|
	\| 0.2281 \| 0.09 \| 1300 \| 0.3968 \| 0.8633 \| 0.8598 \| 0.8815 \| 0.8633 \|
	\| 0.249 \| 0.1 \| 1400 \| 0.2548 \| 0.8804 \| 0.8783 \| 0.8920 \| 0.8804 \|
	\| 0.1986 \| 0.11 \| 1500 \| 0.2590 \| 0.9020 \| 0.9020 \| 0.9021 \| 0.9020 \|
	\| 0.26 \| 0.11 \| 1600 \| 0.3084 \| 0.8804 \| 0.8784 \| 0.8913 \| 0.8804 \|
	\| 0.2272 \| 0.12 \| 1700 \| 0.2827 \| 0.8884 \| 0.8870 \| 0.8956 \| 0.8884 \|
	\| 0.2312 \| 0.13 \| 1800 \| 0.2373 \| 0.9067 \| 0.9068 \| 0.9068 \| 0.9067 \|
	\| 0.2563 \| 0.14 \| 1900 \| 0.2628 \| 0.9008 \| 0.9008 \| 0.9011 \| 0.9008 \|
	\| 0.1876 \| 0.14 \| 2000 \| 0.2744 \| 0.8852 \| 0.8840 \| 0.8906 \| 0.8852 \|
	\| 0.284 \| 0.15 \| 2100 \| 0.2751 \| 0.8928 \| 0.8914 \| 0.9002 \| 0.8928 \|
	\| 0.203 \| 0.16 \| 2200 \| 0.2406 \| 0.9031 \| 0.9034 \| 0.9054 \| 0.9031 \|
	\| 0.2278 \| 0.16 \| 2300 \| 0.2378 \| 0.9115 \| 0.9112 \| 0.9123 \| 0.9115 \|
	\| 0.2204 \| 0.17 \| 2400 \| 0.4288 \| 0.8677 \| 0.8646 \| 0.8837 \| 0.8677 \|
	\| 0.2323 \| 0.18 \| 2500 \| 0.2331 \| 0.9115 \| 0.9113 \| 0.9118 \| 0.9115 \|
	\| 0.2508 \| 0.19 \| 2600 \| 0.2932 \| 0.8956 \| 0.8955 \| 0.8955 \| 0.8956 \|
	\| 0.2838 \| 0.19 \| 2700 \| 0.2454 \| 0.9035 \| 0.9036 \| 0.9037 \| 0.9035 \|
	\| 0.221 \| 0.2 \| 2800 \| 0.3153 \| 0.8800 \| 0.8783 \| 0.8881 \| 0.8800 \|
	\| 0.2167 \| 0.21 \| 2900 \| 0.3200 \| 0.8745 \| 0.8724 \| 0.8838 \| 0.8745 \|
	\| 0.2336 \| 0.21 \| 3000 \| 0.2842 \| 0.8880 \| 0.8866 \| 0.8947 \| 0.8880 \|
	\| 0.2653 \| 0.22 \| 3100 \| 0.2353 \| 0.9059 \| 0.9059 \| 0.9059 \| 0.9059 \|
	\| 0.2953 \| 0.23 \| 3200 \| 0.2374 \| 0.9051 \| 0.9044 \| 0.9087 \| 0.9051 \|
	\| 0.174 \| 0.24 \| 3300 \| 0.2810 \| 0.8964 \| 0.8954 \| 0.9006 \| 0.8964 \|
	\| 0.2184 \| 0.24 \| 3400 \| 0.2127 \| 0.9127 \| 0.9125 \| 0.9131 \| 0.9127 \|
	\| 0.2519 \| 0.25 \| 3500 \| 0.2286 \| 0.9083 \| 0.9085 \| 0.9126 \| 0.9083 \|
	\| 0.2326 \| 0.26 \| 3600 \| 0.2904 \| 0.8948 \| 0.8944 \| 0.8956 \| 0.8948 \|
	\| 0.1862 \| 0.26 \| 3700 \| 0.2203 \| 0.9259 \| 0.9258 \| 0.9259 \| 0.9259 \|
	\| 0.2098 \| 0.27 \| 3800 \| 0.2350 \| 0.9075 \| 0.9074 \| 0.9077 \| 0.9075 \|
	\| 0.2152 \| 0.28 \| 3900 \| 0.2319 \| 0.9063 \| 0.9063 \| 0.9063 \| 0.9063 \|
	\| 0.3154 \| 0.29 \| 4000 \| 0.2184 \| 0.9071 \| 0.9070 \| 0.9072 \| 0.9071 \|
	\| 0.1679 \| 0.29 \| 4100 \| 0.4091 \| 0.8764 \| 0.8740 \| 0.8892 \| 0.8764 \|
	\| 0.1535 \| 0.3 \| 4200 \| 0.2574 \| 0.9091 \| 0.9090 \| 0.9092 \| 0.9091 \|
	\| 0.1487 \| 0.31 \| 4300 \| 0.2510 \| 0.9063 \| 0.9060 \| 0.9072 \| 0.9063 \|
	\| 0.2337 \| 0.31 \| 4400 \| 0.2163 \| 0.9131 \| 0.9128 \| 0.9138 \| 0.9131 \|
	\| 0.3144 \| 0.32 \| 4500 \| 0.2627 \| 0.9051 \| 0.9047 \| 0.9062 \| 0.9051 \|
	\| 0.2487 \| 0.33 \| 4600 \| 0.2557 \| 0.8992 \| 0.8985 \| 0.9014 \| 0.8992 \|
	\| 0.2194 \| 0.34 \| 4700 \| 0.2363 \| 0.9159 \| 0.9157 \| 0.9163 \| 0.9159 \|
	\| 0.2602 \| 0.34 \| 4800 \| 0.2374 \| 0.9051 \| 0.9053 \| 0.9058 \| 0.9051 \|
	\| 0.2353 \| 0.35 \| 4900 \| 0.2482 \| 0.9059 \| 0.9057 \| 0.9062 \| 0.9059 \|
	\| 0.2107 \| 0.36 \| 5000 \| 0.2903 \| 0.9008 \| 0.8998 \| 0.9052 \| 0.9008 \|
	\| 0.2364 \| 0.36 \| 5100 \| 0.2901 \| 0.8760 \| 0.8746 \| 0.8815 \| 0.8760 \|
	\| 0.2009 \| 0.37 \| 5200 \| 0.2491 \| 0.9091 \| 0.9086 \| 0.9116 \| 0.9091 \|
	\| 0.2469 \| 0.38 \| 5300 \| 0.3049 \| 0.8992 \| 0.8988 \| 0.9000 \| 0.8992 \|
	\| 0.162 \| 0.39 \| 5400 \| 0.2847 \| 0.9059 \| 0.9055 \| 0.9071 \| 0.9059 \|
	\| 0.24 \| 0.39 \| 5500 \| 0.2146 \| 0.9135 \| 0.9132 \| 0.9143 \| 0.9135 \|
	\| 0.2667 \| 0.4 \| 5600 \| 0.2379 \| 0.9075 \| 0.9072 \| 0.9085 \| 0.9075 \|
	\| 0.2165 \| 0.41 \| 5700 \| 0.2662 \| 0.8844 \| 0.8829 \| 0.8915 \| 0.8844 \|
	\| 0.2007 \| 0.41 \| 5800 \| 0.2539 \| 0.9047 \| 0.9039 \| 0.9087 \| 0.9047 \|
	\| 0.221 \| 0.42 \| 5900 \| 0.2272 \| 0.9047 \| 0.9046 \| 0.9047 \| 0.9047 \|
	\| 0.2028 \| 0.43 \| 6000 \| 0.3618 \| 0.8669 \| 0.8638 \| 0.8826 \| 0.8669 \|
	\| 0.3003 \| 0.44 \| 6100 \| 0.2454 \| 0.9071 \| 0.9071 \| 0.9071 \| 0.9071 \|
	\| 0.2025 \| 0.44 \| 6200 \| 0.2103 \| 0.9175 \| 0.9175 \| 0.9175 \| 0.9175 \|
	\| 0.253 \| 0.45 \| 6300 \| 0.2470 \| 0.8992 \| 0.8981 \| 0.9044 \| 0.8992 \|
	\| 0.1955 \| 0.46 \| 6400 \| 0.2887 \| 0.9000 \| 0.8992 \| 0.9031 \| 0.9000 \|
	\| 0.1621 \| 0.46 \| 6500 \| 0.2245 \| 0.9151 \| 0.9149 \| 0.9155 \| 0.9151 \|
	\| 0.2532 \| 0.47 \| 6600 \| 0.2493 \| 0.8912 \| 0.8907 \| 0.8924 \| 0.8912 \|
	\| 0.1898 \| 0.48 \| 6700 \| 0.2313 \| 0.9083 \| 0.9082 \| 0.9083 \| 0.9083 \|
	\| 0.1858 \| 0.49 \| 6800 \| 0.2514 \| 0.9031 \| 0.9026 \| 0.9049 \| 0.9031 \|
	\| 0.1977 \| 0.49 \| 6900 \| 0.2155 \| 0.9167 \| 0.9166 \| 0.9167 \| 0.9167 \|
	\| 0.2247 \| 0.5 \| 7000 \| 0.2280 \| 0.9059 \| 0.9056 \| 0.9070 \| 0.9059 \|
	\| 0.1931 \| 0.51 \| 7100 \| 0.2431 \| 0.9047 \| 0.9042 \| 0.9066 \| 0.9047 \|
	\| 0.1746 \| 0.51 \| 7200 \| 0.2400 \| 0.9155 \| 0.9152 \| 0.9164 \| 0.9155 \|
	\| 0.2579 \| 0.52 \| 7300 \| 0.2707 \| 0.9107 \| 0.9102 \| 0.9125 \| 0.9107 \|
	\| 0.2139 \| 0.53 \| 7400 \| 0.2625 \| 0.8920 \| 0.8910 \| 0.8965 \| 0.8920 \|
	\| 0.2703 \| 0.54 \| 7500 \| 0.2500 \| 0.8980 \| 0.8972 \| 0.9013 \| 0.8980 \|
	\| 0.1412 \| 0.54 \| 7600 \| 0.2210 \| 0.9159 \| 0.9158 \| 0.9160 \| 0.9159 \|
	\| 0.2382 \| 0.55 \| 7700 \| 0.2712 \| 0.9028 \| 0.9020 \| 0.9064 \| 0.9028 \|
	\| 0.2498 \| 0.56 \| 7800 \| 0.2200 \| 0.9195 \| 0.9193 \| 0.9197 \| 0.9195 \|
	\| 0.2002 \| 0.56 \| 7900 \| 0.3254 \| 0.8832 \| 0.8813 \| 0.8935 \| 0.8832 \|
	\| 0.2359 \| 0.57 \| 8000 \| 0.3023 \| 0.8928 \| 0.8918 \| 0.8973 \| 0.8928 \|
	\| 0.2193 \| 0.58 \| 8100 \| 0.2837 \| 0.8892 \| 0.8875 \| 0.8988 \| 0.8892 \|
	\| 0.2436 \| 0.59 \| 8200 \| 0.2221 \| 0.9143 \| 0.9142 \| 0.9143 \| 0.9143 \|
	\| 0.1704 \| 0.59 \| 8300 \| 0.2402 \| 0.9123 \| 0.9119 \| 0.9136 \| 0.9123 \|
	\| 0.1979 \| 0.6 \| 8400 \| 0.2722 \| 0.8912 \| 0.8896 \| 0.9003 \| 0.8912 \|
	\| 0.2476 \| 0.61 \| 8500 \| 0.2165 \| 0.9211 \| 0.9209 \| 0.9216 \| 0.9211 \|
	\| 0.1996 \| 0.61 \| 8600 \| 0.2374 \| 0.9151 \| 0.9148 \| 0.9163 \| 0.9151 \|
	\| 0.2278 \| 0.62 \| 8700 \| 0.2357 \| 0.9079 \| 0.9080 \| 0.9083 \| 0.9079 \|
	\| 0.1625 \| 0.63 \| 8800 \| 0.2205 \| 0.9231 \| 0.9228 \| 0.9237 \| 0.9231 \|
	\| 0.2197 \| 0.64 \| 8900 \| 0.3041 \| 0.9020 \| 0.9011 \| 0.9063 \| 0.9020 \|
	\| 0.1868 \| 0.64 \| 9000 \| 0.2280 \| 0.9207 \| 0.9205 \| 0.9212 \| 0.9207 \|
	\| 0.2979 \| 0.65 \| 9100 \| 0.2931 \| 0.8948 \| 0.8935 \| 0.9011 \| 0.8948 \|
	\| 0.1973 \| 0.66 \| 9200 \| 0.2322 \| 0.9163 \| 0.9159 \| 0.9181 \| 0.9163 \|


	### Framework versions

	- Transformers 4.39.3
	- Pytorch 2.1.2+cu121
	- Datasets 2.16.1
	- Tokenizers 0.15.1

	---
	base_model: ManojAlexender/roberta-base_MLM
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: Trail_run_final_roberta
	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. -->

	# Trail_run_final_roberta

	This model is a fine-tuned version of [ManojAlexender/roberta-base_MLM](https://huggingface.co/ManojAlexender/roberta-base_MLM) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2322
	- Accuracy: 0.9163
	- F1: 0.9159
	- Precision: 0.9181
	- Recall: 0.9163

	## 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: 64
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 0.4057 \| 0.01 \| 100 \| 0.3391 \| 0.8808 \| 0.8795 \| 0.8865 \| 0.8808 \|
	\| 0.3447 \| 0.01 \| 200 \| 0.3667 \| 0.8470 \| 0.8417 \| 0.8728 \| 0.8470 \|
	\| 0.3202 \| 0.02 \| 300 \| 0.3871 \| 0.8689 \| 0.8656 \| 0.8869 \| 0.8689 \|
	\| 0.2354 \| 0.03 \| 400 \| 0.4020 \| 0.8597 \| 0.8557 \| 0.8805 \| 0.8597 \|
	\| 0.3268 \| 0.04 \| 500 \| 0.3679 \| 0.8318 \| 0.8252 \| 0.8610 \| 0.8318 \|
	\| 0.2771 \| 0.04 \| 600 \| 0.2474 \| 0.8924 \| 0.8912 \| 0.8982 \| 0.8924 \|
	\| 0.2288 \| 0.05 \| 700 \| 0.2297 \| 0.9103 \| 0.9103 \| 0.9103 \| 0.9103 \|
	\| 0.2307 \| 0.06 \| 800 \| 0.2633 \| 0.8944 \| 0.8939 \| 0.8957 \| 0.8944 \|
	\| 0.3375 \| 0.06 \| 900 \| 0.2458 \| 0.8988 \| 0.8979 \| 0.9024 \| 0.8988 \|
	\| 0.26 \| 0.07 \| 1000 \| 0.2428 \| 0.9071 \| 0.9065 \| 0.9099 \| 0.9071 \|
	\| 0.274 \| 0.08 \| 1100 \| 0.2395 \| 0.9035 \| 0.9036 \| 0.9036 \| 0.9035 \|
	\| 0.2513 \| 0.09 \| 1200 \| 0.4167 \| 0.8569 \| 0.8532 \| 0.8751 \| 0.8569 \|
	\| 0.2281 \| 0.09 \| 1300 \| 0.3968 \| 0.8633 \| 0.8598 \| 0.8815 \| 0.8633 \|
	\| 0.249 \| 0.1 \| 1400 \| 0.2548 \| 0.8804 \| 0.8783 \| 0.8920 \| 0.8804 \|
	\| 0.1986 \| 0.11 \| 1500 \| 0.2590 \| 0.9020 \| 0.9020 \| 0.9021 \| 0.9020 \|
	\| 0.26 \| 0.11 \| 1600 \| 0.3084 \| 0.8804 \| 0.8784 \| 0.8913 \| 0.8804 \|
	\| 0.2272 \| 0.12 \| 1700 \| 0.2827 \| 0.8884 \| 0.8870 \| 0.8956 \| 0.8884 \|
	\| 0.2312 \| 0.13 \| 1800 \| 0.2373 \| 0.9067 \| 0.9068 \| 0.9068 \| 0.9067 \|
	\| 0.2563 \| 0.14 \| 1900 \| 0.2628 \| 0.9008 \| 0.9008 \| 0.9011 \| 0.9008 \|
	\| 0.1876 \| 0.14 \| 2000 \| 0.2744 \| 0.8852 \| 0.8840 \| 0.8906 \| 0.8852 \|
	\| 0.284 \| 0.15 \| 2100 \| 0.2751 \| 0.8928 \| 0.8914 \| 0.9002 \| 0.8928 \|
	\| 0.203 \| 0.16 \| 2200 \| 0.2406 \| 0.9031 \| 0.9034 \| 0.9054 \| 0.9031 \|
	\| 0.2278 \| 0.16 \| 2300 \| 0.2378 \| 0.9115 \| 0.9112 \| 0.9123 \| 0.9115 \|
	\| 0.2204 \| 0.17 \| 2400 \| 0.4288 \| 0.8677 \| 0.8646 \| 0.8837 \| 0.8677 \|
	\| 0.2323 \| 0.18 \| 2500 \| 0.2331 \| 0.9115 \| 0.9113 \| 0.9118 \| 0.9115 \|
	\| 0.2508 \| 0.19 \| 2600 \| 0.2932 \| 0.8956 \| 0.8955 \| 0.8955 \| 0.8956 \|
	\| 0.2838 \| 0.19 \| 2700 \| 0.2454 \| 0.9035 \| 0.9036 \| 0.9037 \| 0.9035 \|
	\| 0.221 \| 0.2 \| 2800 \| 0.3153 \| 0.8800 \| 0.8783 \| 0.8881 \| 0.8800 \|
	\| 0.2167 \| 0.21 \| 2900 \| 0.3200 \| 0.8745 \| 0.8724 \| 0.8838 \| 0.8745 \|
	\| 0.2336 \| 0.21 \| 3000 \| 0.2842 \| 0.8880 \| 0.8866 \| 0.8947 \| 0.8880 \|
	\| 0.2653 \| 0.22 \| 3100 \| 0.2353 \| 0.9059 \| 0.9059 \| 0.9059 \| 0.9059 \|
	\| 0.2953 \| 0.23 \| 3200 \| 0.2374 \| 0.9051 \| 0.9044 \| 0.9087 \| 0.9051 \|
	\| 0.174 \| 0.24 \| 3300 \| 0.2810 \| 0.8964 \| 0.8954 \| 0.9006 \| 0.8964 \|
	\| 0.2184 \| 0.24 \| 3400 \| 0.2127 \| 0.9127 \| 0.9125 \| 0.9131 \| 0.9127 \|
	\| 0.2519 \| 0.25 \| 3500 \| 0.2286 \| 0.9083 \| 0.9085 \| 0.9126 \| 0.9083 \|
	\| 0.2326 \| 0.26 \| 3600 \| 0.2904 \| 0.8948 \| 0.8944 \| 0.8956 \| 0.8948 \|
	\| 0.1862 \| 0.26 \| 3700 \| 0.2203 \| 0.9259 \| 0.9258 \| 0.9259 \| 0.9259 \|
	\| 0.2098 \| 0.27 \| 3800 \| 0.2350 \| 0.9075 \| 0.9074 \| 0.9077 \| 0.9075 \|
	\| 0.2152 \| 0.28 \| 3900 \| 0.2319 \| 0.9063 \| 0.9063 \| 0.9063 \| 0.9063 \|
	\| 0.3154 \| 0.29 \| 4000 \| 0.2184 \| 0.9071 \| 0.9070 \| 0.9072 \| 0.9071 \|
	\| 0.1679 \| 0.29 \| 4100 \| 0.4091 \| 0.8764 \| 0.8740 \| 0.8892 \| 0.8764 \|
	\| 0.1535 \| 0.3 \| 4200 \| 0.2574 \| 0.9091 \| 0.9090 \| 0.9092 \| 0.9091 \|
	\| 0.1487 \| 0.31 \| 4300 \| 0.2510 \| 0.9063 \| 0.9060 \| 0.9072 \| 0.9063 \|
	\| 0.2337 \| 0.31 \| 4400 \| 0.2163 \| 0.9131 \| 0.9128 \| 0.9138 \| 0.9131 \|
	\| 0.3144 \| 0.32 \| 4500 \| 0.2627 \| 0.9051 \| 0.9047 \| 0.9062 \| 0.9051 \|
	\| 0.2487 \| 0.33 \| 4600 \| 0.2557 \| 0.8992 \| 0.8985 \| 0.9014 \| 0.8992 \|
	\| 0.2194 \| 0.34 \| 4700 \| 0.2363 \| 0.9159 \| 0.9157 \| 0.9163 \| 0.9159 \|
	\| 0.2602 \| 0.34 \| 4800 \| 0.2374 \| 0.9051 \| 0.9053 \| 0.9058 \| 0.9051 \|
	\| 0.2353 \| 0.35 \| 4900 \| 0.2482 \| 0.9059 \| 0.9057 \| 0.9062 \| 0.9059 \|
	\| 0.2107 \| 0.36 \| 5000 \| 0.2903 \| 0.9008 \| 0.8998 \| 0.9052 \| 0.9008 \|
	\| 0.2364 \| 0.36 \| 5100 \| 0.2901 \| 0.8760 \| 0.8746 \| 0.8815 \| 0.8760 \|
	\| 0.2009 \| 0.37 \| 5200 \| 0.2491 \| 0.9091 \| 0.9086 \| 0.9116 \| 0.9091 \|
	\| 0.2469 \| 0.38 \| 5300 \| 0.3049 \| 0.8992 \| 0.8988 \| 0.9000 \| 0.8992 \|
	\| 0.162 \| 0.39 \| 5400 \| 0.2847 \| 0.9059 \| 0.9055 \| 0.9071 \| 0.9059 \|
	\| 0.24 \| 0.39 \| 5500 \| 0.2146 \| 0.9135 \| 0.9132 \| 0.9143 \| 0.9135 \|
	\| 0.2667 \| 0.4 \| 5600 \| 0.2379 \| 0.9075 \| 0.9072 \| 0.9085 \| 0.9075 \|
	\| 0.2165 \| 0.41 \| 5700 \| 0.2662 \| 0.8844 \| 0.8829 \| 0.8915 \| 0.8844 \|
	\| 0.2007 \| 0.41 \| 5800 \| 0.2539 \| 0.9047 \| 0.9039 \| 0.9087 \| 0.9047 \|
	\| 0.221 \| 0.42 \| 5900 \| 0.2272 \| 0.9047 \| 0.9046 \| 0.9047 \| 0.9047 \|
	\| 0.2028 \| 0.43 \| 6000 \| 0.3618 \| 0.8669 \| 0.8638 \| 0.8826 \| 0.8669 \|
	\| 0.3003 \| 0.44 \| 6100 \| 0.2454 \| 0.9071 \| 0.9071 \| 0.9071 \| 0.9071 \|
	\| 0.2025 \| 0.44 \| 6200 \| 0.2103 \| 0.9175 \| 0.9175 \| 0.9175 \| 0.9175 \|
	\| 0.253 \| 0.45 \| 6300 \| 0.2470 \| 0.8992 \| 0.8981 \| 0.9044 \| 0.8992 \|
	\| 0.1955 \| 0.46 \| 6400 \| 0.2887 \| 0.9000 \| 0.8992 \| 0.9031 \| 0.9000 \|
	\| 0.1621 \| 0.46 \| 6500 \| 0.2245 \| 0.9151 \| 0.9149 \| 0.9155 \| 0.9151 \|
	\| 0.2532 \| 0.47 \| 6600 \| 0.2493 \| 0.8912 \| 0.8907 \| 0.8924 \| 0.8912 \|
	\| 0.1898 \| 0.48 \| 6700 \| 0.2313 \| 0.9083 \| 0.9082 \| 0.9083 \| 0.9083 \|
	\| 0.1858 \| 0.49 \| 6800 \| 0.2514 \| 0.9031 \| 0.9026 \| 0.9049 \| 0.9031 \|
	\| 0.1977 \| 0.49 \| 6900 \| 0.2155 \| 0.9167 \| 0.9166 \| 0.9167 \| 0.9167 \|
	\| 0.2247 \| 0.5 \| 7000 \| 0.2280 \| 0.9059 \| 0.9056 \| 0.9070 \| 0.9059 \|
	\| 0.1931 \| 0.51 \| 7100 \| 0.2431 \| 0.9047 \| 0.9042 \| 0.9066 \| 0.9047 \|
	\| 0.1746 \| 0.51 \| 7200 \| 0.2400 \| 0.9155 \| 0.9152 \| 0.9164 \| 0.9155 \|
	\| 0.2579 \| 0.52 \| 7300 \| 0.2707 \| 0.9107 \| 0.9102 \| 0.9125 \| 0.9107 \|
	\| 0.2139 \| 0.53 \| 7400 \| 0.2625 \| 0.8920 \| 0.8910 \| 0.8965 \| 0.8920 \|
	\| 0.2703 \| 0.54 \| 7500 \| 0.2500 \| 0.8980 \| 0.8972 \| 0.9013 \| 0.8980 \|
	\| 0.1412 \| 0.54 \| 7600 \| 0.2210 \| 0.9159 \| 0.9158 \| 0.9160 \| 0.9159 \|
	\| 0.2382 \| 0.55 \| 7700 \| 0.2712 \| 0.9028 \| 0.9020 \| 0.9064 \| 0.9028 \|
	\| 0.2498 \| 0.56 \| 7800 \| 0.2200 \| 0.9195 \| 0.9193 \| 0.9197 \| 0.9195 \|
	\| 0.2002 \| 0.56 \| 7900 \| 0.3254 \| 0.8832 \| 0.8813 \| 0.8935 \| 0.8832 \|
	\| 0.2359 \| 0.57 \| 8000 \| 0.3023 \| 0.8928 \| 0.8918 \| 0.8973 \| 0.8928 \|
	\| 0.2193 \| 0.58 \| 8100 \| 0.2837 \| 0.8892 \| 0.8875 \| 0.8988 \| 0.8892 \|
	\| 0.2436 \| 0.59 \| 8200 \| 0.2221 \| 0.9143 \| 0.9142 \| 0.9143 \| 0.9143 \|
	\| 0.1704 \| 0.59 \| 8300 \| 0.2402 \| 0.9123 \| 0.9119 \| 0.9136 \| 0.9123 \|
	\| 0.1979 \| 0.6 \| 8400 \| 0.2722 \| 0.8912 \| 0.8896 \| 0.9003 \| 0.8912 \|
	\| 0.2476 \| 0.61 \| 8500 \| 0.2165 \| 0.9211 \| 0.9209 \| 0.9216 \| 0.9211 \|
	\| 0.1996 \| 0.61 \| 8600 \| 0.2374 \| 0.9151 \| 0.9148 \| 0.9163 \| 0.9151 \|
	\| 0.2278 \| 0.62 \| 8700 \| 0.2357 \| 0.9079 \| 0.9080 \| 0.9083 \| 0.9079 \|
	\| 0.1625 \| 0.63 \| 8800 \| 0.2205 \| 0.9231 \| 0.9228 \| 0.9237 \| 0.9231 \|
	\| 0.2197 \| 0.64 \| 8900 \| 0.3041 \| 0.9020 \| 0.9011 \| 0.9063 \| 0.9020 \|
	\| 0.1868 \| 0.64 \| 9000 \| 0.2280 \| 0.9207 \| 0.9205 \| 0.9212 \| 0.9207 \|
	\| 0.2979 \| 0.65 \| 9100 \| 0.2931 \| 0.8948 \| 0.8935 \| 0.9011 \| 0.8948 \|
	\| 0.1973 \| 0.66 \| 9200 \| 0.2322 \| 0.9163 \| 0.9159 \| 0.9181 \| 0.9163 \|


	### Framework versions

	- Transformers 4.39.3
	- Pytorch 2.1.2+cu121
	- Datasets 2.16.1
	- Tokenizers 0.15.1