whisper-medium-ga2en-v5.2.1-r / README.md

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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
	metrics:
	- bleu
	- wer
	model-index:
	- name: Whisper Small GA-EN Speech Translation, 1 epoch, 10k steps
	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: 36.12
	- name: Wer
	type: wer
	value: 58.307068887888335
	---

	<!-- 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 Small GA-EN Speech Translation, 1 epoch, 10k steps

	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: 1.3134
	- Bleu: 36.12
	- Chrf: 53.74
	- Wer: 58.3071

	## 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: 0.02
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.6291 \| 0.0109 \| 100 \| 2.33 \| 16.34 \| 2.1971 \| 175.5516 \|
	\| 2.6591 \| 0.0219 \| 200 \| 5.57 \| 22.49 \| 2.0357 \| 122.2873 \|
	\| 2.5637 \| 0.0328 \| 300 \| 7.67 \| 26.29 \| 1.8690 \| 133.0032 \|
	\| 2.2954 \| 0.0438 \| 400 \| 11.2 \| 30.03 \| 1.8062 \| 114.2278 \|
	\| 2.3292 \| 0.0547 \| 500 \| 9.85 \| 29.28 \| 1.7421 \| 117.2895 \|
	\| 2.1223 \| 0.0657 \| 600 \| 14.56 \| 32.56 \| 1.6739 \| 84.2864 \|
	\| 2.2398 \| 0.0766 \| 700 \| 13.86 \| 34.74 \| 1.7187 \| 98.9644 \|
	\| 2.002 \| 0.0876 \| 800 \| 15.53 \| 36.64 \| 1.6392 \| 96.7582 \|
	\| 1.8611 \| 0.0985 \| 900 \| 15.8 \| 36.32 \| 1.6283 \| 94.3719 \|
	\| 1.8498 \| 0.1095 \| 1000 \| 17.58 \| 36.0 \| 1.6102 \| 85.5921 \|
	\| 1.7585 \| 0.1204 \| 1100 \| 15.91 \| 36.61 \| 1.6337 \| 100.2251 \|
	\| 1.6115 \| 0.1314 \| 1200 \| 22.21 \| 39.94 \| 1.5381 \| 76.8122 \|
	\| 1.4415 \| 0.1423 \| 1300 \| 20.36 \| 37.87 \| 1.5864 \| 79.1986 \|
	\| 1.5103 \| 0.1533 \| 1400 \| 23.2 \| 41.26 \| 1.4925 \| 75.2364 \|
	\| 1.6576 \| 0.1642 \| 1500 \| 18.12 \| 40.49 \| 1.4508 \| 102.9266 \|
	\| 1.3429 \| 0.1752 \| 1600 \| 27.88 \| 43.74 \| 1.4399 \| 69.7884 \|
	\| 1.2522 \| 0.1861 \| 1700 \| 23.04 \| 43.31 \| 1.4256 \| 77.1724 \|
	\| 1.2018 \| 0.1970 \| 1800 \| 21.06 \| 40.39 \| 1.4072 \| 78.6583 \|
	\| 1.1945 \| 0.2080 \| 1900 \| 23.0 \| 42.71 \| 1.4222 \| 76.7222 \|
	\| 1.1869 \| 0.2189 \| 2000 \| 22.54 \| 42.02 \| 1.3992 \| 75.8667 \|
	\| 1.1752 \| 0.2299 \| 2100 \| 20.81 \| 41.07 \| 1.3926 \| 79.5137 \|
	\| 1.0281 \| 0.2408 \| 2200 \| 27.24 \| 45.55 \| 1.3633 \| 69.6083 \|
	\| 0.894 \| 0.2518 \| 2300 \| 28.6 \| 45.58 \| 1.3287 \| 65.8712 \|
	\| 0.9788 \| 0.2627 \| 2400 \| 27.75 \| 46.21 \| 1.3138 \| 69.2931 \|
	\| 0.8418 \| 0.2737 \| 2500 \| 27.85 \| 46.17 \| 1.3064 \| 68.3026 \|
	\| 0.7559 \| 0.2846 \| 2600 \| 28.44 \| 48.52 \| 1.2903 \| 68.3476 \|
	\| 0.8632 \| 0.2956 \| 2700 \| 27.87 \| 46.86 \| 1.2834 \| 68.3476 \|
	\| 0.7501 \| 0.3065 \| 2800 \| 28.63 \| 49.25 \| 1.2669 \| 68.5277 \|
	\| 0.6953 \| 0.3175 \| 2900 \| 30.46 \| 48.83 \| 1.2615 \| 64.4304 \|
	\| 0.7195 \| 0.3284 \| 3000 \| 27.49 \| 47.94 \| 1.2514 \| 71.0941 \|
	\| 0.6155 \| 0.3394 \| 3100 \| 30.06 \| 49.64 \| 1.2428 \| 66.5916 \|
	\| 0.605 \| 0.3503 \| 3200 \| 31.64 \| 50.27 \| 1.2040 \| 63.8451 \|
	\| 0.6349 \| 0.3612 \| 3300 \| 28.96 \| 49.35 \| 1.2077 \| 65.3760 \|
	\| 0.4669 \| 0.3722 \| 3400 \| 31.17 \| 48.95 \| 1.2219 \| 64.2503 \|
	\| 0.5196 \| 0.3831 \| 3500 \| 30.97 \| 50.13 \| 1.2124 \| 63.8001 \|
	\| 0.5141 \| 0.3941 \| 3600 \| 31.97 \| 50.8 \| 1.2026 \| 63.0347 \|
	\| 0.4221 \| 0.4050 \| 3700 \| 31.76 \| 51.35 \| 1.1893 \| 63.4399 \|
	\| 0.2951 \| 0.4160 \| 3800 \| 32.4 \| 51.08 \| 1.2049 \| 63.1247 \|
	\| 0.3898 \| 0.4269 \| 3900 \| 32.15 \| 51.09 \| 1.1906 \| 63.5299 \|
	\| 0.4071 \| 0.4379 \| 4000 \| 33.1 \| 51.85 \| 1.1873 \| 62.4043 \|
	\| 0.3975 \| 0.4488 \| 4100 \| 29.58 \| 49.33 \| 1.2117 \| 70.3287 \|
	\| 0.4206 \| 0.4598 \| 4200 \| 31.69 \| 50.8 \| 1.2150 \| 65.0158 \|
	\| 0.2935 \| 0.4707 \| 4300 \| 32.9 \| 50.01 \| 1.2484 \| 62.8546 \|
	\| 0.3718 \| 0.4817 \| 4400 \| 31.64 \| 50.55 \| 1.2055 \| 63.8451 \|
	\| 0.3722 \| 0.4926 \| 4500 \| 28.16 \| 49.28 \| 1.2200 \| 70.4638 \|
	\| 0.2986 \| 0.5036 \| 4600 \| 28.76 \| 49.9 \| 1.2240 \| 68.7528 \|
	\| 0.3327 \| 0.5145 \| 4700 \| 29.34 \| 49.67 \| 1.2052 \| 67.5822 \|
	\| 0.2489 \| 0.5255 \| 4800 \| 32.52 \| 51.77 \| 1.2083 \| 62.4493 \|
	\| 0.3653 \| 0.5364 \| 4900 \| 31.48 \| 51.16 \| 1.2166 \| 63.8451 \|
	\| 0.3326 \| 0.5473 \| 5000 \| 33.04 \| 51.71 \| 1.2169 \| 62.4493 \|
	\| 0.3045 \| 0.5583 \| 5100 \| 27.45 \| 48.22 \| 1.2460 \| 68.9779 \|
	\| 0.3444 \| 0.5692 \| 5200 \| 33.14 \| 50.76 \| 1.2829 \| 62.2692 \|
	\| 0.3236 \| 0.5802 \| 5300 \| 28.89 \| 49.37 \| 1.2499 \| 70.3737 \|
	\| 0.3004 \| 0.5911 \| 5400 \| 29.89 \| 49.29 \| 1.3165 \| 68.7078 \|
	\| 0.3019 \| 0.6021 \| 5500 \| 32.8 \| 49.78 \| 1.2782 \| 62.8095 \|
	\| 0.2923 \| 0.6130 \| 5600 \| 31.75 \| 50.26 \| 1.2468 \| 63.3498 \|
	\| 0.3237 \| 0.6240 \| 5700 \| 34.4 \| 52.59 \| 1.2511 \| 61.0986 \|
	\| 0.2226 \| 0.6349 \| 5800 \| 30.51 \| 50.38 \| 1.2479 \| 63.3498 \|
	\| 0.2207 \| 0.6459 \| 5900 \| 32.68 \| 51.97 \| 1.2641 \| 62.1342 \|
	\| 0.2017 \| 0.6568 \| 6000 \| 32.47 \| 51.36 \| 1.2640 \| 62.6745 \|
	\| 0.201 \| 0.6678 \| 6100 \| 33.6 \| 52.29 \| 1.2774 \| 61.4588 \|
	\| 0.203 \| 0.6787 \| 6200 \| 30.27 \| 50.84 \| 1.2670 \| 65.6461 \|
	\| 0.1456 \| 0.6897 \| 6300 \| 31.2 \| 51.05 \| 1.2656 \| 63.3048 \|
	\| 0.1607 \| 0.7006 \| 6400 \| 30.39 \| 51.04 \| 1.2611 \| 65.8262 \|
	\| 0.1933 \| 0.7115 \| 6500 \| 31.78 \| 50.92 \| 1.2545 \| 63.0797 \|
	\| 0.1537 \| 0.7225 \| 6600 \| 30.18 \| 50.18 \| 1.2500 \| 64.7006 \|
	\| 0.1279 \| 0.7334 \| 6700 \| 33.23 \| 51.0 \| 1.2548 \| 59.8379 \|
	\| 0.1189 \| 0.7444 \| 6800 \| 33.51 \| 50.67 \| 1.2594 \| 61.1887 \|
	\| 0.1056 \| 0.7553 \| 6900 \| 32.97 \| 51.02 \| 1.2578 \| 61.9991 \|
	\| 0.1105 \| 0.7663 \| 7000 \| 32.74 \| 50.83 \| 1.2569 \| 62.0441 \|
	\| 0.1183 \| 0.7772 \| 7100 \| 34.07 \| 52.2 \| 1.2590 \| 60.4232 \|
	\| 0.1373 \| 0.7882 \| 7200 \| 33.55 \| 50.6 \| 1.2430 \| 61.2787 \|
	\| 0.1325 \| 0.7991 \| 7300 \| 32.36 \| 50.39 \| 1.2548 \| 62.3143 \|
	\| 0.0907 \| 0.8101 \| 7400 \| 32.28 \| 50.99 \| 1.2578 \| 61.2787 \|
	\| 0.0919 \| 0.8210 \| 7500 \| 33.01 \| 51.81 \| 1.2791 \| 60.4683 \|
	\| 0.0852 \| 0.8320 \| 7600 \| 32.97 \| 51.56 \| 1.2782 \| 61.5489 \|
	\| 0.1223 \| 0.8429 \| 7700 \| 33.57 \| 52.33 \| 1.2638 \| 59.9280 \|
	\| 0.0826 \| 0.8539 \| 7800 \| 33.83 \| 52.7 \| 1.2634 \| 60.1531 \|
	\| 0.0783 \| 0.8648 \| 7900 \| 33.79 \| 52.31 \| 1.2595 \| 60.1081 \|
	\| 0.0986 \| 0.8758 \| 8000 \| 34.33 \| 52.54 \| 1.2608 \| 59.4327 \|
	\| 0.1148 \| 0.8867 \| 8100 \| 1.2736\| 34.03 \| 52.52 \| 59.8829 \|
	\| 0.1134 \| 0.8976 \| 8200 \| 1.3073\| 34.14 \| 51.64 \| 61.5038 \|
	\| 0.1166 \| 0.9086 \| 8300 \| 1.3385\| 30.51 \| 49.26 \| 65.5561 \|
	\| 0.0871 \| 0.9195 \| 8400 \| 1.3313\| 32.31 \| 51.06 \| 62.5394 \|
	\| 0.0927 \| 0.9305 \| 8500 \| 1.3898\| 28.64 \| 48.43 \| 69.3832 \|
	\| 0.1012 \| 0.9414 \| 8600 \| 1.3144\| 33.12 \| 52.02 \| 61.4138 \|
	\| 0.0742 \| 0.9524 \| 8700 \| 1.3284\| 33.68 \| 51.38 \| 61.7740 \|
	\| 0.0802 \| 0.9633 \| 8800 \| 1.3300\| 34.33 \| 51.38 \| 61.4138 \|
	\| 0.0799 \| 0.9743 \| 8900 \| 1.3328\| 33.72 \| 50.77 \| 60.1981 \|
	\| 0.0936 \| 0.9852 \| 9000 \| 1.3181\| 34.76 \| 51.4 \| 60.0630 \|
	\| 0.1091 \| 0.9962 \| 9100 \| 1.3096\| 35.13 \| 52.6 \| 59.9730 \|
	\| 0.0427 \| 1.0071 \| 9200 \| 1.2905\| 35.49 \| 53.12 \| 59.8379 \|
	\| 0.0338 \| 1.0181 \| 9300 \| 1.3097\| 35.33 \| 52.62 \| 60.5133 \|
	\| 0.0363 \| 1.0290 \| 9400 \| 1.3172\| 35.51 \| 53.06 \| 59.6128 \|
	\| 0.0319 \| 1.0400 \| 9500 \| 1.3166\| 36.82 \| 53.6 \| 58.3971 \|
	\| 0.0434 \| 1.0509 \| 9600 \| 1.3050\| 35.62 \| 53.28 \| 59.6578 \|
	\| 0.0218 \| 1.0619 \| 9700 \| 1.3096\| 35.57 \| 53.28 \| 59.5227 \|
	\| 0.0316 \| 1.0728 \| 9800 \| 1.3162\| 36.14 \| 53.87 \| 58.3971 \|
	\| 0.0315 \| 1.0837 \| 9900 \| 1.3121\| 36.26 \| 54.16 \| 58.3521 \|
	\| 0.0229 \| 1.0947 \| 10000 \| 1.3134\| 36.12 \| 53.74 \| 58.3071 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.2.0+cu121
	- Datasets 2.19.2
	- 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
	metrics:
	- bleu
	- wer
	model-index:
	- name: Whisper Small GA-EN Speech Translation, 1 epoch, 10k steps
	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: 36.12
	- name: Wer
	type: wer
	value: 58.307068887888335
	---

	<!-- 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 Small GA-EN Speech Translation, 1 epoch, 10k steps

	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: 1.3134
	- Bleu: 36.12
	- Chrf: 53.74
	- Wer: 58.3071

	## 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: 0.02
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.6291 \| 0.0109 \| 100 \| 2.33 \| 16.34 \| 2.1971 \| 175.5516 \|
	\| 2.6591 \| 0.0219 \| 200 \| 5.57 \| 22.49 \| 2.0357 \| 122.2873 \|
	\| 2.5637 \| 0.0328 \| 300 \| 7.67 \| 26.29 \| 1.8690 \| 133.0032 \|
	\| 2.2954 \| 0.0438 \| 400 \| 11.2 \| 30.03 \| 1.8062 \| 114.2278 \|
	\| 2.3292 \| 0.0547 \| 500 \| 9.85 \| 29.28 \| 1.7421 \| 117.2895 \|
	\| 2.1223 \| 0.0657 \| 600 \| 14.56 \| 32.56 \| 1.6739 \| 84.2864 \|
	\| 2.2398 \| 0.0766 \| 700 \| 13.86 \| 34.74 \| 1.7187 \| 98.9644 \|
	\| 2.002 \| 0.0876 \| 800 \| 15.53 \| 36.64 \| 1.6392 \| 96.7582 \|
	\| 1.8611 \| 0.0985 \| 900 \| 15.8 \| 36.32 \| 1.6283 \| 94.3719 \|
	\| 1.8498 \| 0.1095 \| 1000 \| 17.58 \| 36.0 \| 1.6102 \| 85.5921 \|
	\| 1.7585 \| 0.1204 \| 1100 \| 15.91 \| 36.61 \| 1.6337 \| 100.2251 \|
	\| 1.6115 \| 0.1314 \| 1200 \| 22.21 \| 39.94 \| 1.5381 \| 76.8122 \|
	\| 1.4415 \| 0.1423 \| 1300 \| 20.36 \| 37.87 \| 1.5864 \| 79.1986 \|
	\| 1.5103 \| 0.1533 \| 1400 \| 23.2 \| 41.26 \| 1.4925 \| 75.2364 \|
	\| 1.6576 \| 0.1642 \| 1500 \| 18.12 \| 40.49 \| 1.4508 \| 102.9266 \|
	\| 1.3429 \| 0.1752 \| 1600 \| 27.88 \| 43.74 \| 1.4399 \| 69.7884 \|
	\| 1.2522 \| 0.1861 \| 1700 \| 23.04 \| 43.31 \| 1.4256 \| 77.1724 \|
	\| 1.2018 \| 0.1970 \| 1800 \| 21.06 \| 40.39 \| 1.4072 \| 78.6583 \|
	\| 1.1945 \| 0.2080 \| 1900 \| 23.0 \| 42.71 \| 1.4222 \| 76.7222 \|
	\| 1.1869 \| 0.2189 \| 2000 \| 22.54 \| 42.02 \| 1.3992 \| 75.8667 \|
	\| 1.1752 \| 0.2299 \| 2100 \| 20.81 \| 41.07 \| 1.3926 \| 79.5137 \|
	\| 1.0281 \| 0.2408 \| 2200 \| 27.24 \| 45.55 \| 1.3633 \| 69.6083 \|
	\| 0.894 \| 0.2518 \| 2300 \| 28.6 \| 45.58 \| 1.3287 \| 65.8712 \|
	\| 0.9788 \| 0.2627 \| 2400 \| 27.75 \| 46.21 \| 1.3138 \| 69.2931 \|
	\| 0.8418 \| 0.2737 \| 2500 \| 27.85 \| 46.17 \| 1.3064 \| 68.3026 \|
	\| 0.7559 \| 0.2846 \| 2600 \| 28.44 \| 48.52 \| 1.2903 \| 68.3476 \|
	\| 0.8632 \| 0.2956 \| 2700 \| 27.87 \| 46.86 \| 1.2834 \| 68.3476 \|
	\| 0.7501 \| 0.3065 \| 2800 \| 28.63 \| 49.25 \| 1.2669 \| 68.5277 \|
	\| 0.6953 \| 0.3175 \| 2900 \| 30.46 \| 48.83 \| 1.2615 \| 64.4304 \|
	\| 0.7195 \| 0.3284 \| 3000 \| 27.49 \| 47.94 \| 1.2514 \| 71.0941 \|
	\| 0.6155 \| 0.3394 \| 3100 \| 30.06 \| 49.64 \| 1.2428 \| 66.5916 \|
	\| 0.605 \| 0.3503 \| 3200 \| 31.64 \| 50.27 \| 1.2040 \| 63.8451 \|
	\| 0.6349 \| 0.3612 \| 3300 \| 28.96 \| 49.35 \| 1.2077 \| 65.3760 \|
	\| 0.4669 \| 0.3722 \| 3400 \| 31.17 \| 48.95 \| 1.2219 \| 64.2503 \|
	\| 0.5196 \| 0.3831 \| 3500 \| 30.97 \| 50.13 \| 1.2124 \| 63.8001 \|
	\| 0.5141 \| 0.3941 \| 3600 \| 31.97 \| 50.8 \| 1.2026 \| 63.0347 \|
	\| 0.4221 \| 0.4050 \| 3700 \| 31.76 \| 51.35 \| 1.1893 \| 63.4399 \|
	\| 0.2951 \| 0.4160 \| 3800 \| 32.4 \| 51.08 \| 1.2049 \| 63.1247 \|
	\| 0.3898 \| 0.4269 \| 3900 \| 32.15 \| 51.09 \| 1.1906 \| 63.5299 \|
	\| 0.4071 \| 0.4379 \| 4000 \| 33.1 \| 51.85 \| 1.1873 \| 62.4043 \|
	\| 0.3975 \| 0.4488 \| 4100 \| 29.58 \| 49.33 \| 1.2117 \| 70.3287 \|
	\| 0.4206 \| 0.4598 \| 4200 \| 31.69 \| 50.8 \| 1.2150 \| 65.0158 \|
	\| 0.2935 \| 0.4707 \| 4300 \| 32.9 \| 50.01 \| 1.2484 \| 62.8546 \|
	\| 0.3718 \| 0.4817 \| 4400 \| 31.64 \| 50.55 \| 1.2055 \| 63.8451 \|
	\| 0.3722 \| 0.4926 \| 4500 \| 28.16 \| 49.28 \| 1.2200 \| 70.4638 \|
	\| 0.2986 \| 0.5036 \| 4600 \| 28.76 \| 49.9 \| 1.2240 \| 68.7528 \|
	\| 0.3327 \| 0.5145 \| 4700 \| 29.34 \| 49.67 \| 1.2052 \| 67.5822 \|
	\| 0.2489 \| 0.5255 \| 4800 \| 32.52 \| 51.77 \| 1.2083 \| 62.4493 \|
	\| 0.3653 \| 0.5364 \| 4900 \| 31.48 \| 51.16 \| 1.2166 \| 63.8451 \|
	\| 0.3326 \| 0.5473 \| 5000 \| 33.04 \| 51.71 \| 1.2169 \| 62.4493 \|
	\| 0.3045 \| 0.5583 \| 5100 \| 27.45 \| 48.22 \| 1.2460 \| 68.9779 \|
	\| 0.3444 \| 0.5692 \| 5200 \| 33.14 \| 50.76 \| 1.2829 \| 62.2692 \|
	\| 0.3236 \| 0.5802 \| 5300 \| 28.89 \| 49.37 \| 1.2499 \| 70.3737 \|
	\| 0.3004 \| 0.5911 \| 5400 \| 29.89 \| 49.29 \| 1.3165 \| 68.7078 \|
	\| 0.3019 \| 0.6021 \| 5500 \| 32.8 \| 49.78 \| 1.2782 \| 62.8095 \|
	\| 0.2923 \| 0.6130 \| 5600 \| 31.75 \| 50.26 \| 1.2468 \| 63.3498 \|
	\| 0.3237 \| 0.6240 \| 5700 \| 34.4 \| 52.59 \| 1.2511 \| 61.0986 \|
	\| 0.2226 \| 0.6349 \| 5800 \| 30.51 \| 50.38 \| 1.2479 \| 63.3498 \|
	\| 0.2207 \| 0.6459 \| 5900 \| 32.68 \| 51.97 \| 1.2641 \| 62.1342 \|
	\| 0.2017 \| 0.6568 \| 6000 \| 32.47 \| 51.36 \| 1.2640 \| 62.6745 \|
	\| 0.201 \| 0.6678 \| 6100 \| 33.6 \| 52.29 \| 1.2774 \| 61.4588 \|
	\| 0.203 \| 0.6787 \| 6200 \| 30.27 \| 50.84 \| 1.2670 \| 65.6461 \|
	\| 0.1456 \| 0.6897 \| 6300 \| 31.2 \| 51.05 \| 1.2656 \| 63.3048 \|
	\| 0.1607 \| 0.7006 \| 6400 \| 30.39 \| 51.04 \| 1.2611 \| 65.8262 \|
	\| 0.1933 \| 0.7115 \| 6500 \| 31.78 \| 50.92 \| 1.2545 \| 63.0797 \|
	\| 0.1537 \| 0.7225 \| 6600 \| 30.18 \| 50.18 \| 1.2500 \| 64.7006 \|
	\| 0.1279 \| 0.7334 \| 6700 \| 33.23 \| 51.0 \| 1.2548 \| 59.8379 \|
	\| 0.1189 \| 0.7444 \| 6800 \| 33.51 \| 50.67 \| 1.2594 \| 61.1887 \|
	\| 0.1056 \| 0.7553 \| 6900 \| 32.97 \| 51.02 \| 1.2578 \| 61.9991 \|
	\| 0.1105 \| 0.7663 \| 7000 \| 32.74 \| 50.83 \| 1.2569 \| 62.0441 \|
	\| 0.1183 \| 0.7772 \| 7100 \| 34.07 \| 52.2 \| 1.2590 \| 60.4232 \|
	\| 0.1373 \| 0.7882 \| 7200 \| 33.55 \| 50.6 \| 1.2430 \| 61.2787 \|
	\| 0.1325 \| 0.7991 \| 7300 \| 32.36 \| 50.39 \| 1.2548 \| 62.3143 \|
	\| 0.0907 \| 0.8101 \| 7400 \| 32.28 \| 50.99 \| 1.2578 \| 61.2787 \|
	\| 0.0919 \| 0.8210 \| 7500 \| 33.01 \| 51.81 \| 1.2791 \| 60.4683 \|
	\| 0.0852 \| 0.8320 \| 7600 \| 32.97 \| 51.56 \| 1.2782 \| 61.5489 \|
	\| 0.1223 \| 0.8429 \| 7700 \| 33.57 \| 52.33 \| 1.2638 \| 59.9280 \|
	\| 0.0826 \| 0.8539 \| 7800 \| 33.83 \| 52.7 \| 1.2634 \| 60.1531 \|
	\| 0.0783 \| 0.8648 \| 7900 \| 33.79 \| 52.31 \| 1.2595 \| 60.1081 \|
	\| 0.0986 \| 0.8758 \| 8000 \| 34.33 \| 52.54 \| 1.2608 \| 59.4327 \|
	\| 0.1148 \| 0.8867 \| 8100 \| 1.2736\| 34.03 \| 52.52 \| 59.8829 \|
	\| 0.1134 \| 0.8976 \| 8200 \| 1.3073\| 34.14 \| 51.64 \| 61.5038 \|
	\| 0.1166 \| 0.9086 \| 8300 \| 1.3385\| 30.51 \| 49.26 \| 65.5561 \|
	\| 0.0871 \| 0.9195 \| 8400 \| 1.3313\| 32.31 \| 51.06 \| 62.5394 \|
	\| 0.0927 \| 0.9305 \| 8500 \| 1.3898\| 28.64 \| 48.43 \| 69.3832 \|
	\| 0.1012 \| 0.9414 \| 8600 \| 1.3144\| 33.12 \| 52.02 \| 61.4138 \|
	\| 0.0742 \| 0.9524 \| 8700 \| 1.3284\| 33.68 \| 51.38 \| 61.7740 \|
	\| 0.0802 \| 0.9633 \| 8800 \| 1.3300\| 34.33 \| 51.38 \| 61.4138 \|
	\| 0.0799 \| 0.9743 \| 8900 \| 1.3328\| 33.72 \| 50.77 \| 60.1981 \|
	\| 0.0936 \| 0.9852 \| 9000 \| 1.3181\| 34.76 \| 51.4 \| 60.0630 \|
	\| 0.1091 \| 0.9962 \| 9100 \| 1.3096\| 35.13 \| 52.6 \| 59.9730 \|
	\| 0.0427 \| 1.0071 \| 9200 \| 1.2905\| 35.49 \| 53.12 \| 59.8379 \|
	\| 0.0338 \| 1.0181 \| 9300 \| 1.3097\| 35.33 \| 52.62 \| 60.5133 \|
	\| 0.0363 \| 1.0290 \| 9400 \| 1.3172\| 35.51 \| 53.06 \| 59.6128 \|
	\| 0.0319 \| 1.0400 \| 9500 \| 1.3166\| 36.82 \| 53.6 \| 58.3971 \|
	\| 0.0434 \| 1.0509 \| 9600 \| 1.3050\| 35.62 \| 53.28 \| 59.6578 \|
	\| 0.0218 \| 1.0619 \| 9700 \| 1.3096\| 35.57 \| 53.28 \| 59.5227 \|
	\| 0.0316 \| 1.0728 \| 9800 \| 1.3162\| 36.14 \| 53.87 \| 58.3971 \|
	\| 0.0315 \| 1.0837 \| 9900 \| 1.3121\| 36.26 \| 54.16 \| 58.3521 \|
	\| 0.0229 \| 1.0947 \| 10000 \| 1.3134\| 36.12 \| 53.74 \| 58.3071 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.2.0+cu121
	- Datasets 2.19.2
	- Tokenizers 0.19.1