asr-africa/bambara_mms_20_hour_jeli_asr_dataset

707ea0d verified 4 days ago

9.92 kB

	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: bambara_mms_20_hour_jeli_asr_dataset
	results: []
	---

	<!-- 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. -->

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/wq6zc6lt)
	# bambara_mms_20_hour_jeli_asr_dataset

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.5960
	- Wer: 0.1951
	- Cer: 0.0927

	## 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: 0.0003
	- train_batch_size: 8
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 16
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 2.3781 \| 0.4365 \| 500 \| 1.7790 \| 0.9007 \| 0.4152 \|
	\| 1.5271 \| 0.8730 \| 1000 \| 1.4809 \| 0.7424 \| 0.3462 \|
	\| 1.4216 \| 1.3095 \| 1500 \| 1.4134 \| 0.8316 \| 0.4020 \|
	\| 1.3995 \| 1.7460 \| 2000 \| 1.3669 \| 0.7422 \| 0.3379 \|
	\| 1.3521 \| 2.1825 \| 2500 \| 1.2531 \| 0.6969 \| 0.3176 \|
	\| 1.3161 \| 2.6189 \| 3000 \| 1.3363 \| 0.6643 \| 0.3061 \|
	\| 1.2759 \| 3.0554 \| 3500 \| 1.2953 \| 0.6607 \| 0.3018 \|
	\| 1.2604 \| 3.4919 \| 4000 \| 1.2630 \| 0.6280 \| 0.2889 \|
	\| 1.2424 \| 3.9284 \| 4500 \| 1.4642 \| 0.6308 \| 0.2847 \|
	\| 1.1639 \| 4.3649 \| 5000 \| 1.3593 \| 0.6216 \| 0.2797 \|
	\| 1.2122 \| 4.8014 \| 5500 \| 1.2043 \| 0.5966 \| 0.2716 \|
	\| 1.1504 \| 5.2379 \| 6000 \| 1.1364 \| 0.5892 \| 0.2688 \|
	\| 1.156 \| 5.6744 \| 6500 \| 1.1405 \| 0.6049 \| 0.2726 \|
	\| 1.1371 \| 6.1109 \| 7000 \| 1.1854 \| 0.5817 \| 0.2633 \|
	\| 1.0981 \| 6.5474 \| 7500 \| 1.1306 \| 0.5876 \| 0.2625 \|
	\| 1.1021 \| 6.9838 \| 8000 \| 1.1026 \| 0.5910 \| 0.2659 \|
	\| 1.0295 \| 7.4203 \| 8500 \| 1.2506 \| 0.5671 \| 0.2600 \|
	\| 1.0702 \| 7.8568 \| 9000 \| 1.1672 \| 0.5521 \| 0.2529 \|
	\| 1.0277 \| 8.2933 \| 9500 \| 1.0966 \| 0.5616 \| 0.2544 \|
	\| 1.0023 \| 8.7298 \| 10000 \| 1.1389 \| 0.5353 \| 0.2403 \|
	\| 0.9945 \| 9.1663 \| 10500 \| 1.3434 \| 0.5302 \| 0.2420 \|
	\| 0.9533 \| 9.6028 \| 11000 \| 1.1546 \| 0.5391 \| 0.2517 \|
	\| 0.9675 \| 10.0393 \| 11500 \| 1.1966 \| 0.5355 \| 0.2451 \|
	\| 0.9061 \| 10.4758 \| 12000 \| 1.1808 \| 0.5116 \| 0.2310 \|
	\| 0.9243 \| 10.9123 \| 12500 \| 1.1189 \| 0.5095 \| 0.2290 \|
	\| 0.8834 \| 11.3488 \| 13000 \| 1.2189 \| 0.4979 \| 0.2226 \|
	\| 0.8819 \| 11.7852 \| 13500 \| 1.2035 \| 0.4910 \| 0.2158 \|
	\| 0.8522 \| 12.2217 \| 14000 \| 1.1385 \| 0.4961 \| 0.2173 \|
	\| 0.8417 \| 12.6582 \| 14500 \| 1.1060 \| 0.4787 \| 0.2110 \|
	\| 0.8352 \| 13.0947 \| 15000 \| 1.1295 \| 0.4957 \| 0.2237 \|
	\| 0.7857 \| 13.5312 \| 15500 \| 1.0946 \| 0.4814 \| 0.2142 \|
	\| 0.7963 \| 13.9677 \| 16000 \| 1.0891 \| 0.4844 \| 0.2240 \|
	\| 0.762 \| 14.4042 \| 16500 \| 1.0606 \| 0.4832 \| 0.2177 \|
	\| 0.7594 \| 14.8407 \| 17000 \| 1.0415 \| 0.4529 \| 0.1992 \|
	\| 0.7368 \| 15.2772 \| 17500 \| 1.0882 \| 0.4399 \| 0.1930 \|
	\| 0.7158 \| 15.7137 \| 18000 \| 1.0872 \| 0.4521 \| 0.1972 \|
	\| 0.7102 \| 16.1502 \| 18500 \| 1.0949 \| 0.4259 \| 0.1842 \|
	\| 0.6789 \| 16.5866 \| 19000 \| 1.1207 \| 0.4138 \| 0.1821 \|
	\| 0.6898 \| 17.0231 \| 19500 \| 1.1287 \| 0.4105 \| 0.1792 \|
	\| 0.6463 \| 17.4596 \| 20000 \| 1.2131 \| 0.4103 \| 0.1793 \|
	\| 0.6525 \| 17.8961 \| 20500 \| 1.1986 \| 0.4001 \| 0.1733 \|
	\| 0.6116 \| 18.3326 \| 21000 \| 1.2255 \| 0.4058 \| 0.1778 \|
	\| 0.6138 \| 18.7691 \| 21500 \| 1.2027 \| 0.3946 \| 0.1762 \|
	\| 0.6033 \| 19.2056 \| 22000 \| 1.1681 \| 0.3870 \| 0.1686 \|
	\| 0.5816 \| 19.6421 \| 22500 \| 1.1464 \| 0.3822 \| 0.1662 \|
	\| 0.5826 \| 20.0786 \| 23000 \| 1.1767 \| 0.3817 \| 0.1651 \|
	\| 0.5504 \| 20.5151 \| 23500 \| 1.2805 \| 0.3796 \| 0.1664 \|
	\| 0.5656 \| 20.9515 \| 24000 \| 1.1895 \| 0.3634 \| 0.1581 \|
	\| 0.5204 \| 21.3880 \| 24500 \| 1.2111 \| 0.3569 \| 0.1531 \|
	\| 0.5186 \| 21.8245 \| 25000 \| 1.2840 \| 0.3526 \| 0.1541 \|
	\| 0.5074 \| 22.2610 \| 25500 \| 1.2123 \| 0.3564 \| 0.1558 \|
	\| 0.4966 \| 22.6975 \| 26000 \| 1.1740 \| 0.3467 \| 0.1511 \|
	\| 0.4886 \| 23.1340 \| 26500 \| 1.3208 \| 0.3351 \| 0.1459 \|
	\| 0.4628 \| 23.5705 \| 27000 \| 1.3905 \| 0.3277 \| 0.1439 \|
	\| 0.4743 \| 24.0070 \| 27500 \| 1.3396 \| 0.3378 \| 0.1469 \|
	\| 0.4408 \| 24.4435 \| 28000 \| 1.3767 \| 0.3164 \| 0.1384 \|
	\| 0.4457 \| 24.8800 \| 28500 \| 1.2607 \| 0.3231 \| 0.1364 \|
	\| 0.4242 \| 25.3165 \| 29000 \| 1.2562 \| 0.3181 \| 0.1383 \|
	\| 0.4279 \| 25.7529 \| 29500 \| 1.2523 \| 0.3198 \| 0.1379 \|
	\| 0.4116 \| 26.1894 \| 30000 \| 1.3625 \| 0.3086 \| 0.1332 \|
	\| 0.3963 \| 26.6259 \| 30500 \| 1.2143 \| 0.3132 \| 0.1346 \|
	\| 0.3945 \| 27.0624 \| 31000 \| 1.2973 \| 0.2993 \| 0.1320 \|
	\| 0.3733 \| 27.4989 \| 31500 \| 1.3542 \| 0.2955 \| 0.1296 \|
	\| 0.3816 \| 27.9354 \| 32000 \| 1.3804 \| 0.2946 \| 0.1307 \|
	\| 0.3487 \| 28.3719 \| 32500 \| 1.4206 \| 0.2841 \| 0.1233 \|
	\| 0.3521 \| 28.8084 \| 33000 \| 1.4294 \| 0.2819 \| 0.1236 \|
	\| 0.3351 \| 29.2449 \| 33500 \| 1.5658 \| 0.2797 \| 0.1218 \|
	\| 0.3285 \| 29.6814 \| 34000 \| 1.5103 \| 0.2803 \| 0.1235 \|
	\| 0.3253 \| 30.1179 \| 34500 \| 1.4957 \| 0.2704 \| 0.1209 \|
	\| 0.308 \| 30.5543 \| 35000 \| 1.6964 \| 0.2648 \| 0.1173 \|
	\| 0.3184 \| 30.9908 \| 35500 \| 1.4796 \| 0.2609 \| 0.1153 \|
	\| 0.2941 \| 31.4273 \| 36000 \| 1.5527 \| 0.2597 \| 0.1169 \|
	\| 0.2897 \| 31.8638 \| 36500 \| 1.5907 \| 0.2574 \| 0.1150 \|
	\| 0.2883 \| 32.3003 \| 37000 \| 1.5718 \| 0.2536 \| 0.1132 \|
	\| 0.2792 \| 32.7368 \| 37500 \| 1.5505 \| 0.2527 \| 0.1134 \|
	\| 0.2773 \| 33.1733 \| 38000 \| 1.6607 \| 0.2480 \| 0.1102 \|
	\| 0.2579 \| 33.6098 \| 38500 \| 1.8962 \| 0.2461 \| 0.1108 \|
	\| 0.2658 \| 34.0463 \| 39000 \| 1.9136 \| 0.2426 \| 0.1116 \|
	\| 0.2539 \| 34.4828 \| 39500 \| 1.9131 \| 0.2440 \| 0.1113 \|
	\| 0.2501 \| 34.9192 \| 40000 \| 1.7290 \| 0.2368 \| 0.1083 \|
	\| 0.2358 \| 35.3557 \| 40500 \| 1.9586 \| 0.2309 \| 0.1059 \|
	\| 0.2395 \| 35.7922 \| 41000 \| 1.7617 \| 0.2295 \| 0.1040 \|
	\| 0.2267 \| 36.2287 \| 41500 \| 1.8779 \| 0.2264 \| 0.1021 \|
	\| 0.2239 \| 36.6652 \| 42000 \| 1.8696 \| 0.2248 \| 0.1019 \|
	\| 0.2183 \| 37.1017 \| 42500 \| 1.8445 \| 0.2229 \| 0.1026 \|
	\| 0.2149 \| 37.5382 \| 43000 \| 1.9865 \| 0.2265 \| 0.1041 \|
	\| 0.2125 \| 37.9747 \| 43500 \| 1.8998 \| 0.2252 \| 0.1039 \|
	\| 0.2001 \| 38.4112 \| 44000 \| 2.0591 \| 0.2214 \| 0.1010 \|
	\| 0.204 \| 38.8477 \| 44500 \| 1.9436 \| 0.2145 \| 0.1004 \|
	\| 0.1967 \| 39.2842 \| 45000 \| 2.0536 \| 0.2148 \| 0.0994 \|
	\| 0.1902 \| 39.7206 \| 45500 \| 2.0584 \| 0.2161 \| 0.1000 \|
	\| 0.1862 \| 40.1571 \| 46000 \| 2.0744 \| 0.2146 \| 0.1004 \|
	\| 0.182 \| 40.5936 \| 46500 \| 2.0731 \| 0.2108 \| 0.0982 \|
	\| 0.1876 \| 41.0301 \| 47000 \| 2.0895 \| 0.2096 \| 0.0978 \|
	\| 0.1744 \| 41.4666 \| 47500 \| 2.2355 \| 0.2038 \| 0.0956 \|
	\| 0.178 \| 41.9031 \| 48000 \| 2.1099 \| 0.2099 \| 0.0969 \|
	\| 0.1677 \| 42.3396 \| 48500 \| 2.2260 \| 0.2043 \| 0.0959 \|
	\| 0.1688 \| 42.7761 \| 49000 \| 2.2102 \| 0.2046 \| 0.0949 \|
	\| 0.1676 \| 43.2126 \| 49500 \| 2.2165 \| 0.2047 \| 0.0965 \|
	\| 0.1589 \| 43.6491 \| 50000 \| 2.2741 \| 0.2016 \| 0.0937 \|
	\| 0.1613 \| 44.0856 \| 50500 \| 2.2069 \| 0.2021 \| 0.0947 \|
	\| 0.1529 \| 44.5220 \| 51000 \| 2.3035 \| 0.2018 \| 0.0958 \|
	\| 0.1513 \| 44.9585 \| 51500 \| 2.4750 \| 0.2007 \| 0.0957 \|
	\| 0.1515 \| 45.3950 \| 52000 \| 2.5079 \| 0.2011 \| 0.0956 \|
	\| 0.1463 \| 45.8315 \| 52500 \| 2.5247 \| 0.1994 \| 0.0944 \|
	\| 0.1433 \| 46.2680 \| 53000 \| 2.5564 \| 0.1968 \| 0.0934 \|
	\| 0.143 \| 46.7045 \| 53500 \| 2.5230 \| 0.1970 \| 0.0933 \|
	\| 0.1399 \| 47.1410 \| 54000 \| 2.5532 \| 0.1954 \| 0.0927 \|
	\| 0.1377 \| 47.5775 \| 54500 \| 2.4811 \| 0.1985 \| 0.0933 \|
	\| 0.1358 \| 48.0140 \| 55000 \| 2.6065 \| 0.1980 \| 0.0934 \|
	\| 0.1373 \| 48.4505 \| 55500 \| 2.5808 \| 0.1951 \| 0.0926 \|
	\| 0.1327 \| 48.8869 \| 56000 \| 2.5458 \| 0.1954 \| 0.0930 \|
	\| 0.1329 \| 49.3234 \| 56500 \| 2.5948 \| 0.1942 \| 0.0927 \|
	\| 0.1361 \| 49.7599 \| 57000 \| 2.5960 \| 0.1951 \| 0.0927 \|


	### Framework versions

	- Transformers 4.45.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.17.0
	- Tokenizers 0.20.3

	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: bambara_mms_20_hour_jeli_asr_dataset
	results: []
	---

	<!-- 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. -->

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/wq6zc6lt)
	# bambara_mms_20_hour_jeli_asr_dataset

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.5960
	- Wer: 0.1951
	- Cer: 0.0927

	## 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: 0.0003
	- train_batch_size: 8
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 16
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 2.3781 \| 0.4365 \| 500 \| 1.7790 \| 0.9007 \| 0.4152 \|
	\| 1.5271 \| 0.8730 \| 1000 \| 1.4809 \| 0.7424 \| 0.3462 \|
	\| 1.4216 \| 1.3095 \| 1500 \| 1.4134 \| 0.8316 \| 0.4020 \|
	\| 1.3995 \| 1.7460 \| 2000 \| 1.3669 \| 0.7422 \| 0.3379 \|
	\| 1.3521 \| 2.1825 \| 2500 \| 1.2531 \| 0.6969 \| 0.3176 \|
	\| 1.3161 \| 2.6189 \| 3000 \| 1.3363 \| 0.6643 \| 0.3061 \|
	\| 1.2759 \| 3.0554 \| 3500 \| 1.2953 \| 0.6607 \| 0.3018 \|
	\| 1.2604 \| 3.4919 \| 4000 \| 1.2630 \| 0.6280 \| 0.2889 \|
	\| 1.2424 \| 3.9284 \| 4500 \| 1.4642 \| 0.6308 \| 0.2847 \|
	\| 1.1639 \| 4.3649 \| 5000 \| 1.3593 \| 0.6216 \| 0.2797 \|
	\| 1.2122 \| 4.8014 \| 5500 \| 1.2043 \| 0.5966 \| 0.2716 \|
	\| 1.1504 \| 5.2379 \| 6000 \| 1.1364 \| 0.5892 \| 0.2688 \|
	\| 1.156 \| 5.6744 \| 6500 \| 1.1405 \| 0.6049 \| 0.2726 \|
	\| 1.1371 \| 6.1109 \| 7000 \| 1.1854 \| 0.5817 \| 0.2633 \|
	\| 1.0981 \| 6.5474 \| 7500 \| 1.1306 \| 0.5876 \| 0.2625 \|
	\| 1.1021 \| 6.9838 \| 8000 \| 1.1026 \| 0.5910 \| 0.2659 \|
	\| 1.0295 \| 7.4203 \| 8500 \| 1.2506 \| 0.5671 \| 0.2600 \|
	\| 1.0702 \| 7.8568 \| 9000 \| 1.1672 \| 0.5521 \| 0.2529 \|
	\| 1.0277 \| 8.2933 \| 9500 \| 1.0966 \| 0.5616 \| 0.2544 \|
	\| 1.0023 \| 8.7298 \| 10000 \| 1.1389 \| 0.5353 \| 0.2403 \|
	\| 0.9945 \| 9.1663 \| 10500 \| 1.3434 \| 0.5302 \| 0.2420 \|
	\| 0.9533 \| 9.6028 \| 11000 \| 1.1546 \| 0.5391 \| 0.2517 \|
	\| 0.9675 \| 10.0393 \| 11500 \| 1.1966 \| 0.5355 \| 0.2451 \|
	\| 0.9061 \| 10.4758 \| 12000 \| 1.1808 \| 0.5116 \| 0.2310 \|
	\| 0.9243 \| 10.9123 \| 12500 \| 1.1189 \| 0.5095 \| 0.2290 \|
	\| 0.8834 \| 11.3488 \| 13000 \| 1.2189 \| 0.4979 \| 0.2226 \|
	\| 0.8819 \| 11.7852 \| 13500 \| 1.2035 \| 0.4910 \| 0.2158 \|
	\| 0.8522 \| 12.2217 \| 14000 \| 1.1385 \| 0.4961 \| 0.2173 \|
	\| 0.8417 \| 12.6582 \| 14500 \| 1.1060 \| 0.4787 \| 0.2110 \|
	\| 0.8352 \| 13.0947 \| 15000 \| 1.1295 \| 0.4957 \| 0.2237 \|
	\| 0.7857 \| 13.5312 \| 15500 \| 1.0946 \| 0.4814 \| 0.2142 \|
	\| 0.7963 \| 13.9677 \| 16000 \| 1.0891 \| 0.4844 \| 0.2240 \|
	\| 0.762 \| 14.4042 \| 16500 \| 1.0606 \| 0.4832 \| 0.2177 \|
	\| 0.7594 \| 14.8407 \| 17000 \| 1.0415 \| 0.4529 \| 0.1992 \|
	\| 0.7368 \| 15.2772 \| 17500 \| 1.0882 \| 0.4399 \| 0.1930 \|
	\| 0.7158 \| 15.7137 \| 18000 \| 1.0872 \| 0.4521 \| 0.1972 \|
	\| 0.7102 \| 16.1502 \| 18500 \| 1.0949 \| 0.4259 \| 0.1842 \|
	\| 0.6789 \| 16.5866 \| 19000 \| 1.1207 \| 0.4138 \| 0.1821 \|
	\| 0.6898 \| 17.0231 \| 19500 \| 1.1287 \| 0.4105 \| 0.1792 \|
	\| 0.6463 \| 17.4596 \| 20000 \| 1.2131 \| 0.4103 \| 0.1793 \|
	\| 0.6525 \| 17.8961 \| 20500 \| 1.1986 \| 0.4001 \| 0.1733 \|
	\| 0.6116 \| 18.3326 \| 21000 \| 1.2255 \| 0.4058 \| 0.1778 \|
	\| 0.6138 \| 18.7691 \| 21500 \| 1.2027 \| 0.3946 \| 0.1762 \|
	\| 0.6033 \| 19.2056 \| 22000 \| 1.1681 \| 0.3870 \| 0.1686 \|
	\| 0.5816 \| 19.6421 \| 22500 \| 1.1464 \| 0.3822 \| 0.1662 \|
	\| 0.5826 \| 20.0786 \| 23000 \| 1.1767 \| 0.3817 \| 0.1651 \|
	\| 0.5504 \| 20.5151 \| 23500 \| 1.2805 \| 0.3796 \| 0.1664 \|
	\| 0.5656 \| 20.9515 \| 24000 \| 1.1895 \| 0.3634 \| 0.1581 \|
	\| 0.5204 \| 21.3880 \| 24500 \| 1.2111 \| 0.3569 \| 0.1531 \|
	\| 0.5186 \| 21.8245 \| 25000 \| 1.2840 \| 0.3526 \| 0.1541 \|
	\| 0.5074 \| 22.2610 \| 25500 \| 1.2123 \| 0.3564 \| 0.1558 \|
	\| 0.4966 \| 22.6975 \| 26000 \| 1.1740 \| 0.3467 \| 0.1511 \|
	\| 0.4886 \| 23.1340 \| 26500 \| 1.3208 \| 0.3351 \| 0.1459 \|
	\| 0.4628 \| 23.5705 \| 27000 \| 1.3905 \| 0.3277 \| 0.1439 \|
	\| 0.4743 \| 24.0070 \| 27500 \| 1.3396 \| 0.3378 \| 0.1469 \|
	\| 0.4408 \| 24.4435 \| 28000 \| 1.3767 \| 0.3164 \| 0.1384 \|
	\| 0.4457 \| 24.8800 \| 28500 \| 1.2607 \| 0.3231 \| 0.1364 \|
	\| 0.4242 \| 25.3165 \| 29000 \| 1.2562 \| 0.3181 \| 0.1383 \|
	\| 0.4279 \| 25.7529 \| 29500 \| 1.2523 \| 0.3198 \| 0.1379 \|
	\| 0.4116 \| 26.1894 \| 30000 \| 1.3625 \| 0.3086 \| 0.1332 \|
	\| 0.3963 \| 26.6259 \| 30500 \| 1.2143 \| 0.3132 \| 0.1346 \|
	\| 0.3945 \| 27.0624 \| 31000 \| 1.2973 \| 0.2993 \| 0.1320 \|
	\| 0.3733 \| 27.4989 \| 31500 \| 1.3542 \| 0.2955 \| 0.1296 \|
	\| 0.3816 \| 27.9354 \| 32000 \| 1.3804 \| 0.2946 \| 0.1307 \|
	\| 0.3487 \| 28.3719 \| 32500 \| 1.4206 \| 0.2841 \| 0.1233 \|
	\| 0.3521 \| 28.8084 \| 33000 \| 1.4294 \| 0.2819 \| 0.1236 \|
	\| 0.3351 \| 29.2449 \| 33500 \| 1.5658 \| 0.2797 \| 0.1218 \|
	\| 0.3285 \| 29.6814 \| 34000 \| 1.5103 \| 0.2803 \| 0.1235 \|
	\| 0.3253 \| 30.1179 \| 34500 \| 1.4957 \| 0.2704 \| 0.1209 \|
	\| 0.308 \| 30.5543 \| 35000 \| 1.6964 \| 0.2648 \| 0.1173 \|
	\| 0.3184 \| 30.9908 \| 35500 \| 1.4796 \| 0.2609 \| 0.1153 \|
	\| 0.2941 \| 31.4273 \| 36000 \| 1.5527 \| 0.2597 \| 0.1169 \|
	\| 0.2897 \| 31.8638 \| 36500 \| 1.5907 \| 0.2574 \| 0.1150 \|
	\| 0.2883 \| 32.3003 \| 37000 \| 1.5718 \| 0.2536 \| 0.1132 \|
	\| 0.2792 \| 32.7368 \| 37500 \| 1.5505 \| 0.2527 \| 0.1134 \|
	\| 0.2773 \| 33.1733 \| 38000 \| 1.6607 \| 0.2480 \| 0.1102 \|
	\| 0.2579 \| 33.6098 \| 38500 \| 1.8962 \| 0.2461 \| 0.1108 \|
	\| 0.2658 \| 34.0463 \| 39000 \| 1.9136 \| 0.2426 \| 0.1116 \|
	\| 0.2539 \| 34.4828 \| 39500 \| 1.9131 \| 0.2440 \| 0.1113 \|
	\| 0.2501 \| 34.9192 \| 40000 \| 1.7290 \| 0.2368 \| 0.1083 \|
	\| 0.2358 \| 35.3557 \| 40500 \| 1.9586 \| 0.2309 \| 0.1059 \|
	\| 0.2395 \| 35.7922 \| 41000 \| 1.7617 \| 0.2295 \| 0.1040 \|
	\| 0.2267 \| 36.2287 \| 41500 \| 1.8779 \| 0.2264 \| 0.1021 \|
	\| 0.2239 \| 36.6652 \| 42000 \| 1.8696 \| 0.2248 \| 0.1019 \|
	\| 0.2183 \| 37.1017 \| 42500 \| 1.8445 \| 0.2229 \| 0.1026 \|
	\| 0.2149 \| 37.5382 \| 43000 \| 1.9865 \| 0.2265 \| 0.1041 \|
	\| 0.2125 \| 37.9747 \| 43500 \| 1.8998 \| 0.2252 \| 0.1039 \|
	\| 0.2001 \| 38.4112 \| 44000 \| 2.0591 \| 0.2214 \| 0.1010 \|
	\| 0.204 \| 38.8477 \| 44500 \| 1.9436 \| 0.2145 \| 0.1004 \|
	\| 0.1967 \| 39.2842 \| 45000 \| 2.0536 \| 0.2148 \| 0.0994 \|
	\| 0.1902 \| 39.7206 \| 45500 \| 2.0584 \| 0.2161 \| 0.1000 \|
	\| 0.1862 \| 40.1571 \| 46000 \| 2.0744 \| 0.2146 \| 0.1004 \|
	\| 0.182 \| 40.5936 \| 46500 \| 2.0731 \| 0.2108 \| 0.0982 \|
	\| 0.1876 \| 41.0301 \| 47000 \| 2.0895 \| 0.2096 \| 0.0978 \|
	\| 0.1744 \| 41.4666 \| 47500 \| 2.2355 \| 0.2038 \| 0.0956 \|
	\| 0.178 \| 41.9031 \| 48000 \| 2.1099 \| 0.2099 \| 0.0969 \|
	\| 0.1677 \| 42.3396 \| 48500 \| 2.2260 \| 0.2043 \| 0.0959 \|
	\| 0.1688 \| 42.7761 \| 49000 \| 2.2102 \| 0.2046 \| 0.0949 \|
	\| 0.1676 \| 43.2126 \| 49500 \| 2.2165 \| 0.2047 \| 0.0965 \|
	\| 0.1589 \| 43.6491 \| 50000 \| 2.2741 \| 0.2016 \| 0.0937 \|
	\| 0.1613 \| 44.0856 \| 50500 \| 2.2069 \| 0.2021 \| 0.0947 \|
	\| 0.1529 \| 44.5220 \| 51000 \| 2.3035 \| 0.2018 \| 0.0958 \|
	\| 0.1513 \| 44.9585 \| 51500 \| 2.4750 \| 0.2007 \| 0.0957 \|
	\| 0.1515 \| 45.3950 \| 52000 \| 2.5079 \| 0.2011 \| 0.0956 \|
	\| 0.1463 \| 45.8315 \| 52500 \| 2.5247 \| 0.1994 \| 0.0944 \|
	\| 0.1433 \| 46.2680 \| 53000 \| 2.5564 \| 0.1968 \| 0.0934 \|
	\| 0.143 \| 46.7045 \| 53500 \| 2.5230 \| 0.1970 \| 0.0933 \|
	\| 0.1399 \| 47.1410 \| 54000 \| 2.5532 \| 0.1954 \| 0.0927 \|
	\| 0.1377 \| 47.5775 \| 54500 \| 2.4811 \| 0.1985 \| 0.0933 \|
	\| 0.1358 \| 48.0140 \| 55000 \| 2.6065 \| 0.1980 \| 0.0934 \|
	\| 0.1373 \| 48.4505 \| 55500 \| 2.5808 \| 0.1951 \| 0.0926 \|
	\| 0.1327 \| 48.8869 \| 56000 \| 2.5458 \| 0.1954 \| 0.0930 \|
	\| 0.1329 \| 49.3234 \| 56500 \| 2.5948 \| 0.1942 \| 0.0927 \|
	\| 0.1361 \| 49.7599 \| 57000 \| 2.5960 \| 0.1951 \| 0.0927 \|


	### Framework versions

	- Transformers 4.45.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.17.0
	- Tokenizers 0.20.3