End of training

c6f19e3 verified 7 months ago

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	---
	language:
	- ga
	- en
	license: apache-2.0
	base_model: openai/whisper-medium
	tags:
	- generated_from_trainer
	datasets:
	- ymoslem/IWSLT2023-GA-EN
	- ymoslem/FLEURS-GA-EN
	- ymoslem/BitesizeIrish-GA-EN
	- ymoslem/SpokenWords-GA-EN-MTed
	- ymoslem/Tatoeba-Speech-Irish
	- ymoslem/Wikimedia-Speech-Irish
	- ymoslem/Tatoeba-Speech-Irish-Noise-002
	- ymoslem/Wikimedia-Speech-Irish-Noise-002
	metrics:
	- bleu
	- wer
	model-index:
	- name: Whisper Medium GA-EN Speech Translation
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
	type: ymoslem/IWSLT2023-GA-EN
	metrics:
	- name: Bleu
	type: bleu
	value: 33.46
	- name: Wer
	type: wer
	value: 61.773975686627644
	---

	<!-- 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. -->

	# Whisper Medium GA-EN Speech Translation

	This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.3291
	- Bleu: 33.46
	- Chrf: 52.93
	- Wer: 61.7740

	## 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: 0.0001
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.03
	- training_steps: 9000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.4998 \| 0.0236 \| 100 \| 4.24 \| 19.77 \| 2.0245 \| 123.5029 \|
	\| 2.5999 \| 0.0472 \| 200 \| 5.55 \| 23.63 \| 2.0729 \| 130.1666 \|
	\| 2.4062 \| 0.0708 \| 300 \| 5.92 \| 24.15 \| 1.9928 \| 157.4966 \|
	\| 2.1866 \| 0.0944 \| 400 \| 12.74 \| 30.47 \| 1.8337 \| 93.4714 \|
	\| 2.2485 \| 0.1180 \| 500 \| 10.32 \| 30.65 \| 1.8209 \| 116.4791 \|
	\| 2.1521 \| 0.1416 \| 600 \| 9.84 \| 30.97 \| 1.7512 \| 130.1666 \|
	\| 1.9324 \| 0.1653 \| 700 \| 17.24 \| 34.37 \| 1.7362 \| 85.4570 \|
	\| 1.9703 \| 0.1889 \| 800 \| 13.05 \| 32.27 \| 1.6784 \| 105.7632 \|
	\| 1.7299 \| 0.2125 \| 900 \| 9.81 \| 31.71 \| 1.6530 \| 131.6974 \|
	\| 1.7822 \| 0.2361 \| 1000 \| 11.72 \| 32.5 \| 1.5541 \| 125.7091 \|
	\| 1.5493 \| 0.2597 \| 1100 \| 15.04 \| 36.72 \| 1.5773 \| 92.4358 \|
	\| 1.4813 \| 0.2833 \| 1200 \| 22.08 \| 40.11 \| 1.5341 \| 75.8667 \|
	\| 1.5285 \| 0.3069 \| 1300 \| 18.88 \| 40.93 \| 1.4834 \| 95.4975 \|
	\| 1.3255 \| 0.3305 \| 1400 \| 20.11 \| 40.82 \| 1.4956 \| 85.2319 \|
	\| 1.3931 \| 0.3541 \| 1500 \| 22.81 \| 41.51 \| 1.4718 \| 72.2197 \|
	\| 1.3962 \| 0.3777 \| 1600 \| 25.43 \| 43.53 \| 1.3794 \| 71.1842 \|
	\| 1.1412 \| 0.4013 \| 1700 \| 22.13 \| 43.19 \| 1.4172 \| 86.9428 \|
	\| 1.1132 \| 0.4249 \| 1800 \| 21.27 \| 42.45 \| 1.3989 \| 81.0896 \|
	\| 0.9261 \| 0.4485 \| 1900 \| 26.39 \| 45.4 \| 1.4147 \| 70.6889 \|
	\| 0.994 \| 0.4721 \| 2000 \| 24.38 \| 42.87 \| 1.4365 \| 77.5326 \|
	\| 0.8598 \| 0.4958 \| 2100 \| 19.36 \| 41.49 \| 1.3559 \| 96.6231 \|
	\| 0.7784 \| 0.5194 \| 2200 \| 26.54 \| 45.57 \| 1.3550 \| 69.5633 \|
	\| 0.7858 \| 0.5430 \| 2300 \| 27.52 \| 47.58 \| 1.3156 \| 68.8879 \|
	\| 0.7715 \| 0.5666 \| 2400 \| 26.12 \| 46.47 \| 1.2985 \| 72.5349 \|
	\| 0.7079 \| 0.5902 \| 2500 \| 25.62 \| 47.61 \| 1.3134 \| 68.6177 \|
	\| 0.6704 \| 0.6138 \| 2600 \| 28.2 \| 47.37 \| 1.3047 \| 69.1130 \|
	\| 0.6579 \| 0.6374 \| 2700 \| 29.52 \| 49.39 \| 1.2486 \| 68.2125 \|
	\| 0.502 \| 0.6610 \| 2800 \| 28.08 \| 48.99 \| 1.2511 \| 68.6177 \|
	\| 0.4442 \| 0.6846 \| 2900 \| 32.57 \| 50.66 \| 1.2800 \| 63.3498 \|
	\| 0.5175 \| 0.7082 \| 3000 \| 29.69 \| 48.77 \| 1.2650 \| 66.2314 \|
	\| 0.4416 \| 0.7318 \| 3100 \| 32.36 \| 50.29 \| 1.2554 \| 61.9090 \|
	\| 0.4529 \| 0.7554 \| 3200 \| 32.6 \| 50.94 \| 1.2050 \| 61.5489 \|
	\| 0.4435 \| 0.7790 \| 3300 \| 33.2 \| 52.17 \| 1.2103 \| 61.3688 \|
	\| 0.3724 \| 0.8026 \| 3400 \| 33.89 \| 52.88 \| 1.1756 \| 59.8379 \|
	\| 0.3883 \| 0.8263 \| 3500 \| 32.21 \| 51.86 \| 1.1979 \| 62.0891 \|
	\| 0.3534 \| 0.8499 \| 3600 \| 32.75 \| 51.85 \| 1.1943 \| 61.2337 \|
	\| 0.326 \| 0.8735 \| 3700 \| 32.43 \| 51.5 \| 1.1891 \| 62.1342 \|
	\| 0.305 \| 0.8971 \| 3800 \| 33.43 \| 51.45 \| 1.1858 \| 59.4327 \|
	\| 0.2258 \| 0.9207 \| 3900 \| 32.53 \| 51.42 \| 1.1827 \| 61.1887 \|
	\| 0.3104 \| 0.9443 \| 4000 \| 32.1 \| 51.33 \| 1.1857 \| 61.2337 \|
	\| 0.3847 \| 0.9679 \| 4100 \| 1.3506\| 29.91 \| 48.63 \| 66.5466 \|
	\| 0.426 \| 0.9915 \| 4200 \| 1.3458\| 25.68 \| 45.27 \| 70.1036 \|
	\| 0.2622 \| 1.0151 \| 4300 \| 1.3544\| 27.52 \| 48.0 \| 66.4115 \|
	\| 0.2429 \| 1.0387 \| 4400 \| 1.4330\| 22.57 \| 45.45 \| 79.9190 \|
	\| 0.269 \| 1.0623 \| 4500 \| 1.4399\| 24.7 \| 45.73 \| 74.7411 \|
	\| 0.3171 \| 1.0859 \| 4600 \| 1.3711\| 29.55 \| 47.78 \| 68.4827 \|
	\| 0.2321 \| 1.1095 \| 4700 \| 1.4350\| 24.73 \| 45.52 \| 77.1724 \|
	\| 0.2595 \| 1.1331 \| 4800 \| 1.3851\| 30.54 \| 47.85 \| 65.1508 \|
	\| 0.2426 \| 1.1568 \| 4900 \| 1.4109\| 28.87 \| 47.5 \| 68.3926 \|
	\| 0.2496 \| 1.1804 \| 5000 \| 1.3717\| 29.97 \| 48.74 \| 68.6628 \|
	\| 0.2551 \| 1.2040 \| 5100 \| 1.4157\| 29.92 \| 47.59 \| 66.3215 \|
	\| 0.231 \| 1.2276 \| 5200 \| 1.3908\| 28.97 \| 47.9 \| 66.0063 \|
	\| 0.245 \| 1.2512 \| 5300 \| 1.4082\| 30.22 \| 47.71 \| 63.7100 \|
	\| 0.284 \| 1.2748 \| 5400 \| 1.3696\| 27.47 \| 48.31 \| 70.7789 \|
	\| 0.2284 \| 1.2984 \| 5500 \| 1.4044\| 27.63 \| 47.37 \| 68.2575 \|
	\| 0.2457 \| 1.3220 \| 5600 \| 1.3722\| 31.38 \| 48.8 \| 64.7906 \|
	\| 0.2346 \| 1.3456 \| 5700 \| 1.3397\| 33.61 \| 50.14 \| 60.3332 \|
	\| 0.2088 \| 1.3692 \| 5800 \| 1.3920\| 30.84 \| 48.51 \| 65.4660 \|
	\| 0.1832 \| 1.3928 \| 5900 \| 1.3892\| 31.47 \| 49.56 \| 64.5205 \|
	\| 0.2171 \| 1.4164 \| 6000 \| 1.3606\| 32.51 \| 49.8 \| 63.1697 \|
	\| 0.1799 \| 1.4400 \| 6100 \| 1.4130\| 30.8 \| 50.05 \| 63.3949 \|
	\| 0.1756 \| 1.4636 \| 6200 \| 1.3458\| 30.25 \| 50.16 \| 66.1864 \|
	\| 0.1617 \| 1.4873 \| 6300 \| 1.3971\| 32.27 \| 50.74 \| 63.4849 \|
	\| 0.1909 \| 1.5109 \| 6400 \| 1.4275\| 27.41 \| 47.04 \| 72.0396 \|
	\| 0.1516 \| 1.5345 \| 6500 \| 1.3591\| 30.1 \| 49.05 \| 66.0513 \|
	\| 0.1892 \| 1.5581 \| 6600 \| 1.3646\| 31.72 \| 48.17 \| 62.6294 \|
	\| 0.2086 \| 1.5817 \| 6700 \| 1.3314\| 28.85 \| 49.68 \| 67.3120 \|
	\| 0.1253 \| 1.6053 \| 6800 \| 1.3461\| 29.84 \| 49.13 \| 66.5466 \|
	\| 0.1307 \| 1.6289 \| 6900 \| 1.3671\| 29.39 \| 48.77 \| 67.7172 \|
	\| 0.1376 \| 1.6525 \| 7000 \| 1.3769\| 31.27 \| 47.97 \| 66.5916 \|
	\| 0.1593 \| 1.6761 \| 7100 \| 1.3699\| 30.53 \| 49.33 \| 65.4660 \|
	\| 0.1604 \| 1.6997 \| 7200 \| 1.3540\| 31.99 \| 48.93 \| 63.8001 \|
	\| 0.118 \| 1.7233 \| 7300 \| 1.3523\| 29.52 \| 49.26 \| 67.5822 \|
	\| 0.1148 \| 1.7469 \| 7400 \| 1.3130\| 31.49 \| 49.49 \| 62.8996 \|
	\| 0.0946 \| 1.7705 \| 7500 \| 1.3468\| 32.6 \| 49.76 \| 63.1697 \|
	\| 0.0891 \| 1.7941 \| 7600 \| 1.3268\| 31.84 \| 50.41 \| 63.5750 \|
	\| 0.103 \| 1.8178 \| 7700 \| 1.3243\| 32.81 \| 50.61 \| 60.3782 \|
	\| 0.1016 \| 1.8414 \| 7800 \| 1.2945\| 33.07 \| 53.14 \| 61.0086 \|
	\| 0.1014 \| 1.8650 \| 7900 \| 1.3163\| 32.35 \| 51.28 \| 63.3498 \|
	\| 0.1257 \| 1.8886 \| 8000 \| 1.3246\| 31.65 \| 51.86 \| 61.7740 \|
	\| 0.0859 \| 1.9122 \| 8100 \| 1.3247\| 30.69 \| 51.47 \| 64.4304 \|
	\| 0.0943 \| 1.9358 \| 8200 \| 1.3030\| 33.06 \| 52.31 \| 61.6389 \|
	\| 0.11 \| 1.9594 \| 8300 \| 1.2866\| 33.32 \| 52.83 \| 60.1081 \|
	\| 0.0723 \| 1.9830 \| 8400 \| 1.3071\| 32.96 \| 51.64 \| 61.7740 \|
	\| 0.0312 \| 2.0066 \| 8500 \| 1.3202\| 33.2 \| 52.78 \| 62.0891 \|
	\| 0.0303 \| 2.0302 \| 8600 \| 1.3348\| 33.24 \| 52.75 \| 62.4043 \|
	\| 0.02 \| 2.0538 \| 8700 \| 1.3447\| 33.32 \| 52.6 \| 62.0891 \|
	\| 0.0329 \| 2.0774 \| 8800 \| 1.3328\| 34.04 \| 52.93 \| 60.7384 \|
	\| 0.0216 \| 2.1010 \| 8900 \| 1.3266\| 33.47 \| 52.75 \| 61.3237 \|
	\| 0.0224 \| 2.1246 \| 9000 \| 1.3291\| 33.46 \| 52.93 \| 61.7740 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.2.0+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1

	---
	language:
	- ga
	- en
	license: apache-2.0
	base_model: openai/whisper-medium
	tags:
	- generated_from_trainer
	datasets:
	- ymoslem/IWSLT2023-GA-EN
	- ymoslem/FLEURS-GA-EN
	- ymoslem/BitesizeIrish-GA-EN
	- ymoslem/SpokenWords-GA-EN-MTed
	- ymoslem/Tatoeba-Speech-Irish
	- ymoslem/Wikimedia-Speech-Irish
	- ymoslem/Tatoeba-Speech-Irish-Noise-002
	- ymoslem/Wikimedia-Speech-Irish-Noise-002
	metrics:
	- bleu
	- wer
	model-index:
	- name: Whisper Medium GA-EN Speech Translation
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
	type: ymoslem/IWSLT2023-GA-EN
	metrics:
	- name: Bleu
	type: bleu
	value: 33.46
	- name: Wer
	type: wer
	value: 61.773975686627644
	---

	<!-- 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. -->

	# Whisper Medium GA-EN Speech Translation

	This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.3291
	- Bleu: 33.46
	- Chrf: 52.93
	- Wer: 61.7740

	## 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: 0.0001
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.03
	- training_steps: 9000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.4998 \| 0.0236 \| 100 \| 4.24 \| 19.77 \| 2.0245 \| 123.5029 \|
	\| 2.5999 \| 0.0472 \| 200 \| 5.55 \| 23.63 \| 2.0729 \| 130.1666 \|
	\| 2.4062 \| 0.0708 \| 300 \| 5.92 \| 24.15 \| 1.9928 \| 157.4966 \|
	\| 2.1866 \| 0.0944 \| 400 \| 12.74 \| 30.47 \| 1.8337 \| 93.4714 \|
	\| 2.2485 \| 0.1180 \| 500 \| 10.32 \| 30.65 \| 1.8209 \| 116.4791 \|
	\| 2.1521 \| 0.1416 \| 600 \| 9.84 \| 30.97 \| 1.7512 \| 130.1666 \|
	\| 1.9324 \| 0.1653 \| 700 \| 17.24 \| 34.37 \| 1.7362 \| 85.4570 \|
	\| 1.9703 \| 0.1889 \| 800 \| 13.05 \| 32.27 \| 1.6784 \| 105.7632 \|
	\| 1.7299 \| 0.2125 \| 900 \| 9.81 \| 31.71 \| 1.6530 \| 131.6974 \|
	\| 1.7822 \| 0.2361 \| 1000 \| 11.72 \| 32.5 \| 1.5541 \| 125.7091 \|
	\| 1.5493 \| 0.2597 \| 1100 \| 15.04 \| 36.72 \| 1.5773 \| 92.4358 \|
	\| 1.4813 \| 0.2833 \| 1200 \| 22.08 \| 40.11 \| 1.5341 \| 75.8667 \|
	\| 1.5285 \| 0.3069 \| 1300 \| 18.88 \| 40.93 \| 1.4834 \| 95.4975 \|
	\| 1.3255 \| 0.3305 \| 1400 \| 20.11 \| 40.82 \| 1.4956 \| 85.2319 \|
	\| 1.3931 \| 0.3541 \| 1500 \| 22.81 \| 41.51 \| 1.4718 \| 72.2197 \|
	\| 1.3962 \| 0.3777 \| 1600 \| 25.43 \| 43.53 \| 1.3794 \| 71.1842 \|
	\| 1.1412 \| 0.4013 \| 1700 \| 22.13 \| 43.19 \| 1.4172 \| 86.9428 \|
	\| 1.1132 \| 0.4249 \| 1800 \| 21.27 \| 42.45 \| 1.3989 \| 81.0896 \|
	\| 0.9261 \| 0.4485 \| 1900 \| 26.39 \| 45.4 \| 1.4147 \| 70.6889 \|
	\| 0.994 \| 0.4721 \| 2000 \| 24.38 \| 42.87 \| 1.4365 \| 77.5326 \|
	\| 0.8598 \| 0.4958 \| 2100 \| 19.36 \| 41.49 \| 1.3559 \| 96.6231 \|
	\| 0.7784 \| 0.5194 \| 2200 \| 26.54 \| 45.57 \| 1.3550 \| 69.5633 \|
	\| 0.7858 \| 0.5430 \| 2300 \| 27.52 \| 47.58 \| 1.3156 \| 68.8879 \|
	\| 0.7715 \| 0.5666 \| 2400 \| 26.12 \| 46.47 \| 1.2985 \| 72.5349 \|
	\| 0.7079 \| 0.5902 \| 2500 \| 25.62 \| 47.61 \| 1.3134 \| 68.6177 \|
	\| 0.6704 \| 0.6138 \| 2600 \| 28.2 \| 47.37 \| 1.3047 \| 69.1130 \|
	\| 0.6579 \| 0.6374 \| 2700 \| 29.52 \| 49.39 \| 1.2486 \| 68.2125 \|
	\| 0.502 \| 0.6610 \| 2800 \| 28.08 \| 48.99 \| 1.2511 \| 68.6177 \|
	\| 0.4442 \| 0.6846 \| 2900 \| 32.57 \| 50.66 \| 1.2800 \| 63.3498 \|
	\| 0.5175 \| 0.7082 \| 3000 \| 29.69 \| 48.77 \| 1.2650 \| 66.2314 \|
	\| 0.4416 \| 0.7318 \| 3100 \| 32.36 \| 50.29 \| 1.2554 \| 61.9090 \|
	\| 0.4529 \| 0.7554 \| 3200 \| 32.6 \| 50.94 \| 1.2050 \| 61.5489 \|
	\| 0.4435 \| 0.7790 \| 3300 \| 33.2 \| 52.17 \| 1.2103 \| 61.3688 \|
	\| 0.3724 \| 0.8026 \| 3400 \| 33.89 \| 52.88 \| 1.1756 \| 59.8379 \|
	\| 0.3883 \| 0.8263 \| 3500 \| 32.21 \| 51.86 \| 1.1979 \| 62.0891 \|
	\| 0.3534 \| 0.8499 \| 3600 \| 32.75 \| 51.85 \| 1.1943 \| 61.2337 \|
	\| 0.326 \| 0.8735 \| 3700 \| 32.43 \| 51.5 \| 1.1891 \| 62.1342 \|
	\| 0.305 \| 0.8971 \| 3800 \| 33.43 \| 51.45 \| 1.1858 \| 59.4327 \|
	\| 0.2258 \| 0.9207 \| 3900 \| 32.53 \| 51.42 \| 1.1827 \| 61.1887 \|
	\| 0.3104 \| 0.9443 \| 4000 \| 32.1 \| 51.33 \| 1.1857 \| 61.2337 \|
	\| 0.3847 \| 0.9679 \| 4100 \| 1.3506\| 29.91 \| 48.63 \| 66.5466 \|
	\| 0.426 \| 0.9915 \| 4200 \| 1.3458\| 25.68 \| 45.27 \| 70.1036 \|
	\| 0.2622 \| 1.0151 \| 4300 \| 1.3544\| 27.52 \| 48.0 \| 66.4115 \|
	\| 0.2429 \| 1.0387 \| 4400 \| 1.4330\| 22.57 \| 45.45 \| 79.9190 \|
	\| 0.269 \| 1.0623 \| 4500 \| 1.4399\| 24.7 \| 45.73 \| 74.7411 \|
	\| 0.3171 \| 1.0859 \| 4600 \| 1.3711\| 29.55 \| 47.78 \| 68.4827 \|
	\| 0.2321 \| 1.1095 \| 4700 \| 1.4350\| 24.73 \| 45.52 \| 77.1724 \|
	\| 0.2595 \| 1.1331 \| 4800 \| 1.3851\| 30.54 \| 47.85 \| 65.1508 \|
	\| 0.2426 \| 1.1568 \| 4900 \| 1.4109\| 28.87 \| 47.5 \| 68.3926 \|
	\| 0.2496 \| 1.1804 \| 5000 \| 1.3717\| 29.97 \| 48.74 \| 68.6628 \|
	\| 0.2551 \| 1.2040 \| 5100 \| 1.4157\| 29.92 \| 47.59 \| 66.3215 \|
	\| 0.231 \| 1.2276 \| 5200 \| 1.3908\| 28.97 \| 47.9 \| 66.0063 \|
	\| 0.245 \| 1.2512 \| 5300 \| 1.4082\| 30.22 \| 47.71 \| 63.7100 \|
	\| 0.284 \| 1.2748 \| 5400 \| 1.3696\| 27.47 \| 48.31 \| 70.7789 \|
	\| 0.2284 \| 1.2984 \| 5500 \| 1.4044\| 27.63 \| 47.37 \| 68.2575 \|
	\| 0.2457 \| 1.3220 \| 5600 \| 1.3722\| 31.38 \| 48.8 \| 64.7906 \|
	\| 0.2346 \| 1.3456 \| 5700 \| 1.3397\| 33.61 \| 50.14 \| 60.3332 \|
	\| 0.2088 \| 1.3692 \| 5800 \| 1.3920\| 30.84 \| 48.51 \| 65.4660 \|
	\| 0.1832 \| 1.3928 \| 5900 \| 1.3892\| 31.47 \| 49.56 \| 64.5205 \|
	\| 0.2171 \| 1.4164 \| 6000 \| 1.3606\| 32.51 \| 49.8 \| 63.1697 \|
	\| 0.1799 \| 1.4400 \| 6100 \| 1.4130\| 30.8 \| 50.05 \| 63.3949 \|
	\| 0.1756 \| 1.4636 \| 6200 \| 1.3458\| 30.25 \| 50.16 \| 66.1864 \|
	\| 0.1617 \| 1.4873 \| 6300 \| 1.3971\| 32.27 \| 50.74 \| 63.4849 \|
	\| 0.1909 \| 1.5109 \| 6400 \| 1.4275\| 27.41 \| 47.04 \| 72.0396 \|
	\| 0.1516 \| 1.5345 \| 6500 \| 1.3591\| 30.1 \| 49.05 \| 66.0513 \|
	\| 0.1892 \| 1.5581 \| 6600 \| 1.3646\| 31.72 \| 48.17 \| 62.6294 \|
	\| 0.2086 \| 1.5817 \| 6700 \| 1.3314\| 28.85 \| 49.68 \| 67.3120 \|
	\| 0.1253 \| 1.6053 \| 6800 \| 1.3461\| 29.84 \| 49.13 \| 66.5466 \|
	\| 0.1307 \| 1.6289 \| 6900 \| 1.3671\| 29.39 \| 48.77 \| 67.7172 \|
	\| 0.1376 \| 1.6525 \| 7000 \| 1.3769\| 31.27 \| 47.97 \| 66.5916 \|
	\| 0.1593 \| 1.6761 \| 7100 \| 1.3699\| 30.53 \| 49.33 \| 65.4660 \|
	\| 0.1604 \| 1.6997 \| 7200 \| 1.3540\| 31.99 \| 48.93 \| 63.8001 \|
	\| 0.118 \| 1.7233 \| 7300 \| 1.3523\| 29.52 \| 49.26 \| 67.5822 \|
	\| 0.1148 \| 1.7469 \| 7400 \| 1.3130\| 31.49 \| 49.49 \| 62.8996 \|
	\| 0.0946 \| 1.7705 \| 7500 \| 1.3468\| 32.6 \| 49.76 \| 63.1697 \|
	\| 0.0891 \| 1.7941 \| 7600 \| 1.3268\| 31.84 \| 50.41 \| 63.5750 \|
	\| 0.103 \| 1.8178 \| 7700 \| 1.3243\| 32.81 \| 50.61 \| 60.3782 \|
	\| 0.1016 \| 1.8414 \| 7800 \| 1.2945\| 33.07 \| 53.14 \| 61.0086 \|
	\| 0.1014 \| 1.8650 \| 7900 \| 1.3163\| 32.35 \| 51.28 \| 63.3498 \|
	\| 0.1257 \| 1.8886 \| 8000 \| 1.3246\| 31.65 \| 51.86 \| 61.7740 \|
	\| 0.0859 \| 1.9122 \| 8100 \| 1.3247\| 30.69 \| 51.47 \| 64.4304 \|
	\| 0.0943 \| 1.9358 \| 8200 \| 1.3030\| 33.06 \| 52.31 \| 61.6389 \|
	\| 0.11 \| 1.9594 \| 8300 \| 1.2866\| 33.32 \| 52.83 \| 60.1081 \|
	\| 0.0723 \| 1.9830 \| 8400 \| 1.3071\| 32.96 \| 51.64 \| 61.7740 \|
	\| 0.0312 \| 2.0066 \| 8500 \| 1.3202\| 33.2 \| 52.78 \| 62.0891 \|
	\| 0.0303 \| 2.0302 \| 8600 \| 1.3348\| 33.24 \| 52.75 \| 62.4043 \|
	\| 0.02 \| 2.0538 \| 8700 \| 1.3447\| 33.32 \| 52.6 \| 62.0891 \|
	\| 0.0329 \| 2.0774 \| 8800 \| 1.3328\| 34.04 \| 52.93 \| 60.7384 \|
	\| 0.0216 \| 2.1010 \| 8900 \| 1.3266\| 33.47 \| 52.75 \| 61.3237 \|
	\| 0.0224 \| 2.1246 \| 9000 \| 1.3291\| 33.46 \| 52.93 \| 61.7740 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.2.0+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1