whisper-medium-ga2en-v6.3.1-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: 30.86
	- name: Wer
	type: wer
	value: 67.04187303016658
	---

	<!-- 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: 1.0885
	- Bleu: 30.86
	- Chrf: 54.11
	- Wer: 67.0419

	## 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: 8000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.5374 \| 0.0138 \| 100 \| 2.56 \| 18.92 \| 2.1201 \| 222.4674 \|
	\| 2.446 \| 0.0276 \| 200 \| 3.07 \| 20.56 \| 2.1960 \| 170.5088 \|
	\| 2.2819 \| 0.0414 \| 300 \| 5.87 \| 25.17 \| 1.9811 \| 114.5880 \|
	\| 2.1904 \| 0.0552 \| 400 \| 8.41 \| 25.65 \| 1.9974 \| 99.1896 \|
	\| 2.026 \| 0.0690 \| 500 \| 7.99 \| 27.64 \| 1.8961 \| 130.7069 \|
	\| 2.0448 \| 0.0828 \| 600 \| 9.15 \| 27.78 \| 1.9410 \| 104.9077 \|
	\| 1.8606 \| 0.0966 \| 700 \| 9.57 \| 29.34 \| 1.8451 \| 110.4908 \|
	\| 1.9887 \| 0.1103 \| 800 \| 13.44 \| 32.32 \| 1.7419 \| 84.3314 \|
	\| 1.8633 \| 0.1241 \| 900 \| 13.43 \| 31.58 \| 1.7376 \| 102.1162 \|
	\| 1.7576 \| 0.1379 \| 1000 \| 11.9 \| 32.68 \| 1.6879 \| 106.6186 \|
	\| 1.7142 \| 0.1517 \| 1100 \| 12.4 \| 33.66 \| 1.7571 \| 102.6114 \|
	\| 1.7168 \| 0.1655 \| 1200 \| 17.35 \| 36.55 \| 1.6003 \| 87.9784 \|
	\| 1.6741 \| 0.1793 \| 1300 \| 15.41 \| 35.46 \| 1.5883 \| 92.8411 \|
	\| 1.6534 \| 0.1931 \| 1400 \| 17.12 \| 37.24 \| 1.5366 \| 90.2296 \|
	\| 1.58 \| 0.2069 \| 1500 \| 17.49 \| 38.5 \| 1.5141 \| 92.1207 \|
	\| 1.403 \| 0.2207 \| 1600 \| 16.78 \| 39.13 \| 1.4606 \| 88.9689 \|
	\| 1.3806 \| 0.2345 \| 1700 \| 19.26 \| 40.02 \| 1.4263 \| 86.7177 \|
	\| 1.5111 \| 0.2483 \| 1800 \| 18.4 \| 39.47 \| 1.4060 \| 92.2557 \|
	\| 1.4261 \| 0.2621 \| 1900 \| 21.19 \| 42.13 \| 1.3911 \| 78.7033 \|
	\| 1.2974 \| 0.2759 \| 2000 \| 15.6 \| 38.66 \| 1.3871 \| 100.3152 \|
	\| 1.2694 \| 0.2897 \| 2100 \| 16.21 \| 39.99 \| 1.3527 \| 91.2652 \|
	\| 1.204 \| 0.3034 \| 2200 \| 20.2 \| 41.18 \| 1.3232 \| 86.8978 \|
	\| 1.1922 \| 0.3172 \| 2300 \| 16.44 \| 40.85 \| 1.3338 \| 103.1968 \|
	\| 1.1237 \| 0.3310 \| 2400 \| 19.29 \| 43.73 \| 1.2830 \| 94.4620 \|
	\| 1.0989 \| 0.3448 \| 2500 \| 25.11 \| 46.84 \| 1.2844 \| 75.0563 \|
	\| 1.0766 \| 0.3586 \| 2600 \| 23.87 \| 46.1 \| 1.2578 \| 74.5160 \|
	\| 1.0432 \| 0.3724 \| 2700 \| 22.31 \| 44.91 \| 1.2414 \| 86.9878 \|
	\| 1.1588 \| 0.3862 \| 2800 \| 23.32 \| 45.94 \| 1.2051 \| 77.1724 \|
	\| 1.0062 \| 0.4 \| 2900 \| 26.15 \| 48.27 \| 1.2059 \| 69.4282 \|
	\| 0.9178 \| 0.4138 \| 3000 \| 29.13 \| 48.92 \| 1.1756 \| 64.7456 \|
	\| 0.9108 \| 0.4276 \| 3100 \| 28.34 \| 48.9 \| 1.1665 \| 67.2220 \|
	\| 0.9868 \| 0.4414 \| 3200 \| 25.64 \| 48.93 \| 1.1489 \| 75.3264 \|
	\| 0.9563 \| 0.4552 \| 3300 \| 27.58 \| 49.67 \| 1.1181 \| 71.8145 \|
	\| 0.9138 \| 0.4690 \| 3400 \| 28.37 \| 50.96 \| 1.1247 \| 71.4543 \|
	\| 0.8508 \| 0.4828 \| 3500 \| 29.75 \| 51.41 \| 1.1007 \| 68.3476 \|
	\| 0.836 \| 0.4966 \| 3600 \| 30.99 \| 52.2 \| 1.1114 \| 66.5916 \|
	\| 0.8435 \| 0.5103 \| 3700 \| 30.64 \| 52.77 \| 1.0782 \| 68.2125 \|
	\| 0.8323 \| 0.5241 \| 3800 \| 29.78 \| 52.94 \| 1.0744 \| 68.9779 \|
	\| 0.818 \| 0.5379 \| 3900 \| 31.23 \| 53.21 \| 1.0639 \| 67.7623 \|
	\| 0.8095 \| 0.5517 \| 4000 \| 31.02 \| 53.51 \| 1.0576 \| 68.5277 \|
	\| 0.922 \| 0.5655 \| 4100 \| 1.2445\| 25.47 \| 46.16 \| 74.2909 \|
	\| 1.0387 \| 0.5793 \| 4200 \| 1.2634\| 25.44 \| 46.19 \| 71.0491 \|
	\| 0.9386 \| 0.5931 \| 4300 \| 1.2457\| 22.36 \| 45.4 \| 76.8122 \|
	\| 0.9297 \| 0.6069 \| 4400 \| 1.2502\| 28.65 \| 46.48 \| 65.7362 \|
	\| 0.9837 \| 0.6207 \| 4500 \| 1.2503\| 26.81 \| 46.53 \| 68.9779 \|
	\| 1.0226 \| 0.6345 \| 4600 \| 1.2282\| 19.37 \| 44.1 \| 86.4926 \|
	\| 0.9896 \| 0.6483 \| 4700 \| 1.2568\| 26.06 \| 46.46 \| 70.8240 \|
	\| 0.9805 \| 0.6621 \| 4800 \| 1.2364\| 19.29 \| 42.56 \| 82.0351 \|
	\| 0.8982 \| 0.6759 \| 4900 \| 1.2346\| 28.58 \| 47.84 \| 64.6556 \|
	\| 0.8303 \| 0.6897 \| 5000 \| 1.2136\| 27.25 \| 48.15 \| 68.3476 \|
	\| 0.905 \| 0.7034 \| 5100 \| 1.1808\| 27.99 \| 50.31 \| 67.2220 \|
	\| 0.8125 \| 0.7172 \| 5200 \| 1.1971\| 28.91 \| 47.63 \| 65.4660 \|
	\| 0.7965 \| 0.7310 \| 5300 \| 1.1789\| 25.96 \| 47.21 \| 69.5633 \|
	\| 0.8244 \| 0.7448 \| 5400 \| 1.2237\| 28.65 \| 48.63 \| 66.6367 \|
	\| 0.7637 \| 0.7586 \| 5500 \| 1.1765\| 30.4 \| 50.24 \| 66.6817 \|
	\| 0.7333 \| 0.7724 \| 5600 \| 1.1295\| 29.94 \| 51.34 \| 68.8879 \|
	\| 0.8141 \| 0.7862 \| 5700 \| 1.1238\| 27.51 \| 50.61 \| 74.7861 \|
	\| 0.6969 \| 0.8 \| 5800 \| 1.1350\| 23.95 \| 48.76 \| 87.6632 \|
	\| 0.7162 \| 0.8138 \| 5900 \| 1.1493\| 26.34 \| 48.65 \| 74.0207 \|
	\| 0.7421 \| 0.8276 \| 6000 \| 1.0976\| 28.69 \| 52.23 \| 68.5727 \|
	\| 0.593 \| 0.8414 \| 6100 \| 1.1163\| 34.96 \| 53.13 \| 59.3426 \|
	\| 0.678 \| 0.8552 \| 6200 \| 1.1072\| 34.14 \| 53.2 \| 61.6839 \|
	\| 0.6018 \| 0.8690 \| 6300 \| 1.0959\| 31.8 \| 53.33 \| 64.1153 \|
	\| 0.6038 \| 0.8828 \| 6400 \| 1.0959\| 24.77 \| 50.61 \| 84.2413 \|
	\| 0.6174 \| 0.8966 \| 6500 \| 1.0891\| 25.48 \| 50.6 \| 81.6749 \|
	\| 0.595 \| 0.9103 \| 6600 \| 1.1037\| 23.83 \| 48.07 \| 83.3859 \|
	\| 0.6114 \| 0.9241 \| 6700 \| 1.0723\| 28.03 \| 52.18 \| 70.7789 \|
	\| 0.6257 \| 0.9379 \| 6800 \| 1.0797\| 33.13 \| 52.95 \| 61.5038 \|
	\| 0.6689 \| 0.9517 \| 6900 \| 1.0803\| 30.53 \| 52.41 \| 68.4376 \|
	\| 0.4908 \| 0.9655 \| 7000 \| 1.0901\| 30.1 \| 51.71 \| 69.1130 \|
	\| 0.5439 \| 0.9793 \| 7100 \| 1.0672\| 25.81 \| 49.36 \| 76.5871 \|
	\| 0.5994 \| 0.9931 \| 7200 \| 1.0705\| 31.56 \| 52.51 \| 66.1414 \|
	\| 0.2451 \| 1.0069 \| 7300 \| 1.1069\| 33.0 \| 53.29 \| 64.7006 \|
	\| 0.2609 \| 1.0207 \| 7400 \| 1.0877\| 31.68 \| 54.3 \| 64.9257 \|
	\| 0.2813 \| 1.0345 \| 7500 \| 1.0910\| 34.93 \| 54.74 \| 60.1531 \|
	\| 0.2367 \| 1.0483 \| 7600 \| 1.0999\| 30.87 \| 53.09 \| 65.9163 \|
	\| 0.2018 \| 1.0621 \| 7700 \| 1.0917\| 35.53 \| 54.42 \| 58.7573 \|
	\| 0.2407 \| 1.0759 \| 7800 \| 1.0859\| 34.38 \| 54.5 \| 60.9185 \|
	\| 0.2385 \| 1.0897 \| 7900 \| 1.0866\| 31.27 \| 54.12 \| 65.3309 \|
	\| 0.2074 \| 1.1034 \| 8000 \| 1.0885\| 30.86 \| 54.11 \| 67.0419 \|


	### 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: 30.86
	- name: Wer
	type: wer
	value: 67.04187303016658
	---

	<!-- 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: 1.0885
	- Bleu: 30.86
	- Chrf: 54.11
	- Wer: 67.0419

	## 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: 8000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Bleu \| Chrf \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:----:\|:-----:\|:-----:\|:---------------:\|:--------:\|
	\| 2.5374 \| 0.0138 \| 100 \| 2.56 \| 18.92 \| 2.1201 \| 222.4674 \|
	\| 2.446 \| 0.0276 \| 200 \| 3.07 \| 20.56 \| 2.1960 \| 170.5088 \|
	\| 2.2819 \| 0.0414 \| 300 \| 5.87 \| 25.17 \| 1.9811 \| 114.5880 \|
	\| 2.1904 \| 0.0552 \| 400 \| 8.41 \| 25.65 \| 1.9974 \| 99.1896 \|
	\| 2.026 \| 0.0690 \| 500 \| 7.99 \| 27.64 \| 1.8961 \| 130.7069 \|
	\| 2.0448 \| 0.0828 \| 600 \| 9.15 \| 27.78 \| 1.9410 \| 104.9077 \|
	\| 1.8606 \| 0.0966 \| 700 \| 9.57 \| 29.34 \| 1.8451 \| 110.4908 \|
	\| 1.9887 \| 0.1103 \| 800 \| 13.44 \| 32.32 \| 1.7419 \| 84.3314 \|
	\| 1.8633 \| 0.1241 \| 900 \| 13.43 \| 31.58 \| 1.7376 \| 102.1162 \|
	\| 1.7576 \| 0.1379 \| 1000 \| 11.9 \| 32.68 \| 1.6879 \| 106.6186 \|
	\| 1.7142 \| 0.1517 \| 1100 \| 12.4 \| 33.66 \| 1.7571 \| 102.6114 \|
	\| 1.7168 \| 0.1655 \| 1200 \| 17.35 \| 36.55 \| 1.6003 \| 87.9784 \|
	\| 1.6741 \| 0.1793 \| 1300 \| 15.41 \| 35.46 \| 1.5883 \| 92.8411 \|
	\| 1.6534 \| 0.1931 \| 1400 \| 17.12 \| 37.24 \| 1.5366 \| 90.2296 \|
	\| 1.58 \| 0.2069 \| 1500 \| 17.49 \| 38.5 \| 1.5141 \| 92.1207 \|
	\| 1.403 \| 0.2207 \| 1600 \| 16.78 \| 39.13 \| 1.4606 \| 88.9689 \|
	\| 1.3806 \| 0.2345 \| 1700 \| 19.26 \| 40.02 \| 1.4263 \| 86.7177 \|
	\| 1.5111 \| 0.2483 \| 1800 \| 18.4 \| 39.47 \| 1.4060 \| 92.2557 \|
	\| 1.4261 \| 0.2621 \| 1900 \| 21.19 \| 42.13 \| 1.3911 \| 78.7033 \|
	\| 1.2974 \| 0.2759 \| 2000 \| 15.6 \| 38.66 \| 1.3871 \| 100.3152 \|
	\| 1.2694 \| 0.2897 \| 2100 \| 16.21 \| 39.99 \| 1.3527 \| 91.2652 \|
	\| 1.204 \| 0.3034 \| 2200 \| 20.2 \| 41.18 \| 1.3232 \| 86.8978 \|
	\| 1.1922 \| 0.3172 \| 2300 \| 16.44 \| 40.85 \| 1.3338 \| 103.1968 \|
	\| 1.1237 \| 0.3310 \| 2400 \| 19.29 \| 43.73 \| 1.2830 \| 94.4620 \|
	\| 1.0989 \| 0.3448 \| 2500 \| 25.11 \| 46.84 \| 1.2844 \| 75.0563 \|
	\| 1.0766 \| 0.3586 \| 2600 \| 23.87 \| 46.1 \| 1.2578 \| 74.5160 \|
	\| 1.0432 \| 0.3724 \| 2700 \| 22.31 \| 44.91 \| 1.2414 \| 86.9878 \|
	\| 1.1588 \| 0.3862 \| 2800 \| 23.32 \| 45.94 \| 1.2051 \| 77.1724 \|
	\| 1.0062 \| 0.4 \| 2900 \| 26.15 \| 48.27 \| 1.2059 \| 69.4282 \|
	\| 0.9178 \| 0.4138 \| 3000 \| 29.13 \| 48.92 \| 1.1756 \| 64.7456 \|
	\| 0.9108 \| 0.4276 \| 3100 \| 28.34 \| 48.9 \| 1.1665 \| 67.2220 \|
	\| 0.9868 \| 0.4414 \| 3200 \| 25.64 \| 48.93 \| 1.1489 \| 75.3264 \|
	\| 0.9563 \| 0.4552 \| 3300 \| 27.58 \| 49.67 \| 1.1181 \| 71.8145 \|
	\| 0.9138 \| 0.4690 \| 3400 \| 28.37 \| 50.96 \| 1.1247 \| 71.4543 \|
	\| 0.8508 \| 0.4828 \| 3500 \| 29.75 \| 51.41 \| 1.1007 \| 68.3476 \|
	\| 0.836 \| 0.4966 \| 3600 \| 30.99 \| 52.2 \| 1.1114 \| 66.5916 \|
	\| 0.8435 \| 0.5103 \| 3700 \| 30.64 \| 52.77 \| 1.0782 \| 68.2125 \|
	\| 0.8323 \| 0.5241 \| 3800 \| 29.78 \| 52.94 \| 1.0744 \| 68.9779 \|
	\| 0.818 \| 0.5379 \| 3900 \| 31.23 \| 53.21 \| 1.0639 \| 67.7623 \|
	\| 0.8095 \| 0.5517 \| 4000 \| 31.02 \| 53.51 \| 1.0576 \| 68.5277 \|
	\| 0.922 \| 0.5655 \| 4100 \| 1.2445\| 25.47 \| 46.16 \| 74.2909 \|
	\| 1.0387 \| 0.5793 \| 4200 \| 1.2634\| 25.44 \| 46.19 \| 71.0491 \|
	\| 0.9386 \| 0.5931 \| 4300 \| 1.2457\| 22.36 \| 45.4 \| 76.8122 \|
	\| 0.9297 \| 0.6069 \| 4400 \| 1.2502\| 28.65 \| 46.48 \| 65.7362 \|
	\| 0.9837 \| 0.6207 \| 4500 \| 1.2503\| 26.81 \| 46.53 \| 68.9779 \|
	\| 1.0226 \| 0.6345 \| 4600 \| 1.2282\| 19.37 \| 44.1 \| 86.4926 \|
	\| 0.9896 \| 0.6483 \| 4700 \| 1.2568\| 26.06 \| 46.46 \| 70.8240 \|
	\| 0.9805 \| 0.6621 \| 4800 \| 1.2364\| 19.29 \| 42.56 \| 82.0351 \|
	\| 0.8982 \| 0.6759 \| 4900 \| 1.2346\| 28.58 \| 47.84 \| 64.6556 \|
	\| 0.8303 \| 0.6897 \| 5000 \| 1.2136\| 27.25 \| 48.15 \| 68.3476 \|
	\| 0.905 \| 0.7034 \| 5100 \| 1.1808\| 27.99 \| 50.31 \| 67.2220 \|
	\| 0.8125 \| 0.7172 \| 5200 \| 1.1971\| 28.91 \| 47.63 \| 65.4660 \|
	\| 0.7965 \| 0.7310 \| 5300 \| 1.1789\| 25.96 \| 47.21 \| 69.5633 \|
	\| 0.8244 \| 0.7448 \| 5400 \| 1.2237\| 28.65 \| 48.63 \| 66.6367 \|
	\| 0.7637 \| 0.7586 \| 5500 \| 1.1765\| 30.4 \| 50.24 \| 66.6817 \|
	\| 0.7333 \| 0.7724 \| 5600 \| 1.1295\| 29.94 \| 51.34 \| 68.8879 \|
	\| 0.8141 \| 0.7862 \| 5700 \| 1.1238\| 27.51 \| 50.61 \| 74.7861 \|
	\| 0.6969 \| 0.8 \| 5800 \| 1.1350\| 23.95 \| 48.76 \| 87.6632 \|
	\| 0.7162 \| 0.8138 \| 5900 \| 1.1493\| 26.34 \| 48.65 \| 74.0207 \|
	\| 0.7421 \| 0.8276 \| 6000 \| 1.0976\| 28.69 \| 52.23 \| 68.5727 \|
	\| 0.593 \| 0.8414 \| 6100 \| 1.1163\| 34.96 \| 53.13 \| 59.3426 \|
	\| 0.678 \| 0.8552 \| 6200 \| 1.1072\| 34.14 \| 53.2 \| 61.6839 \|
	\| 0.6018 \| 0.8690 \| 6300 \| 1.0959\| 31.8 \| 53.33 \| 64.1153 \|
	\| 0.6038 \| 0.8828 \| 6400 \| 1.0959\| 24.77 \| 50.61 \| 84.2413 \|
	\| 0.6174 \| 0.8966 \| 6500 \| 1.0891\| 25.48 \| 50.6 \| 81.6749 \|
	\| 0.595 \| 0.9103 \| 6600 \| 1.1037\| 23.83 \| 48.07 \| 83.3859 \|
	\| 0.6114 \| 0.9241 \| 6700 \| 1.0723\| 28.03 \| 52.18 \| 70.7789 \|
	\| 0.6257 \| 0.9379 \| 6800 \| 1.0797\| 33.13 \| 52.95 \| 61.5038 \|
	\| 0.6689 \| 0.9517 \| 6900 \| 1.0803\| 30.53 \| 52.41 \| 68.4376 \|
	\| 0.4908 \| 0.9655 \| 7000 \| 1.0901\| 30.1 \| 51.71 \| 69.1130 \|
	\| 0.5439 \| 0.9793 \| 7100 \| 1.0672\| 25.81 \| 49.36 \| 76.5871 \|
	\| 0.5994 \| 0.9931 \| 7200 \| 1.0705\| 31.56 \| 52.51 \| 66.1414 \|
	\| 0.2451 \| 1.0069 \| 7300 \| 1.1069\| 33.0 \| 53.29 \| 64.7006 \|
	\| 0.2609 \| 1.0207 \| 7400 \| 1.0877\| 31.68 \| 54.3 \| 64.9257 \|
	\| 0.2813 \| 1.0345 \| 7500 \| 1.0910\| 34.93 \| 54.74 \| 60.1531 \|
	\| 0.2367 \| 1.0483 \| 7600 \| 1.0999\| 30.87 \| 53.09 \| 65.9163 \|
	\| 0.2018 \| 1.0621 \| 7700 \| 1.0917\| 35.53 \| 54.42 \| 58.7573 \|
	\| 0.2407 \| 1.0759 \| 7800 \| 1.0859\| 34.38 \| 54.5 \| 60.9185 \|
	\| 0.2385 \| 1.0897 \| 7900 \| 1.0866\| 31.27 \| 54.12 \| 65.3309 \|
	\| 0.2074 \| 1.1034 \| 8000 \| 1.0885\| 30.86 \| 54.11 \| 67.0419 \|


	### Framework versions

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