whisper-medium-ga2en-v6.2.2-r / README.md

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	---
	language:
	- ga
	- en
	license: apache-2.0
	base_model: openai/whisper-small
	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/EUbookshop-Speech-Irish
	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, Wikimedia, and EUbookshop
	type: ymoslem/IWSLT2023-GA-EN
	metrics:
	- name: Bleu
	type: bleu
	value: 36.46
	- name: Wer
	type: wer
	value: 58.26204412426835
	---

	<!-- 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-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1121
	- Bleu: 36.46
	- Chrf: 55.74
	- Wer: 58.2620

	## 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 \|
	\|:-------------:\|:------:\|:-----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.6534 \| 0.0138 \| 100 \| 1.43 \| 15.99 \| 2.2446 \| 269.1130 \|
	\| 2.4519 \| 0.0276 \| 200 \| 2.13 \| 18.36 \| 2.1941 \| 250.5178 \|
	\| 2.2928 \| 0.0414 \| 300 \| 7.14 \| 25.95 \| 2.0086 \| 128.3656 \|
	\| 2.233 \| 0.0552 \| 400 \| 5.61 \| 24.25 \| 2.0239 \| 134.0837 \|
	\| 2.0406 \| 0.0690 \| 500 \| 5.64 \| 25.65 \| 1.9215 \| 183.8361 \|
	\| 2.0273 \| 0.0828 \| 600 \| 13.41 \| 30.96 \| 1.8556 \| 83.7010 \|
	\| 1.895 \| 0.0966 \| 700 \| 7.02 \| 26.82 \| 1.8278 \| 158.2170 \|
	\| 1.9889 \| 0.1103 \| 800 \| 12.22 \| 31.62 \| 1.7842 \| 99.6398 \|
	\| 1.8484 \| 0.1241 \| 900 \| 10.97 \| 30.45 \| 1.7648 \| 91.1751 \|
	\| 1.7491 \| 0.1379 \| 1000 \| 10.0 \| 29.42 \| 1.7498 \| 109.0050 \|
	\| 1.699 \| 0.1517 \| 1100 \| 12.53 \| 34.87 \| 1.6662 \| 109.9054 \|
	\| 1.6959 \| 0.1655 \| 1200 \| 14.54 \| 34.8 \| 1.6287 \| 92.3008 \|
	\| 1.6682 \| 0.1793 \| 1300 \| 13.26 \| 33.5 \| 1.5800 \| 103.0617 \|
	\| 1.6625 \| 0.1931 \| 1400 \| 19.71 \| 37.33 \| 1.6115 \| 75.9118 \|
	\| 1.5462 \| 0.2069 \| 1500 \| 18.3 \| 39.49 \| 1.4993 \| 93.7866 \|
	\| 1.3834 \| 0.2207 \| 1600 \| 20.32 \| 40.87 \| 1.4906 \| 79.2436 \|
	\| 1.39 \| 0.2345 \| 1700 \| 17.3 \| 38.16 \| 1.4752 \| 93.1562 \|
	\| 1.5061 \| 0.2483 \| 1800 \| 20.11 \| 39.69 \| 1.4004 \| 81.0446 \|
	\| 1.4125 \| 0.2621 \| 1900 \| 23.82 \| 42.67 \| 1.3854 \| 73.3904 \|
	\| 1.3181 \| 0.2759 \| 2000 \| 20.57 \| 40.87 \| 1.3979 \| 78.8384 \|
	\| 1.283 \| 0.2897 \| 2100 \| 17.97 \| 40.47 \| 1.3446 \| 88.8789 \|
	\| 1.2061 \| 0.3034 \| 2200 \| 25.12 \| 45.42 \| 1.3130 \| 73.5254 \|
	\| 1.2091 \| 0.3172 \| 2300 \| 22.12 \| 43.56 \| 1.3274 \| 79.8739 \|
	\| 1.1264 \| 0.3310 \| 2400 \| 22.94 \| 45.96 \| 1.2771 \| 78.2080 \|
	\| 1.0972 \| 0.3448 \| 2500 \| 24.38 \| 46.04 \| 1.2858 \| 75.4615 \|
	\| 1.0822 \| 0.3586 \| 2600 \| 27.39 \| 48.34 \| 1.2376 \| 67.6722 \|
	\| 1.0316 \| 0.3724 \| 2700 \| 28.0 \| 47.61 \| 1.2461 \| 68.5277 \|
	\| 1.165 \| 0.3862 \| 2800 \| 26.05 \| 48.13 \| 1.1869 \| 71.6794 \|
	\| 1.025 \| 0.4 \| 2900 \| 27.14 \| 47.91 \| 1.1716 \| 68.7528 \|
	\| 0.8978 \| 0.4138 \| 3000 \| 28.34 \| 49.15 \| 1.1628 \| 65.6461 \|
	\| 0.9146 \| 0.4276 \| 3100 \| 25.81 \| 48.42 \| 1.1703 \| 71.7244 \|
	\| 0.9764 \| 0.4414 \| 3200 \| 29.63 \| 51.22 \| 1.1526 \| 67.3570 \|
	\| 0.9455 \| 0.4552 \| 3300 \| 25.31 \| 49.73 \| 1.1108 \| 72.6249 \|
	\| 0.9073 \| 0.4690 \| 3400 \| 27.7 \| 50.85 \| 1.1085 \| 72.7150 \|
	\| 0.8596 \| 0.4828 \| 3500 \| 28.34 \| 52.39 \| 1.0927 \| 67.9424 \|
	\| 0.8241 \| 0.4966 \| 3600 \| 29.95 \| 51.37 \| 1.1026 \| 65.2859 \|
	\| 0.8436 \| 0.5103 \| 3700 \| 27.18 \| 51.45 \| 1.0718 \| 71.2292 \|
	\| 0.8318 \| 0.5241 \| 3800 \| 30.71 \| 53.35 \| 1.0678 \| 64.3404 \|
	\| 0.8262 \| 0.5379 \| 3900 \| 27.05 \| 51.94 \| 1.0534 \| 71.5894 \|
	\| 0.8129 \| 0.5517 \| 4000 \| 27.38 \| 51.97 \| 1.0491 \| 72.1747 \|
	\| 0.9036 \| 0.5655 \| 4100 \| 14.43 \| 40.57 \| 1.2250 \| 139.3066 \|
	\| 1.0314 \| 0.5793 \| 4200 \| 24.27 \| 46.97 \| 1.2310 \| 75.5966 \|
	\| 0.9209 \| 0.5931 \| 4300 \| 23.55 \| 46.04 \| 1.2447 \| 76.4070 \|
	\| 0.9204 \| 0.6069 \| 4400 \| 25.87 \| 45.32 \| 1.2891 \| 73.0302 \|
	\| 0.9843 \| 0.6207 \| 4500 \| 27.2 \| 46.36 \| 1.2269 \| 71.8145 \|
	\| 1.0225 \| 0.6345 \| 4600 \| 26.16 \| 45.72 \| 1.2403 \| 69.6983 \|
	\| 0.9773 \| 0.6483 \| 4700 \| 26.37 \| 45.62 \| 1.2464 \| 68.4376 \|
	\| 0.9794 \| 0.6621 \| 4800 \| 24.77 \| 47.11 \| 1.2461 \| 72.0846 \|
	\| 0.8905 \| 0.6759 \| 4900 \| 24.58 \| 46.35 \| 1.2345 \| 71.2742 \|
	\| 0.8305 \| 0.6897 \| 5000 \| 27.28 \| 48.37 \| 1.2239 \| 68.1675 \|
	\| 0.9019 \| 0.7034 \| 5100 \| 27.04 \| 50.28 \| 1.1730 \| 70.1486 \|
	\| 0.7969 \| 0.7172 \| 5200 \| 26.27 \| 48.07 \| 1.1807 \| 69.0230 \|
	\| 0.8036 \| 0.7310 \| 5300 \| 23.04 \| 48.3 \| 1.1632 \| 77.5326 \|
	\| 0.8195 \| 0.7448 \| 5400 \| 25.58 \| 50.29 \| 1.1811 \| 76.2269 \|
	\| 0.7697 \| 0.7586 \| 5500 \| 23.99 \| 48.91 \| 1.1825 \| 81.4948 \|
	\| 0.727 \| 0.7724 \| 5600 \| 23.93 \| 49.23 \| 1.1623 \| 79.5137 \|
	\| 0.8002 \| 0.7862 \| 5700 \| 26.29 \| 50.44 \| 1.1503 \| 75.6866 \|
	\| 0.6909 \| 0.8 \| 5800 \| 29.27 \| 50.85 \| 1.1338 \| 64.0252 \|
	\| 0.7146 \| 0.8138 \| 5900 \| 28.24 \| 50.82 \| 1.1420 \| 66.6367 \|
	\| 0.7452 \| 0.8276 \| 6000 \| 31.33 \| 51.92 \| 1.1328 \| 62.4944 \|
	\| 0.5989 \| 0.8414 \| 6100 \| 31.1 \| 52.15 \| 1.1455 \| 65.1959 \|
	\| 0.6818 \| 0.8552 \| 6200 \| 32.56 \| 52.46 \| 1.1112 \| 62.1342 \|
	\| 0.6074 \| 0.8690 \| 6300 \| 33.48 \| 53.32 \| 1.1072 \| 60.6033 \|
	\| 0.5942 \| 0.8828 \| 6400 \| 31.39 \| 51.03 \| 1.1462 \| 62.8546 \|
	\| 0.6341 \| 0.8966 \| 6500 \| 31.55 \| 52.15 \| 1.1093 \| 62.4043 \|
	\| 0.5992 \| 0.9103 \| 6600 \| 33.06 \| 52.52 \| 1.1215 \| 61.4588 \|
	\| 0.6156 \| 0.9241 \| 6700 \| 32.38 \| 52.76 \| 1.1031 \| 62.9446 \|
	\| 0.6169 \| 0.9379 \| 6800 \| 31.46 \| 52.96 \| 1.1082 \| 64.3404 \|
	\| 0.6543 \| 0.9517 \| 6900 \| 33.49 \| 54.02 \| 1.0943 \| 63.1247 \|
	\| 0.5017 \| 0.9655 \| 7000 \| 30.95 \| 52.64 \| 1.1141 \| 68.6177 \|
	\| 0.5583 \| 0.9793 \| 7100 \| 34.39 \| 54.03 \| 1.1004 \| 61.6839 \|
	\| 0.5986 \| 0.9931 \| 7200 \| 33.92 \| 52.85 \| 1.1055 \| 62.4944 \|
	\| 0.2443 \| 1.0069 \| 7300 \| 34.86 \| 53.01 \| 1.1442 \| 60.1981 \|
	\| 0.254 \| 1.0207 \| 7400 \| 33.92 \| 53.25 \| 1.1458 \| 62.1792 \|
	\| 0.2827 \| 1.0345 \| 7500 \| 34.49 \| 53.43 \| 1.1190 \| 60.6484 \|
	\| 0.2326 \| 1.0483 \| 7600 \| 35.47 \| 53.53 \| 1.1237 \| 59.2076 \|
	\| 0.2017 \| 1.0621 \| 7700 \| 34.65 \| 53.87 \| 1.1179 \| 60.0180 \|
	\| 0.2367 \| 1.0759 \| 7800 \| 34.23 \| 53.67 \| 1.1075 \| 60.6484 \|
	\| 0.2276 \| 1.0897 \| 7900 \| 34.67 \| 54.51 \| 1.1063 \| 60.3332 \|
	\| 0.2087 \| 1.1034 \| 8000 \| 34.44 \| 54.07 \| 1.1090 \| 60.6484 \|
	\| 0.2514 \| 1.1172 \| 8100 \| 1.1199\| 29.85 \| 51.91 \| 69.6083 \|
	\| 0.2692 \| 1.1310 \| 8200 \| 1.1642\| 28.05 \| 51.94 \| 72.1747 \|
	\| 0.2784 \| 1.1448 \| 8300 \| 1.1262\| 27.26 \| 50.77 \| 74.8312 \|
	\| 0.2539 \| 1.1586 \| 8400 \| 1.1463\| 30.7 \| 53.1 \| 65.0158 \|
	\| 0.2599 \| 1.1724 \| 8500 \| 1.1255\| 31.64 \| 53.71 \| 63.2148 \|
	\| 0.2419 \| 1.1862 \| 8600 \| 1.1223\| 33.2 \| 54.15 \| 62.4043 \|
	\| 0.2583 \| 1.2 \| 8700 \| 1.1304\| 33.98 \| 53.65 \| 61.2787 \|
	\| 0.239 \| 1.2138 \| 8800 \| 1.1371\| 34.68 \| 54.35 \| 61.7740 \|
	\| 0.2198 \| 1.2276 \| 8900 \| 1.1533\| 30.65 \| 52.15 \| 72.2647 \|
	\| 0.248 \| 1.2414 \| 9000 \| 1.1266\| 31.98 \| 53.68 \| 65.4210 \|
	\| 0.2377 \| 1.2552 \| 9100 \| 1.1510\| 30.9 \| 53.6 \| 67.9424 \|
	\| 0.2183 \| 1.2690 \| 9200 \| 1.1565\| 30.35 \| 53.04 \| 73.1202 \|
	\| 0.1999 \| 1.2828 \| 9300 \| 1.1426\| 29.48 \| 53.0 \| 74.2909 \|
	\| 0.22 \| 1.2966 \| 9400 \| 1.1332\| 31.93 \| 53.16 \| 66.1414 \|
	\| 0.2063 \| 1.3103 \| 9500 \| 1.1144\| 32.42 \| 53.79 \| 63.3949 \|
	\| 0.2054 \| 1.3241 \| 9600 \| 1.1146\| 33.64 \| 54.69 \| 61.5038 \|
	\| 0.2145 \| 1.3379 \| 9700 \| 1.1123\| 36.68 \| 55.64 \| 57.5867 \|
	\| 0.2059 \| 1.3517 \| 9800 \| 1.1102\| 36.93 \| 56.15 \| 57.5416 \|
	\| 0.2001 \| 1.3655 \| 9900 \| 1.1143\| 36.4 \| 56.09 \| 57.9469 \|
	\| 0.1973 \| 1.3793 \| 10000 \| 1.1121\| 36.46 \| 55.74 \| 58.2620 \|


	### 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-small
	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/EUbookshop-Speech-Irish
	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, Wikimedia, and EUbookshop
	type: ymoslem/IWSLT2023-GA-EN
	metrics:
	- name: Bleu
	type: bleu
	value: 36.46
	- name: Wer
	type: wer
	value: 58.26204412426835
	---

	<!-- 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-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1121
	- Bleu: 36.46
	- Chrf: 55.74
	- Wer: 58.2620

	## 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 \|
	\|:-------------:\|:------:\|:-----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.6534 \| 0.0138 \| 100 \| 1.43 \| 15.99 \| 2.2446 \| 269.1130 \|
	\| 2.4519 \| 0.0276 \| 200 \| 2.13 \| 18.36 \| 2.1941 \| 250.5178 \|
	\| 2.2928 \| 0.0414 \| 300 \| 7.14 \| 25.95 \| 2.0086 \| 128.3656 \|
	\| 2.233 \| 0.0552 \| 400 \| 5.61 \| 24.25 \| 2.0239 \| 134.0837 \|
	\| 2.0406 \| 0.0690 \| 500 \| 5.64 \| 25.65 \| 1.9215 \| 183.8361 \|
	\| 2.0273 \| 0.0828 \| 600 \| 13.41 \| 30.96 \| 1.8556 \| 83.7010 \|
	\| 1.895 \| 0.0966 \| 700 \| 7.02 \| 26.82 \| 1.8278 \| 158.2170 \|
	\| 1.9889 \| 0.1103 \| 800 \| 12.22 \| 31.62 \| 1.7842 \| 99.6398 \|
	\| 1.8484 \| 0.1241 \| 900 \| 10.97 \| 30.45 \| 1.7648 \| 91.1751 \|
	\| 1.7491 \| 0.1379 \| 1000 \| 10.0 \| 29.42 \| 1.7498 \| 109.0050 \|
	\| 1.699 \| 0.1517 \| 1100 \| 12.53 \| 34.87 \| 1.6662 \| 109.9054 \|
	\| 1.6959 \| 0.1655 \| 1200 \| 14.54 \| 34.8 \| 1.6287 \| 92.3008 \|
	\| 1.6682 \| 0.1793 \| 1300 \| 13.26 \| 33.5 \| 1.5800 \| 103.0617 \|
	\| 1.6625 \| 0.1931 \| 1400 \| 19.71 \| 37.33 \| 1.6115 \| 75.9118 \|
	\| 1.5462 \| 0.2069 \| 1500 \| 18.3 \| 39.49 \| 1.4993 \| 93.7866 \|
	\| 1.3834 \| 0.2207 \| 1600 \| 20.32 \| 40.87 \| 1.4906 \| 79.2436 \|
	\| 1.39 \| 0.2345 \| 1700 \| 17.3 \| 38.16 \| 1.4752 \| 93.1562 \|
	\| 1.5061 \| 0.2483 \| 1800 \| 20.11 \| 39.69 \| 1.4004 \| 81.0446 \|
	\| 1.4125 \| 0.2621 \| 1900 \| 23.82 \| 42.67 \| 1.3854 \| 73.3904 \|
	\| 1.3181 \| 0.2759 \| 2000 \| 20.57 \| 40.87 \| 1.3979 \| 78.8384 \|
	\| 1.283 \| 0.2897 \| 2100 \| 17.97 \| 40.47 \| 1.3446 \| 88.8789 \|
	\| 1.2061 \| 0.3034 \| 2200 \| 25.12 \| 45.42 \| 1.3130 \| 73.5254 \|
	\| 1.2091 \| 0.3172 \| 2300 \| 22.12 \| 43.56 \| 1.3274 \| 79.8739 \|
	\| 1.1264 \| 0.3310 \| 2400 \| 22.94 \| 45.96 \| 1.2771 \| 78.2080 \|
	\| 1.0972 \| 0.3448 \| 2500 \| 24.38 \| 46.04 \| 1.2858 \| 75.4615 \|
	\| 1.0822 \| 0.3586 \| 2600 \| 27.39 \| 48.34 \| 1.2376 \| 67.6722 \|
	\| 1.0316 \| 0.3724 \| 2700 \| 28.0 \| 47.61 \| 1.2461 \| 68.5277 \|
	\| 1.165 \| 0.3862 \| 2800 \| 26.05 \| 48.13 \| 1.1869 \| 71.6794 \|
	\| 1.025 \| 0.4 \| 2900 \| 27.14 \| 47.91 \| 1.1716 \| 68.7528 \|
	\| 0.8978 \| 0.4138 \| 3000 \| 28.34 \| 49.15 \| 1.1628 \| 65.6461 \|
	\| 0.9146 \| 0.4276 \| 3100 \| 25.81 \| 48.42 \| 1.1703 \| 71.7244 \|
	\| 0.9764 \| 0.4414 \| 3200 \| 29.63 \| 51.22 \| 1.1526 \| 67.3570 \|
	\| 0.9455 \| 0.4552 \| 3300 \| 25.31 \| 49.73 \| 1.1108 \| 72.6249 \|
	\| 0.9073 \| 0.4690 \| 3400 \| 27.7 \| 50.85 \| 1.1085 \| 72.7150 \|
	\| 0.8596 \| 0.4828 \| 3500 \| 28.34 \| 52.39 \| 1.0927 \| 67.9424 \|
	\| 0.8241 \| 0.4966 \| 3600 \| 29.95 \| 51.37 \| 1.1026 \| 65.2859 \|
	\| 0.8436 \| 0.5103 \| 3700 \| 27.18 \| 51.45 \| 1.0718 \| 71.2292 \|
	\| 0.8318 \| 0.5241 \| 3800 \| 30.71 \| 53.35 \| 1.0678 \| 64.3404 \|
	\| 0.8262 \| 0.5379 \| 3900 \| 27.05 \| 51.94 \| 1.0534 \| 71.5894 \|
	\| 0.8129 \| 0.5517 \| 4000 \| 27.38 \| 51.97 \| 1.0491 \| 72.1747 \|
	\| 0.9036 \| 0.5655 \| 4100 \| 14.43 \| 40.57 \| 1.2250 \| 139.3066 \|
	\| 1.0314 \| 0.5793 \| 4200 \| 24.27 \| 46.97 \| 1.2310 \| 75.5966 \|
	\| 0.9209 \| 0.5931 \| 4300 \| 23.55 \| 46.04 \| 1.2447 \| 76.4070 \|
	\| 0.9204 \| 0.6069 \| 4400 \| 25.87 \| 45.32 \| 1.2891 \| 73.0302 \|
	\| 0.9843 \| 0.6207 \| 4500 \| 27.2 \| 46.36 \| 1.2269 \| 71.8145 \|
	\| 1.0225 \| 0.6345 \| 4600 \| 26.16 \| 45.72 \| 1.2403 \| 69.6983 \|
	\| 0.9773 \| 0.6483 \| 4700 \| 26.37 \| 45.62 \| 1.2464 \| 68.4376 \|
	\| 0.9794 \| 0.6621 \| 4800 \| 24.77 \| 47.11 \| 1.2461 \| 72.0846 \|
	\| 0.8905 \| 0.6759 \| 4900 \| 24.58 \| 46.35 \| 1.2345 \| 71.2742 \|
	\| 0.8305 \| 0.6897 \| 5000 \| 27.28 \| 48.37 \| 1.2239 \| 68.1675 \|
	\| 0.9019 \| 0.7034 \| 5100 \| 27.04 \| 50.28 \| 1.1730 \| 70.1486 \|
	\| 0.7969 \| 0.7172 \| 5200 \| 26.27 \| 48.07 \| 1.1807 \| 69.0230 \|
	\| 0.8036 \| 0.7310 \| 5300 \| 23.04 \| 48.3 \| 1.1632 \| 77.5326 \|
	\| 0.8195 \| 0.7448 \| 5400 \| 25.58 \| 50.29 \| 1.1811 \| 76.2269 \|
	\| 0.7697 \| 0.7586 \| 5500 \| 23.99 \| 48.91 \| 1.1825 \| 81.4948 \|
	\| 0.727 \| 0.7724 \| 5600 \| 23.93 \| 49.23 \| 1.1623 \| 79.5137 \|
	\| 0.8002 \| 0.7862 \| 5700 \| 26.29 \| 50.44 \| 1.1503 \| 75.6866 \|
	\| 0.6909 \| 0.8 \| 5800 \| 29.27 \| 50.85 \| 1.1338 \| 64.0252 \|
	\| 0.7146 \| 0.8138 \| 5900 \| 28.24 \| 50.82 \| 1.1420 \| 66.6367 \|
	\| 0.7452 \| 0.8276 \| 6000 \| 31.33 \| 51.92 \| 1.1328 \| 62.4944 \|
	\| 0.5989 \| 0.8414 \| 6100 \| 31.1 \| 52.15 \| 1.1455 \| 65.1959 \|
	\| 0.6818 \| 0.8552 \| 6200 \| 32.56 \| 52.46 \| 1.1112 \| 62.1342 \|
	\| 0.6074 \| 0.8690 \| 6300 \| 33.48 \| 53.32 \| 1.1072 \| 60.6033 \|
	\| 0.5942 \| 0.8828 \| 6400 \| 31.39 \| 51.03 \| 1.1462 \| 62.8546 \|
	\| 0.6341 \| 0.8966 \| 6500 \| 31.55 \| 52.15 \| 1.1093 \| 62.4043 \|
	\| 0.5992 \| 0.9103 \| 6600 \| 33.06 \| 52.52 \| 1.1215 \| 61.4588 \|
	\| 0.6156 \| 0.9241 \| 6700 \| 32.38 \| 52.76 \| 1.1031 \| 62.9446 \|
	\| 0.6169 \| 0.9379 \| 6800 \| 31.46 \| 52.96 \| 1.1082 \| 64.3404 \|
	\| 0.6543 \| 0.9517 \| 6900 \| 33.49 \| 54.02 \| 1.0943 \| 63.1247 \|
	\| 0.5017 \| 0.9655 \| 7000 \| 30.95 \| 52.64 \| 1.1141 \| 68.6177 \|
	\| 0.5583 \| 0.9793 \| 7100 \| 34.39 \| 54.03 \| 1.1004 \| 61.6839 \|
	\| 0.5986 \| 0.9931 \| 7200 \| 33.92 \| 52.85 \| 1.1055 \| 62.4944 \|
	\| 0.2443 \| 1.0069 \| 7300 \| 34.86 \| 53.01 \| 1.1442 \| 60.1981 \|
	\| 0.254 \| 1.0207 \| 7400 \| 33.92 \| 53.25 \| 1.1458 \| 62.1792 \|
	\| 0.2827 \| 1.0345 \| 7500 \| 34.49 \| 53.43 \| 1.1190 \| 60.6484 \|
	\| 0.2326 \| 1.0483 \| 7600 \| 35.47 \| 53.53 \| 1.1237 \| 59.2076 \|
	\| 0.2017 \| 1.0621 \| 7700 \| 34.65 \| 53.87 \| 1.1179 \| 60.0180 \|
	\| 0.2367 \| 1.0759 \| 7800 \| 34.23 \| 53.67 \| 1.1075 \| 60.6484 \|
	\| 0.2276 \| 1.0897 \| 7900 \| 34.67 \| 54.51 \| 1.1063 \| 60.3332 \|
	\| 0.2087 \| 1.1034 \| 8000 \| 34.44 \| 54.07 \| 1.1090 \| 60.6484 \|
	\| 0.2514 \| 1.1172 \| 8100 \| 1.1199\| 29.85 \| 51.91 \| 69.6083 \|
	\| 0.2692 \| 1.1310 \| 8200 \| 1.1642\| 28.05 \| 51.94 \| 72.1747 \|
	\| 0.2784 \| 1.1448 \| 8300 \| 1.1262\| 27.26 \| 50.77 \| 74.8312 \|
	\| 0.2539 \| 1.1586 \| 8400 \| 1.1463\| 30.7 \| 53.1 \| 65.0158 \|
	\| 0.2599 \| 1.1724 \| 8500 \| 1.1255\| 31.64 \| 53.71 \| 63.2148 \|
	\| 0.2419 \| 1.1862 \| 8600 \| 1.1223\| 33.2 \| 54.15 \| 62.4043 \|
	\| 0.2583 \| 1.2 \| 8700 \| 1.1304\| 33.98 \| 53.65 \| 61.2787 \|
	\| 0.239 \| 1.2138 \| 8800 \| 1.1371\| 34.68 \| 54.35 \| 61.7740 \|
	\| 0.2198 \| 1.2276 \| 8900 \| 1.1533\| 30.65 \| 52.15 \| 72.2647 \|
	\| 0.248 \| 1.2414 \| 9000 \| 1.1266\| 31.98 \| 53.68 \| 65.4210 \|
	\| 0.2377 \| 1.2552 \| 9100 \| 1.1510\| 30.9 \| 53.6 \| 67.9424 \|
	\| 0.2183 \| 1.2690 \| 9200 \| 1.1565\| 30.35 \| 53.04 \| 73.1202 \|
	\| 0.1999 \| 1.2828 \| 9300 \| 1.1426\| 29.48 \| 53.0 \| 74.2909 \|
	\| 0.22 \| 1.2966 \| 9400 \| 1.1332\| 31.93 \| 53.16 \| 66.1414 \|
	\| 0.2063 \| 1.3103 \| 9500 \| 1.1144\| 32.42 \| 53.79 \| 63.3949 \|
	\| 0.2054 \| 1.3241 \| 9600 \| 1.1146\| 33.64 \| 54.69 \| 61.5038 \|
	\| 0.2145 \| 1.3379 \| 9700 \| 1.1123\| 36.68 \| 55.64 \| 57.5867 \|
	\| 0.2059 \| 1.3517 \| 9800 \| 1.1102\| 36.93 \| 56.15 \| 57.5416 \|
	\| 0.2001 \| 1.3655 \| 9900 \| 1.1143\| 36.4 \| 56.09 \| 57.9469 \|
	\| 0.1973 \| 1.3793 \| 10000 \| 1.1121\| 36.46 \| 55.74 \| 58.2620 \|


	### Framework versions

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