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	---
	license: mit
	base_model: roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	model-index:
	- name: roberta-base-emotion-prediction-phr
	results: []
	datasets:
	- vibhorag101/sem_eval_2018_task_1_english_cleaned_labels
	- sem_eval_2018_task_1
	language:
	- en
	pipeline_tag: text-classification
	---

	<!-- 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. -->

	# roberta-base-emotion-prediction-phr

	This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3301
	- Accuracy: 0.2814
	- Micro Precision: 0.7422
	- Micro Recall: 0.6510
	- Micro F1: 0.6945
	- Micro Roc Auc: 0.7940

	## 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: 2e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Micro Precision \| Micro Recall \| Micro F1 \| Micro Roc Auc \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:-------------:\|
	\| 0.4952 \| 0.12 \| 100 \| 0.4515 \| 0.1574 \| 0.5861 \| 0.3505 \| 0.4386 \| 0.6404 \|
	\| 0.4152 \| 0.23 \| 200 \| 0.3839 \| 0.2041 \| 0.7102 \| 0.4593 \| 0.5578 \| 0.7033 \|
	\| 0.3878 \| 0.35 \| 300 \| 0.3625 \| 0.2341 \| 0.7384 \| 0.5198 \| 0.6101 \| 0.7340 \|
	\| 0.3764 \| 0.47 \| 400 \| 0.3506 \| 0.2412 \| 0.7666 \| 0.5092 \| 0.6119 \| 0.7328 \|
	\| 0.372 \| 0.58 \| 500 \| 0.3450 \| 0.2375 \| 0.7686 \| 0.5251 \| 0.6239 \| 0.7403 \|
	\| 0.3588 \| 0.7 \| 600 \| 0.3464 \| 0.2249 \| 0.7804 \| 0.4964 \| 0.6068 \| 0.7286 \|
	\| 0.3383 \| 0.82 \| 700 \| 0.3471 \| 0.2470 \| 0.7503 \| 0.5578 \| 0.6398 \| 0.7528 \|
	\| 0.3489 \| 0.94 \| 800 \| 0.3284 \| 0.2620 \| 0.7702 \| 0.5682 \| 0.6539 \| 0.7603 \|
	\| 0.3287 \| 1.05 \| 900 \| 0.3214 \| 0.2820 \| 0.7707 \| 0.5936 \| 0.6706 \| 0.7720 \|
	\| 0.3158 \| 1.17 \| 1000 \| 0.3352 \| 0.2657 \| 0.7580 \| 0.5814 \| 0.6580 \| 0.7646 \|
	\| 0.3247 \| 1.29 \| 1100 \| 0.3219 \| 0.2811 \| 0.7696 \| 0.6031 \| 0.6763 \| 0.7762 \|
	\| 0.3159 \| 1.4 \| 1200 \| 0.3237 \| 0.2688 \| 0.7479 \| 0.6138 \| 0.6743 \| 0.7778 \|
	\| 0.3207 \| 1.52 \| 1300 \| 0.3217 \| 0.2461 \| 0.7676 \| 0.5767 \| 0.6586 \| 0.7638 \|
	\| 0.3087 \| 1.64 \| 1400 \| 0.3253 \| 0.2424 \| 0.7484 \| 0.5883 \| 0.6587 \| 0.7663 \|
	\| 0.3057 \| 1.75 \| 1500 \| 0.3174 \| 0.2728 \| 0.7587 \| 0.6116 \| 0.6773 \| 0.7785 \|
	\| 0.3099 \| 1.87 \| 1600 \| 0.3150 \| 0.2774 \| 0.7683 \| 0.6001 \| 0.6738 \| 0.7746 \|
	\| 0.3006 \| 1.99 \| 1700 \| 0.3176 \| 0.2633 \| 0.7636 \| 0.5881 \| 0.6645 \| 0.7685 \|
	\| 0.285 \| 2.11 \| 1800 \| 0.3177 \| 0.2722 \| 0.7363 \| 0.6484 \| 0.6896 \| 0.7915 \|
	\| 0.2886 \| 2.22 \| 1900 \| 0.3156 \| 0.2768 \| 0.7734 \| 0.5935 \| 0.6716 \| 0.7723 \|
	\| 0.2785 \| 2.34 \| 2000 \| 0.3101 \| 0.2808 \| 0.7692 \| 0.6151 \| 0.6836 \| 0.7816 \|
	\| 0.2801 \| 2.46 \| 2100 \| 0.3121 \| 0.2728 \| 0.7739 \| 0.5956 \| 0.6732 \| 0.7734 \|
	\| 0.2876 \| 2.57 \| 2200 \| 0.3166 \| 0.2777 \| 0.7577 \| 0.6157 \| 0.6794 \| 0.7802 \|
	\| 0.2769 \| 2.69 \| 2300 \| 0.3143 \| 0.2881 \| 0.7691 \| 0.6124 \| 0.6819 \| 0.7803 \|
	\| 0.2755 \| 2.81 \| 2400 \| 0.3133 \| 0.2792 \| 0.7577 \| 0.6263 \| 0.6857 \| 0.7850 \|
	\| 0.2815 \| 2.92 \| 2500 \| 0.3197 \| 0.2716 \| 0.7406 \| 0.6466 \| 0.6904 \| 0.7914 \|
	\| 0.2671 \| 3.04 \| 2600 \| 0.3133 \| 0.2857 \| 0.7549 \| 0.6438 \| 0.6949 \| 0.7925 \|
	\| 0.2431 \| 3.16 \| 2700 \| 0.3225 \| 0.2722 \| 0.7515 \| 0.6320 \| 0.6866 \| 0.7866 \|
	\| 0.2512 \| 3.27 \| 2800 \| 0.3221 \| 0.2743 \| 0.7616 \| 0.6106 \| 0.6778 \| 0.7784 \|
	\| 0.2574 \| 3.39 \| 2900 \| 0.3191 \| 0.2737 \| 0.7561 \| 0.6214 \| 0.6822 \| 0.7825 \|
	\| 0.2527 \| 3.51 \| 3000 \| 0.3207 \| 0.2666 \| 0.7443 \| 0.6315 \| 0.6833 \| 0.7852 \|
	\| 0.2615 \| 3.63 \| 3100 \| 0.3170 \| 0.2670 \| 0.7443 \| 0.6471 \| 0.6923 \| 0.7923 \|
	\| 0.2583 \| 3.74 \| 3200 \| 0.3122 \| 0.2685 \| 0.7729 \| 0.6068 \| 0.6799 \| 0.7783 \|
	\| 0.2543 \| 3.86 \| 3300 \| 0.3175 \| 0.2709 \| 0.7492 \| 0.6432 \| 0.6921 \| 0.7913 \|
	\| 0.2546 \| 3.98 \| 3400 \| 0.3164 \| 0.2752 \| 0.7661 \| 0.6186 \| 0.6845 \| 0.7828 \|
	\| 0.2274 \| 4.09 \| 3500 \| 0.3172 \| 0.2759 \| 0.7437 \| 0.6426 \| 0.6895 \| 0.7902 \|
	\| 0.2328 \| 4.21 \| 3600 \| 0.3214 \| 0.2737 \| 0.7548 \| 0.6297 \| 0.6866 \| 0.7861 \|
	\| 0.2354 \| 4.33 \| 3700 \| 0.3192 \| 0.2792 \| 0.7546 \| 0.6310 \| 0.6872 \| 0.7866 \|
	\| 0.2238 \| 4.44 \| 3800 \| 0.3199 \| 0.2709 \| 0.7453 \| 0.6444 \| 0.6912 \| 0.7912 \|
	\| 0.2376 \| 4.56 \| 3900 \| 0.3176 \| 0.2734 \| 0.7599 \| 0.6247 \| 0.6857 \| 0.7846 \|
	\| 0.2344 \| 4.68 \| 4000 \| 0.3189 \| 0.2639 \| 0.7437 \| 0.6390 \| 0.6874 \| 0.7885 \|
	\| 0.2222 \| 4.8 \| 4100 \| 0.3222 \| 0.2636 \| 0.7436 \| 0.6409 \| 0.6884 \| 0.7894 \|
	\| 0.232 \| 4.91 \| 4200 \| 0.3227 \| 0.2725 \| 0.7472 \| 0.6426 \| 0.6910 \| 0.7907 \|
	\| 0.2367 \| 5.03 \| 4300 \| 0.3243 \| 0.2670 \| 0.7463 \| 0.6339 \| 0.6855 \| 0.7866 \|
	\| 0.2154 \| 5.15 \| 4400 \| 0.3257 \| 0.2593 \| 0.7366 \| 0.6513 \| 0.6913 \| 0.7929 \|
	\| 0.2089 \| 5.26 \| 4500 \| 0.3261 \| 0.2700 \| 0.7416 \| 0.6453 \| 0.6901 \| 0.7910 \|
	\| 0.2081 \| 5.38 \| 4600 \| 0.3269 \| 0.2731 \| 0.7602 \| 0.6133 \| 0.6789 \| 0.7794 \|
	\| 0.2116 \| 5.5 \| 4700 \| 0.3308 \| 0.2593 \| 0.7229 \| 0.6687 \| 0.6947 \| 0.7983 \|
	\| 0.2128 \| 5.61 \| 4800 \| 0.3263 \| 0.2660 \| 0.7422 \| 0.6432 \| 0.6891 \| 0.7902 \|
	\| 0.2059 \| 5.73 \| 4900 \| 0.3295 \| 0.2728 \| 0.7356 \| 0.6550 \| 0.6929 \| 0.7944 \|
	\| 0.2103 \| 5.85 \| 5000 \| 0.3301 \| 0.2814 \| 0.7442 \| 0.6510 \| 0.6945 \| 0.7940 \|
	\| 0.2151 \| 5.96 \| 5100 \| 0.3300 \| 0.2541 \| 0.7221 \| 0.6598 \| 0.6896 \| 0.7942 \|
	\| 0.1954 \| 6.08 \| 5200 \| 0.3325 \| 0.2765 \| 0.7476 \| 0.6381 \| 0.6885 \| 0.7887 \|
	\| 0.2028 \| 6.2 \| 5300 \| 0.3316 \| 0.2559 \| 0.7364 \| 0.6400 \| 0.6848 \| 0.7878 \|
	\| 0.1911 \| 6.32 \| 5400 \| 0.3332 \| 0.2553 \| 0.7370 \| 0.6386 \| 0.6843 \| 0.7873 \|
	\| 0.2015 \| 6.43 \| 5500 \| 0.3349 \| 0.2645 \| 0.7308 \| 0.6538 \| 0.6902 \| 0.7931 \|
	\| 0.1901 \| 6.55 \| 5600 \| 0.3389 \| 0.2587 \| 0.7197 \| 0.6682 \| 0.6930 \| 0.7975 \|
	\| 0.197 \| 6.67 \| 5700 \| 0.3349 \| 0.2728 \| 0.7400 \| 0.6424 \| 0.6878 \| 0.7895 \|
	\| 0.1907 \| 6.78 \| 5800 \| 0.3354 \| 0.2627 \| 0.7454 \| 0.6349 \| 0.6857 \| 0.7870 \|
	\| 0.1853 \| 6.9 \| 5900 \| 0.3420 \| 0.2657 \| 0.7356 \| 0.6513 \| 0.6909 \| 0.7927 \|
	\| 0.1841 \| 7.02 \| 6000 \| 0.3399 \| 0.2584 \| 0.7308 \| 0.6554 \| 0.6910 \| 0.7937 \|
	\| 0.1739 \| 7.13 \| 6100 \| 0.3409 \| 0.2620 \| 0.7364 \| 0.6446 \| 0.6874 \| 0.7898 \|
	\| 0.1768 \| 7.25 \| 6200 \| 0.3417 \| 0.2593 \| 0.7314 \| 0.6474 \| 0.6868 \| 0.7902 \|
	\| 0.1762 \| 7.37 \| 6300 \| 0.3384 \| 0.2654 \| 0.7398 \| 0.6373 \| 0.6847 \| 0.7871 \|
	\| 0.177 \| 7.49 \| 6400 \| 0.3448 \| 0.2541 \| 0.7237 \| 0.6547 \| 0.6875 \| 0.7922 \|
	\| 0.1787 \| 7.6 \| 6500 \| 0.3422 \| 0.2513 \| 0.7317 \| 0.6425 \| 0.6842 \| 0.7881 \|
	\| 0.1793 \| 7.72 \| 6600 \| 0.3452 \| 0.2611 \| 0.7231 \| 0.6582 \| 0.6891 \| 0.7936 \|
	\| 0.1772 \| 7.84 \| 6700 \| 0.3470 \| 0.2587 \| 0.7193 \| 0.6618 \| 0.6894 \| 0.7946 \|
	\| 0.1799 \| 7.95 \| 6800 \| 0.3459 \| 0.2547 \| 0.7238 \| 0.6494 \| 0.6846 \| 0.7898 \|
	\| 0.1726 \| 8.07 \| 6900 \| 0.3477 \| 0.2507 \| 0.7259 \| 0.6419 \| 0.6813 \| 0.7869 \|
	\| 0.1672 \| 8.19 \| 7000 \| 0.3489 \| 0.2492 \| 0.7215 \| 0.6499 \| 0.6838 \| 0.7897 \|
	\| 0.1664 \| 8.3 \| 7100 \| 0.3474 \| 0.2498 \| 0.7197 \| 0.6491 \| 0.6826 \| 0.7890 \|
	\| 0.1712 \| 8.42 \| 7200 \| 0.3477 \| 0.2516 \| 0.7309 \| 0.6404 \| 0.6827 \| 0.7870 \|
	\| 0.166 \| 8.54 \| 7300 \| 0.3487 \| 0.2553 \| 0.7209 \| 0.6547 \| 0.6862 \| 0.7917 \|
	\| 0.1706 \| 8.65 \| 7400 \| 0.3487 \| 0.2538 \| 0.7239 \| 0.6518 \| 0.6860 \| 0.7909 \|
	\| 0.1674 \| 8.77 \| 7500 \| 0.3506 \| 0.2538 \| 0.7216 \| 0.6541 \| 0.6862 \| 0.7916 \|
	\| 0.1655 \| 8.89 \| 7600 \| 0.3476 \| 0.2553 \| 0.7283 \| 0.6465 \| 0.6849 \| 0.7893 \|
	\| 0.1609 \| 9.01 \| 7700 \| 0.3498 \| 0.2495 \| 0.7273 \| 0.6443 \| 0.6833 \| 0.7882 \|
	\| 0.1647 \| 9.12 \| 7800 \| 0.3507 \| 0.2522 \| 0.7255 \| 0.6423 \| 0.6814 \| 0.7870 \|
	\| 0.1531 \| 9.24 \| 7900 \| 0.3503 \| 0.2522 \| 0.7292 \| 0.6426 \| 0.6832 \| 0.7878 \|
	\| 0.1577 \| 9.36 \| 8000 \| 0.3524 \| 0.2528 \| 0.7212 \| 0.6569 \| 0.6875 \| 0.7927 \|
	\| 0.1592 \| 9.47 \| 8100 \| 0.3517 \| 0.2519 \| 0.7186 \| 0.6536 \| 0.6845 \| 0.7908 \|
	\| 0.1615 \| 9.59 \| 8200 \| 0.3514 \| 0.2510 \| 0.7183 \| 0.6529 \| 0.6841 \| 0.7905 \|
	\| 0.1529 \| 9.71 \| 8300 \| 0.3515 \| 0.2516 \| 0.7221 \| 0.6489 \| 0.6835 \| 0.7893 \|
	\| 0.1607 \| 9.82 \| 8400 \| 0.3520 \| 0.2528 \| 0.7212 \| 0.6499 \| 0.6837 \| 0.7896 \|
	\| 0.1506 \| 9.94 \| 8500 \| 0.3524 \| 0.2522 \| 0.7220 \| 0.6522 \| 0.6853 \| 0.7908 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.1.0+cu121
	- Datasets 2.14.5
	- Tokenizers 0.13.3

	---
	license: mit
	base_model: roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	model-index:
	- name: roberta-base-emotion-prediction-phr
	results: []
	datasets:
	- vibhorag101/sem_eval_2018_task_1_english_cleaned_labels
	- sem_eval_2018_task_1
	language:
	- en
	pipeline_tag: text-classification
	---

	<!-- 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. -->

	# roberta-base-emotion-prediction-phr

	This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3301
	- Accuracy: 0.2814
	- Micro Precision: 0.7422
	- Micro Recall: 0.6510
	- Micro F1: 0.6945
	- Micro Roc Auc: 0.7940

	## 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: 2e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Micro Precision \| Micro Recall \| Micro F1 \| Micro Roc Auc \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:-------------:\|
	\| 0.4952 \| 0.12 \| 100 \| 0.4515 \| 0.1574 \| 0.5861 \| 0.3505 \| 0.4386 \| 0.6404 \|
	\| 0.4152 \| 0.23 \| 200 \| 0.3839 \| 0.2041 \| 0.7102 \| 0.4593 \| 0.5578 \| 0.7033 \|
	\| 0.3878 \| 0.35 \| 300 \| 0.3625 \| 0.2341 \| 0.7384 \| 0.5198 \| 0.6101 \| 0.7340 \|
	\| 0.3764 \| 0.47 \| 400 \| 0.3506 \| 0.2412 \| 0.7666 \| 0.5092 \| 0.6119 \| 0.7328 \|
	\| 0.372 \| 0.58 \| 500 \| 0.3450 \| 0.2375 \| 0.7686 \| 0.5251 \| 0.6239 \| 0.7403 \|
	\| 0.3588 \| 0.7 \| 600 \| 0.3464 \| 0.2249 \| 0.7804 \| 0.4964 \| 0.6068 \| 0.7286 \|
	\| 0.3383 \| 0.82 \| 700 \| 0.3471 \| 0.2470 \| 0.7503 \| 0.5578 \| 0.6398 \| 0.7528 \|
	\| 0.3489 \| 0.94 \| 800 \| 0.3284 \| 0.2620 \| 0.7702 \| 0.5682 \| 0.6539 \| 0.7603 \|
	\| 0.3287 \| 1.05 \| 900 \| 0.3214 \| 0.2820 \| 0.7707 \| 0.5936 \| 0.6706 \| 0.7720 \|
	\| 0.3158 \| 1.17 \| 1000 \| 0.3352 \| 0.2657 \| 0.7580 \| 0.5814 \| 0.6580 \| 0.7646 \|
	\| 0.3247 \| 1.29 \| 1100 \| 0.3219 \| 0.2811 \| 0.7696 \| 0.6031 \| 0.6763 \| 0.7762 \|
	\| 0.3159 \| 1.4 \| 1200 \| 0.3237 \| 0.2688 \| 0.7479 \| 0.6138 \| 0.6743 \| 0.7778 \|
	\| 0.3207 \| 1.52 \| 1300 \| 0.3217 \| 0.2461 \| 0.7676 \| 0.5767 \| 0.6586 \| 0.7638 \|
	\| 0.3087 \| 1.64 \| 1400 \| 0.3253 \| 0.2424 \| 0.7484 \| 0.5883 \| 0.6587 \| 0.7663 \|
	\| 0.3057 \| 1.75 \| 1500 \| 0.3174 \| 0.2728 \| 0.7587 \| 0.6116 \| 0.6773 \| 0.7785 \|
	\| 0.3099 \| 1.87 \| 1600 \| 0.3150 \| 0.2774 \| 0.7683 \| 0.6001 \| 0.6738 \| 0.7746 \|
	\| 0.3006 \| 1.99 \| 1700 \| 0.3176 \| 0.2633 \| 0.7636 \| 0.5881 \| 0.6645 \| 0.7685 \|
	\| 0.285 \| 2.11 \| 1800 \| 0.3177 \| 0.2722 \| 0.7363 \| 0.6484 \| 0.6896 \| 0.7915 \|
	\| 0.2886 \| 2.22 \| 1900 \| 0.3156 \| 0.2768 \| 0.7734 \| 0.5935 \| 0.6716 \| 0.7723 \|
	\| 0.2785 \| 2.34 \| 2000 \| 0.3101 \| 0.2808 \| 0.7692 \| 0.6151 \| 0.6836 \| 0.7816 \|
	\| 0.2801 \| 2.46 \| 2100 \| 0.3121 \| 0.2728 \| 0.7739 \| 0.5956 \| 0.6732 \| 0.7734 \|
	\| 0.2876 \| 2.57 \| 2200 \| 0.3166 \| 0.2777 \| 0.7577 \| 0.6157 \| 0.6794 \| 0.7802 \|
	\| 0.2769 \| 2.69 \| 2300 \| 0.3143 \| 0.2881 \| 0.7691 \| 0.6124 \| 0.6819 \| 0.7803 \|
	\| 0.2755 \| 2.81 \| 2400 \| 0.3133 \| 0.2792 \| 0.7577 \| 0.6263 \| 0.6857 \| 0.7850 \|
	\| 0.2815 \| 2.92 \| 2500 \| 0.3197 \| 0.2716 \| 0.7406 \| 0.6466 \| 0.6904 \| 0.7914 \|
	\| 0.2671 \| 3.04 \| 2600 \| 0.3133 \| 0.2857 \| 0.7549 \| 0.6438 \| 0.6949 \| 0.7925 \|
	\| 0.2431 \| 3.16 \| 2700 \| 0.3225 \| 0.2722 \| 0.7515 \| 0.6320 \| 0.6866 \| 0.7866 \|
	\| 0.2512 \| 3.27 \| 2800 \| 0.3221 \| 0.2743 \| 0.7616 \| 0.6106 \| 0.6778 \| 0.7784 \|
	\| 0.2574 \| 3.39 \| 2900 \| 0.3191 \| 0.2737 \| 0.7561 \| 0.6214 \| 0.6822 \| 0.7825 \|
	\| 0.2527 \| 3.51 \| 3000 \| 0.3207 \| 0.2666 \| 0.7443 \| 0.6315 \| 0.6833 \| 0.7852 \|
	\| 0.2615 \| 3.63 \| 3100 \| 0.3170 \| 0.2670 \| 0.7443 \| 0.6471 \| 0.6923 \| 0.7923 \|
	\| 0.2583 \| 3.74 \| 3200 \| 0.3122 \| 0.2685 \| 0.7729 \| 0.6068 \| 0.6799 \| 0.7783 \|
	\| 0.2543 \| 3.86 \| 3300 \| 0.3175 \| 0.2709 \| 0.7492 \| 0.6432 \| 0.6921 \| 0.7913 \|
	\| 0.2546 \| 3.98 \| 3400 \| 0.3164 \| 0.2752 \| 0.7661 \| 0.6186 \| 0.6845 \| 0.7828 \|
	\| 0.2274 \| 4.09 \| 3500 \| 0.3172 \| 0.2759 \| 0.7437 \| 0.6426 \| 0.6895 \| 0.7902 \|
	\| 0.2328 \| 4.21 \| 3600 \| 0.3214 \| 0.2737 \| 0.7548 \| 0.6297 \| 0.6866 \| 0.7861 \|
	\| 0.2354 \| 4.33 \| 3700 \| 0.3192 \| 0.2792 \| 0.7546 \| 0.6310 \| 0.6872 \| 0.7866 \|
	\| 0.2238 \| 4.44 \| 3800 \| 0.3199 \| 0.2709 \| 0.7453 \| 0.6444 \| 0.6912 \| 0.7912 \|
	\| 0.2376 \| 4.56 \| 3900 \| 0.3176 \| 0.2734 \| 0.7599 \| 0.6247 \| 0.6857 \| 0.7846 \|
	\| 0.2344 \| 4.68 \| 4000 \| 0.3189 \| 0.2639 \| 0.7437 \| 0.6390 \| 0.6874 \| 0.7885 \|
	\| 0.2222 \| 4.8 \| 4100 \| 0.3222 \| 0.2636 \| 0.7436 \| 0.6409 \| 0.6884 \| 0.7894 \|
	\| 0.232 \| 4.91 \| 4200 \| 0.3227 \| 0.2725 \| 0.7472 \| 0.6426 \| 0.6910 \| 0.7907 \|
	\| 0.2367 \| 5.03 \| 4300 \| 0.3243 \| 0.2670 \| 0.7463 \| 0.6339 \| 0.6855 \| 0.7866 \|
	\| 0.2154 \| 5.15 \| 4400 \| 0.3257 \| 0.2593 \| 0.7366 \| 0.6513 \| 0.6913 \| 0.7929 \|
	\| 0.2089 \| 5.26 \| 4500 \| 0.3261 \| 0.2700 \| 0.7416 \| 0.6453 \| 0.6901 \| 0.7910 \|
	\| 0.2081 \| 5.38 \| 4600 \| 0.3269 \| 0.2731 \| 0.7602 \| 0.6133 \| 0.6789 \| 0.7794 \|
	\| 0.2116 \| 5.5 \| 4700 \| 0.3308 \| 0.2593 \| 0.7229 \| 0.6687 \| 0.6947 \| 0.7983 \|
	\| 0.2128 \| 5.61 \| 4800 \| 0.3263 \| 0.2660 \| 0.7422 \| 0.6432 \| 0.6891 \| 0.7902 \|
	\| 0.2059 \| 5.73 \| 4900 \| 0.3295 \| 0.2728 \| 0.7356 \| 0.6550 \| 0.6929 \| 0.7944 \|
	\| 0.2103 \| 5.85 \| 5000 \| 0.3301 \| 0.2814 \| 0.7442 \| 0.6510 \| 0.6945 \| 0.7940 \|
	\| 0.2151 \| 5.96 \| 5100 \| 0.3300 \| 0.2541 \| 0.7221 \| 0.6598 \| 0.6896 \| 0.7942 \|
	\| 0.1954 \| 6.08 \| 5200 \| 0.3325 \| 0.2765 \| 0.7476 \| 0.6381 \| 0.6885 \| 0.7887 \|
	\| 0.2028 \| 6.2 \| 5300 \| 0.3316 \| 0.2559 \| 0.7364 \| 0.6400 \| 0.6848 \| 0.7878 \|
	\| 0.1911 \| 6.32 \| 5400 \| 0.3332 \| 0.2553 \| 0.7370 \| 0.6386 \| 0.6843 \| 0.7873 \|
	\| 0.2015 \| 6.43 \| 5500 \| 0.3349 \| 0.2645 \| 0.7308 \| 0.6538 \| 0.6902 \| 0.7931 \|
	\| 0.1901 \| 6.55 \| 5600 \| 0.3389 \| 0.2587 \| 0.7197 \| 0.6682 \| 0.6930 \| 0.7975 \|
	\| 0.197 \| 6.67 \| 5700 \| 0.3349 \| 0.2728 \| 0.7400 \| 0.6424 \| 0.6878 \| 0.7895 \|
	\| 0.1907 \| 6.78 \| 5800 \| 0.3354 \| 0.2627 \| 0.7454 \| 0.6349 \| 0.6857 \| 0.7870 \|
	\| 0.1853 \| 6.9 \| 5900 \| 0.3420 \| 0.2657 \| 0.7356 \| 0.6513 \| 0.6909 \| 0.7927 \|
	\| 0.1841 \| 7.02 \| 6000 \| 0.3399 \| 0.2584 \| 0.7308 \| 0.6554 \| 0.6910 \| 0.7937 \|
	\| 0.1739 \| 7.13 \| 6100 \| 0.3409 \| 0.2620 \| 0.7364 \| 0.6446 \| 0.6874 \| 0.7898 \|
	\| 0.1768 \| 7.25 \| 6200 \| 0.3417 \| 0.2593 \| 0.7314 \| 0.6474 \| 0.6868 \| 0.7902 \|
	\| 0.1762 \| 7.37 \| 6300 \| 0.3384 \| 0.2654 \| 0.7398 \| 0.6373 \| 0.6847 \| 0.7871 \|
	\| 0.177 \| 7.49 \| 6400 \| 0.3448 \| 0.2541 \| 0.7237 \| 0.6547 \| 0.6875 \| 0.7922 \|
	\| 0.1787 \| 7.6 \| 6500 \| 0.3422 \| 0.2513 \| 0.7317 \| 0.6425 \| 0.6842 \| 0.7881 \|
	\| 0.1793 \| 7.72 \| 6600 \| 0.3452 \| 0.2611 \| 0.7231 \| 0.6582 \| 0.6891 \| 0.7936 \|
	\| 0.1772 \| 7.84 \| 6700 \| 0.3470 \| 0.2587 \| 0.7193 \| 0.6618 \| 0.6894 \| 0.7946 \|
	\| 0.1799 \| 7.95 \| 6800 \| 0.3459 \| 0.2547 \| 0.7238 \| 0.6494 \| 0.6846 \| 0.7898 \|
	\| 0.1726 \| 8.07 \| 6900 \| 0.3477 \| 0.2507 \| 0.7259 \| 0.6419 \| 0.6813 \| 0.7869 \|
	\| 0.1672 \| 8.19 \| 7000 \| 0.3489 \| 0.2492 \| 0.7215 \| 0.6499 \| 0.6838 \| 0.7897 \|
	\| 0.1664 \| 8.3 \| 7100 \| 0.3474 \| 0.2498 \| 0.7197 \| 0.6491 \| 0.6826 \| 0.7890 \|
	\| 0.1712 \| 8.42 \| 7200 \| 0.3477 \| 0.2516 \| 0.7309 \| 0.6404 \| 0.6827 \| 0.7870 \|
	\| 0.166 \| 8.54 \| 7300 \| 0.3487 \| 0.2553 \| 0.7209 \| 0.6547 \| 0.6862 \| 0.7917 \|
	\| 0.1706 \| 8.65 \| 7400 \| 0.3487 \| 0.2538 \| 0.7239 \| 0.6518 \| 0.6860 \| 0.7909 \|
	\| 0.1674 \| 8.77 \| 7500 \| 0.3506 \| 0.2538 \| 0.7216 \| 0.6541 \| 0.6862 \| 0.7916 \|
	\| 0.1655 \| 8.89 \| 7600 \| 0.3476 \| 0.2553 \| 0.7283 \| 0.6465 \| 0.6849 \| 0.7893 \|
	\| 0.1609 \| 9.01 \| 7700 \| 0.3498 \| 0.2495 \| 0.7273 \| 0.6443 \| 0.6833 \| 0.7882 \|
	\| 0.1647 \| 9.12 \| 7800 \| 0.3507 \| 0.2522 \| 0.7255 \| 0.6423 \| 0.6814 \| 0.7870 \|
	\| 0.1531 \| 9.24 \| 7900 \| 0.3503 \| 0.2522 \| 0.7292 \| 0.6426 \| 0.6832 \| 0.7878 \|
	\| 0.1577 \| 9.36 \| 8000 \| 0.3524 \| 0.2528 \| 0.7212 \| 0.6569 \| 0.6875 \| 0.7927 \|
	\| 0.1592 \| 9.47 \| 8100 \| 0.3517 \| 0.2519 \| 0.7186 \| 0.6536 \| 0.6845 \| 0.7908 \|
	\| 0.1615 \| 9.59 \| 8200 \| 0.3514 \| 0.2510 \| 0.7183 \| 0.6529 \| 0.6841 \| 0.7905 \|
	\| 0.1529 \| 9.71 \| 8300 \| 0.3515 \| 0.2516 \| 0.7221 \| 0.6489 \| 0.6835 \| 0.7893 \|
	\| 0.1607 \| 9.82 \| 8400 \| 0.3520 \| 0.2528 \| 0.7212 \| 0.6499 \| 0.6837 \| 0.7896 \|
	\| 0.1506 \| 9.94 \| 8500 \| 0.3524 \| 0.2522 \| 0.7220 \| 0.6522 \| 0.6853 \| 0.7908 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.1.0+cu121
	- Datasets 2.14.5
	- Tokenizers 0.13.3