ASR-test / README.md

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	---
	language:
	- vi
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	datasets:
	- ducha07/audio_HTV_thoisu
	metrics:
	- wer
	model-index:
	- name: ASR-test
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: HTV news
	type: ducha07/audio_HTV_thoisu
	metrics:
	- name: Wer
	type: wer
	value: 0.2796665364074508
	---

	<!-- 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. -->

	# ASR-test-1

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6593
	- Wer: 0.2797

	## 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: 0.001
	- train_batch_size: 32
	- eval_batch_size: 8
	- 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: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 4.8562 \| 0.92 \| 100 \| 0.8316 \| 0.4500 \|
	\| 1.0777 \| 1.83 \| 200 \| 0.6898 \| 0.3899 \|
	\| 0.98 \| 2.75 \| 300 \| 0.6811 \| 0.3740 \|
	\| 0.8967 \| 3.67 \| 400 \| 0.6332 \| 0.3565 \|
	\| 0.8965 \| 4.59 \| 500 \| 0.6038 \| 0.3517 \|
	\| 0.8396 \| 5.5 \| 600 \| 0.6040 \| 0.3479 \|
	\| 0.8137 \| 6.42 \| 700 \| 0.5929 \| 0.3408 \|
	\| 0.8304 \| 7.34 \| 800 \| 0.5911 \| 0.3513 \|
	\| 0.7894 \| 8.26 \| 900 \| 0.6078 \| 0.3357 \|
	\| 0.7412 \| 9.17 \| 1000 \| 0.6214 \| 0.3230 \|
	\| 0.7653 \| 10.09 \| 1100 \| 0.5869 \| 0.3444 \|
	\| 0.7437 \| 11.01 \| 1200 \| 0.5906 \| 0.3213 \|
	\| 0.7083 \| 11.93 \| 1300 \| 0.5952 \| 0.3139 \|
	\| 0.7168 \| 12.84 \| 1400 \| 0.5721 \| 0.3267 \|
	\| 0.7008 \| 13.76 \| 1500 \| 0.5895 \| 0.3177 \|
	\| 0.6825 \| 14.68 \| 1600 \| 0.5909 \| 0.3098 \|
	\| 0.6989 \| 15.6 \| 1700 \| 0.5979 \| 0.3673 \|
	\| 0.6717 \| 16.51 \| 1800 \| 0.5863 \| 0.3077 \|
	\| 0.6496 \| 17.43 \| 1900 \| 0.5798 \| 0.3043 \|
	\| 0.6609 \| 18.35 \| 2000 \| 0.5787 \| 0.3555 \|
	\| 0.628 \| 19.27 \| 2100 \| 0.5889 \| 0.3133 \|
	\| 0.6322 \| 20.18 \| 2200 \| 0.5913 \| 0.3077 \|
	\| 0.634 \| 21.1 \| 2300 \| 0.5769 \| 0.3193 \|
	\| 0.6172 \| 22.02 \| 2400 \| 0.5731 \| 0.3005 \|
	\| 0.6043 \| 22.94 \| 2500 \| 0.5820 \| 0.3075 \|
	\| 0.6051 \| 23.85 \| 2600 \| 0.5831 \| 0.3435 \|
	\| 0.5865 \| 24.77 \| 2700 \| 0.5790 \| 0.3029 \|
	\| 0.5806 \| 25.69 \| 2800 \| 0.5945 \| 0.3053 \|
	\| 0.5901 \| 26.61 \| 2900 \| 0.5780 \| 0.3126 \|
	\| 0.5769 \| 27.52 \| 3000 \| 0.5732 \| 0.2963 \|
	\| 0.5539 \| 28.44 \| 3100 \| 0.5837 \| 0.2950 \|
	\| 0.5799 \| 29.36 \| 3200 \| 0.5835 \| 0.3178 \|
	\| 0.5518 \| 30.28 \| 3300 \| 0.5941 \| 0.2943 \|
	\| 0.549 \| 31.19 \| 3400 \| 0.5960 \| 0.2979 \|
	\| 0.5612 \| 32.11 \| 3500 \| 0.5747 \| 0.3167 \|
	\| 0.5411 \| 33.03 \| 3600 \| 0.5855 \| 0.2978 \|
	\| 0.536 \| 33.94 \| 3700 \| 0.5720 \| 0.2944 \|
	\| 0.5329 \| 34.86 \| 3800 \| 0.5998 \| 0.3186 \|
	\| 0.5185 \| 35.78 \| 3900 \| 0.5936 \| 0.2884 \|
	\| 0.5186 \| 36.7 \| 4000 \| 0.5773 \| 0.2901 \|
	\| 0.5027 \| 37.61 \| 4100 \| 0.5969 \| 0.3264 \|
	\| 0.52 \| 38.53 \| 4200 \| 0.6184 \| 0.2939 \|
	\| 0.4992 \| 39.45 \| 4300 \| 0.5887 \| 0.2943 \|
	\| 0.5064 \| 40.37 \| 4400 \| 0.5814 \| 0.2966 \|
	\| 0.4928 \| 41.28 \| 4500 \| 0.6128 \| 0.2902 \|
	\| 0.508 \| 42.2 \| 4600 \| 0.5943 \| 0.2923 \|
	\| 0.4887 \| 43.12 \| 4700 \| 0.6100 \| 0.3039 \|
	\| 0.4872 \| 44.04 \| 4800 \| 0.6044 \| 0.2875 \|
	\| 0.4711 \| 44.95 \| 4900 \| 0.5961 \| 0.2974 \|
	\| 0.4813 \| 45.87 \| 5000 \| 0.6022 \| 0.2945 \|
	\| 0.4818 \| 46.79 \| 5100 \| 0.6199 \| 0.2898 \|
	\| 0.4492 \| 47.71 \| 5200 \| 0.6161 \| 0.2943 \|
	\| 0.4715 \| 48.62 \| 5300 \| 0.6038 \| 0.2838 \|
	\| 0.4601 \| 49.54 \| 5400 \| 0.6223 \| 0.2829 \|
	\| 0.4432 \| 50.46 \| 5500 \| 0.6058 \| 0.2965 \|
	\| 0.4419 \| 51.38 \| 5600 \| 0.6134 \| 0.2917 \|
	\| 0.4564 \| 52.29 \| 5700 \| 0.6124 \| 0.2857 \|
	\| 0.4349 \| 53.21 \| 5800 \| 0.6229 \| 0.2877 \|
	\| 0.4358 \| 54.13 \| 5900 \| 0.6095 \| 0.2898 \|
	\| 0.4432 \| 55.05 \| 6000 \| 0.6365 \| 0.2881 \|
	\| 0.4277 \| 55.96 \| 6100 \| 0.6169 \| 0.2870 \|
	\| 0.4397 \| 56.88 \| 6200 \| 0.6174 \| 0.2849 \|
	\| 0.4245 \| 57.8 \| 6300 \| 0.6340 \| 0.2858 \|
	\| 0.4203 \| 58.72 \| 6400 \| 0.6321 \| 0.2909 \|
	\| 0.4112 \| 59.63 \| 6500 \| 0.6243 \| 0.2866 \|
	\| 0.4244 \| 60.55 \| 6600 \| 0.6318 \| 0.2775 \|
	\| 0.4119 \| 61.47 \| 6700 \| 0.6215 \| 0.2798 \|
	\| 0.403 \| 62.39 \| 6800 \| 0.6213 \| 0.2829 \|
	\| 0.4158 \| 63.3 \| 6900 \| 0.6451 \| 0.2795 \|
	\| 0.3997 \| 64.22 \| 7000 \| 0.6317 \| 0.2854 \|
	\| 0.4006 \| 65.14 \| 7100 \| 0.6329 \| 0.2846 \|
	\| 0.4051 \| 66.06 \| 7200 \| 0.6318 \| 0.2834 \|
	\| 0.3953 \| 66.97 \| 7300 \| 0.6442 \| 0.2855 \|
	\| 0.4119 \| 67.89 \| 7400 \| 0.6345 \| 0.2893 \|
	\| 0.3976 \| 68.81 \| 7500 \| 0.6361 \| 0.2798 \|
	\| 0.3965 \| 69.72 \| 7600 \| 0.6355 \| 0.2853 \|
	\| 0.3957 \| 70.64 \| 7700 \| 0.6457 \| 0.2814 \|
	\| 0.3837 \| 71.56 \| 7800 \| 0.6396 \| 0.2855 \|
	\| 0.3893 \| 72.48 \| 7900 \| 0.6424 \| 0.2842 \|
	\| 0.3816 \| 73.39 \| 8000 \| 0.6496 \| 0.2778 \|
	\| 0.3855 \| 74.31 \| 8100 \| 0.6427 \| 0.2881 \|
	\| 0.3767 \| 75.23 \| 8200 \| 0.6394 \| 0.2858 \|
	\| 0.3747 \| 76.15 \| 8300 \| 0.6513 \| 0.2844 \|
	\| 0.3829 \| 77.06 \| 8400 \| 0.6602 \| 0.2775 \|
	\| 0.3721 \| 77.98 \| 8500 \| 0.6427 \| 0.2825 \|
	\| 0.3708 \| 78.9 \| 8600 \| 0.6507 \| 0.2847 \|
	\| 0.3767 \| 79.82 \| 8700 \| 0.6518 \| 0.2816 \|
	\| 0.3655 \| 80.73 \| 8800 \| 0.6597 \| 0.2802 \|
	\| 0.3614 \| 81.65 \| 8900 \| 0.6542 \| 0.2781 \|
	\| 0.3629 \| 82.57 \| 9000 \| 0.6520 \| 0.2782 \|
	\| 0.3621 \| 83.49 \| 9100 \| 0.6501 \| 0.2797 \|
	\| 0.3616 \| 84.4 \| 9200 \| 0.6528 \| 0.2777 \|
	\| 0.3519 \| 85.32 \| 9300 \| 0.6549 \| 0.2798 \|
	\| 0.3572 \| 86.24 \| 9400 \| 0.6541 \| 0.2789 \|
	\| 0.3585 \| 87.16 \| 9500 \| 0.6497 \| 0.2778 \|
	\| 0.3531 \| 88.07 \| 9600 \| 0.6523 \| 0.2781 \|
	\| 0.3586 \| 88.99 \| 9700 \| 0.6578 \| 0.2789 \|
	\| 0.3463 \| 89.91 \| 9800 \| 0.6565 \| 0.2816 \|
	\| 0.3508 \| 90.83 \| 9900 \| 0.6559 \| 0.2797 \|
	\| 0.3513 \| 91.74 \| 10000 \| 0.6611 \| 0.2794 \|
	\| 0.3425 \| 92.66 \| 10100 \| 0.6538 \| 0.2804 \|
	\| 0.3596 \| 93.58 \| 10200 \| 0.6639 \| 0.2808 \|
	\| 0.3632 \| 94.5 \| 10300 \| 0.6561 \| 0.2789 \|
	\| 0.348 \| 95.41 \| 10400 \| 0.6556 \| 0.2786 \|
	\| 0.3514 \| 96.33 \| 10500 \| 0.6575 \| 0.2791 \|
	\| 0.3499 \| 97.25 \| 10600 \| 0.6573 \| 0.2795 \|
	\| 0.3353 \| 98.17 \| 10700 \| 0.6589 \| 0.2797 \|
	\| 0.3468 \| 99.08 \| 10800 \| 0.6589 \| 0.2799 \|
	\| 0.3571 \| 100.0 \| 10900 \| 0.6593 \| 0.2797 \|


	### Framework versions

	- Transformers 4.37.0.dev0
	- Pytorch 2.1.0+cu121
	- Datasets 2.16.1
	- Tokenizers 0.15.0

	---
	language:
	- vi
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	datasets:
	- ducha07/audio_HTV_thoisu
	metrics:
	- wer
	model-index:
	- name: ASR-test
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: HTV news
	type: ducha07/audio_HTV_thoisu
	metrics:
	- name: Wer
	type: wer
	value: 0.2796665364074508
	---

	<!-- 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. -->

	# ASR-test-1

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6593
	- Wer: 0.2797

	## 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: 0.001
	- train_batch_size: 32
	- eval_batch_size: 8
	- 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: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 4.8562 \| 0.92 \| 100 \| 0.8316 \| 0.4500 \|
	\| 1.0777 \| 1.83 \| 200 \| 0.6898 \| 0.3899 \|
	\| 0.98 \| 2.75 \| 300 \| 0.6811 \| 0.3740 \|
	\| 0.8967 \| 3.67 \| 400 \| 0.6332 \| 0.3565 \|
	\| 0.8965 \| 4.59 \| 500 \| 0.6038 \| 0.3517 \|
	\| 0.8396 \| 5.5 \| 600 \| 0.6040 \| 0.3479 \|
	\| 0.8137 \| 6.42 \| 700 \| 0.5929 \| 0.3408 \|
	\| 0.8304 \| 7.34 \| 800 \| 0.5911 \| 0.3513 \|
	\| 0.7894 \| 8.26 \| 900 \| 0.6078 \| 0.3357 \|
	\| 0.7412 \| 9.17 \| 1000 \| 0.6214 \| 0.3230 \|
	\| 0.7653 \| 10.09 \| 1100 \| 0.5869 \| 0.3444 \|
	\| 0.7437 \| 11.01 \| 1200 \| 0.5906 \| 0.3213 \|
	\| 0.7083 \| 11.93 \| 1300 \| 0.5952 \| 0.3139 \|
	\| 0.7168 \| 12.84 \| 1400 \| 0.5721 \| 0.3267 \|
	\| 0.7008 \| 13.76 \| 1500 \| 0.5895 \| 0.3177 \|
	\| 0.6825 \| 14.68 \| 1600 \| 0.5909 \| 0.3098 \|
	\| 0.6989 \| 15.6 \| 1700 \| 0.5979 \| 0.3673 \|
	\| 0.6717 \| 16.51 \| 1800 \| 0.5863 \| 0.3077 \|
	\| 0.6496 \| 17.43 \| 1900 \| 0.5798 \| 0.3043 \|
	\| 0.6609 \| 18.35 \| 2000 \| 0.5787 \| 0.3555 \|
	\| 0.628 \| 19.27 \| 2100 \| 0.5889 \| 0.3133 \|
	\| 0.6322 \| 20.18 \| 2200 \| 0.5913 \| 0.3077 \|
	\| 0.634 \| 21.1 \| 2300 \| 0.5769 \| 0.3193 \|
	\| 0.6172 \| 22.02 \| 2400 \| 0.5731 \| 0.3005 \|
	\| 0.6043 \| 22.94 \| 2500 \| 0.5820 \| 0.3075 \|
	\| 0.6051 \| 23.85 \| 2600 \| 0.5831 \| 0.3435 \|
	\| 0.5865 \| 24.77 \| 2700 \| 0.5790 \| 0.3029 \|
	\| 0.5806 \| 25.69 \| 2800 \| 0.5945 \| 0.3053 \|
	\| 0.5901 \| 26.61 \| 2900 \| 0.5780 \| 0.3126 \|
	\| 0.5769 \| 27.52 \| 3000 \| 0.5732 \| 0.2963 \|
	\| 0.5539 \| 28.44 \| 3100 \| 0.5837 \| 0.2950 \|
	\| 0.5799 \| 29.36 \| 3200 \| 0.5835 \| 0.3178 \|
	\| 0.5518 \| 30.28 \| 3300 \| 0.5941 \| 0.2943 \|
	\| 0.549 \| 31.19 \| 3400 \| 0.5960 \| 0.2979 \|
	\| 0.5612 \| 32.11 \| 3500 \| 0.5747 \| 0.3167 \|
	\| 0.5411 \| 33.03 \| 3600 \| 0.5855 \| 0.2978 \|
	\| 0.536 \| 33.94 \| 3700 \| 0.5720 \| 0.2944 \|
	\| 0.5329 \| 34.86 \| 3800 \| 0.5998 \| 0.3186 \|
	\| 0.5185 \| 35.78 \| 3900 \| 0.5936 \| 0.2884 \|
	\| 0.5186 \| 36.7 \| 4000 \| 0.5773 \| 0.2901 \|
	\| 0.5027 \| 37.61 \| 4100 \| 0.5969 \| 0.3264 \|
	\| 0.52 \| 38.53 \| 4200 \| 0.6184 \| 0.2939 \|
	\| 0.4992 \| 39.45 \| 4300 \| 0.5887 \| 0.2943 \|
	\| 0.5064 \| 40.37 \| 4400 \| 0.5814 \| 0.2966 \|
	\| 0.4928 \| 41.28 \| 4500 \| 0.6128 \| 0.2902 \|
	\| 0.508 \| 42.2 \| 4600 \| 0.5943 \| 0.2923 \|
	\| 0.4887 \| 43.12 \| 4700 \| 0.6100 \| 0.3039 \|
	\| 0.4872 \| 44.04 \| 4800 \| 0.6044 \| 0.2875 \|
	\| 0.4711 \| 44.95 \| 4900 \| 0.5961 \| 0.2974 \|
	\| 0.4813 \| 45.87 \| 5000 \| 0.6022 \| 0.2945 \|
	\| 0.4818 \| 46.79 \| 5100 \| 0.6199 \| 0.2898 \|
	\| 0.4492 \| 47.71 \| 5200 \| 0.6161 \| 0.2943 \|
	\| 0.4715 \| 48.62 \| 5300 \| 0.6038 \| 0.2838 \|
	\| 0.4601 \| 49.54 \| 5400 \| 0.6223 \| 0.2829 \|
	\| 0.4432 \| 50.46 \| 5500 \| 0.6058 \| 0.2965 \|
	\| 0.4419 \| 51.38 \| 5600 \| 0.6134 \| 0.2917 \|
	\| 0.4564 \| 52.29 \| 5700 \| 0.6124 \| 0.2857 \|
	\| 0.4349 \| 53.21 \| 5800 \| 0.6229 \| 0.2877 \|
	\| 0.4358 \| 54.13 \| 5900 \| 0.6095 \| 0.2898 \|
	\| 0.4432 \| 55.05 \| 6000 \| 0.6365 \| 0.2881 \|
	\| 0.4277 \| 55.96 \| 6100 \| 0.6169 \| 0.2870 \|
	\| 0.4397 \| 56.88 \| 6200 \| 0.6174 \| 0.2849 \|
	\| 0.4245 \| 57.8 \| 6300 \| 0.6340 \| 0.2858 \|
	\| 0.4203 \| 58.72 \| 6400 \| 0.6321 \| 0.2909 \|
	\| 0.4112 \| 59.63 \| 6500 \| 0.6243 \| 0.2866 \|
	\| 0.4244 \| 60.55 \| 6600 \| 0.6318 \| 0.2775 \|
	\| 0.4119 \| 61.47 \| 6700 \| 0.6215 \| 0.2798 \|
	\| 0.403 \| 62.39 \| 6800 \| 0.6213 \| 0.2829 \|
	\| 0.4158 \| 63.3 \| 6900 \| 0.6451 \| 0.2795 \|
	\| 0.3997 \| 64.22 \| 7000 \| 0.6317 \| 0.2854 \|
	\| 0.4006 \| 65.14 \| 7100 \| 0.6329 \| 0.2846 \|
	\| 0.4051 \| 66.06 \| 7200 \| 0.6318 \| 0.2834 \|
	\| 0.3953 \| 66.97 \| 7300 \| 0.6442 \| 0.2855 \|
	\| 0.4119 \| 67.89 \| 7400 \| 0.6345 \| 0.2893 \|
	\| 0.3976 \| 68.81 \| 7500 \| 0.6361 \| 0.2798 \|
	\| 0.3965 \| 69.72 \| 7600 \| 0.6355 \| 0.2853 \|
	\| 0.3957 \| 70.64 \| 7700 \| 0.6457 \| 0.2814 \|
	\| 0.3837 \| 71.56 \| 7800 \| 0.6396 \| 0.2855 \|
	\| 0.3893 \| 72.48 \| 7900 \| 0.6424 \| 0.2842 \|
	\| 0.3816 \| 73.39 \| 8000 \| 0.6496 \| 0.2778 \|
	\| 0.3855 \| 74.31 \| 8100 \| 0.6427 \| 0.2881 \|
	\| 0.3767 \| 75.23 \| 8200 \| 0.6394 \| 0.2858 \|
	\| 0.3747 \| 76.15 \| 8300 \| 0.6513 \| 0.2844 \|
	\| 0.3829 \| 77.06 \| 8400 \| 0.6602 \| 0.2775 \|
	\| 0.3721 \| 77.98 \| 8500 \| 0.6427 \| 0.2825 \|
	\| 0.3708 \| 78.9 \| 8600 \| 0.6507 \| 0.2847 \|
	\| 0.3767 \| 79.82 \| 8700 \| 0.6518 \| 0.2816 \|
	\| 0.3655 \| 80.73 \| 8800 \| 0.6597 \| 0.2802 \|
	\| 0.3614 \| 81.65 \| 8900 \| 0.6542 \| 0.2781 \|
	\| 0.3629 \| 82.57 \| 9000 \| 0.6520 \| 0.2782 \|
	\| 0.3621 \| 83.49 \| 9100 \| 0.6501 \| 0.2797 \|
	\| 0.3616 \| 84.4 \| 9200 \| 0.6528 \| 0.2777 \|
	\| 0.3519 \| 85.32 \| 9300 \| 0.6549 \| 0.2798 \|
	\| 0.3572 \| 86.24 \| 9400 \| 0.6541 \| 0.2789 \|
	\| 0.3585 \| 87.16 \| 9500 \| 0.6497 \| 0.2778 \|
	\| 0.3531 \| 88.07 \| 9600 \| 0.6523 \| 0.2781 \|
	\| 0.3586 \| 88.99 \| 9700 \| 0.6578 \| 0.2789 \|
	\| 0.3463 \| 89.91 \| 9800 \| 0.6565 \| 0.2816 \|
	\| 0.3508 \| 90.83 \| 9900 \| 0.6559 \| 0.2797 \|
	\| 0.3513 \| 91.74 \| 10000 \| 0.6611 \| 0.2794 \|
	\| 0.3425 \| 92.66 \| 10100 \| 0.6538 \| 0.2804 \|
	\| 0.3596 \| 93.58 \| 10200 \| 0.6639 \| 0.2808 \|
	\| 0.3632 \| 94.5 \| 10300 \| 0.6561 \| 0.2789 \|
	\| 0.348 \| 95.41 \| 10400 \| 0.6556 \| 0.2786 \|
	\| 0.3514 \| 96.33 \| 10500 \| 0.6575 \| 0.2791 \|
	\| 0.3499 \| 97.25 \| 10600 \| 0.6573 \| 0.2795 \|
	\| 0.3353 \| 98.17 \| 10700 \| 0.6589 \| 0.2797 \|
	\| 0.3468 \| 99.08 \| 10800 \| 0.6589 \| 0.2799 \|
	\| 0.3571 \| 100.0 \| 10900 \| 0.6593 \| 0.2797 \|


	### Framework versions

	- Transformers 4.37.0.dev0
	- Pytorch 2.1.0+cu121
	- Datasets 2.16.1
	- Tokenizers 0.15.0