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	---
	license: mit
	base_model: facebook/m2m100_1.2B
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: cs_m2m_0.001_50_v0.2
	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. -->

	# cs_m2m_0.001_50_v0.2

	This model is a fine-tuned version of [facebook/m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 8.4343
	- Bleu: 0.0488
	- Gen Len: 93.2857

	## 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: 0.001
	- 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
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\|
	\| 5.0853 \| 1.0 \| 6 \| 6.9325 \| 0.0 \| 5.0 \|
	\| 4.3538 \| 2.0 \| 12 \| 7.0396 \| 0.1923 \| 7.5714 \|
	\| 4.6426 \| 3.0 \| 18 \| 7.0321 \| 0.1563 \| 42.1429 \|
	\| 5.1737 \| 4.0 \| 24 \| 7.0390 \| 0.0335 \| 103.5238 \|
	\| 3.9214 \| 5.0 \| 30 \| 7.0585 \| 0.0 \| 5.0 \|
	\| 4.7309 \| 6.0 \| 36 \| 7.1597 \| 0.1313 \| 7.7619 \|
	\| 4.3458 \| 7.0 \| 42 \| 7.1875 \| 0.0 \| 5.0 \|
	\| 4.1409 \| 8.0 \| 48 \| 7.1934 \| 0.308 \| 18.1429 \|
	\| 3.8187 \| 9.0 \| 54 \| 7.1696 \| 0.0 \| 5.0 \|
	\| 3.9459 \| 10.0 \| 60 \| 7.1153 \| 0.0 \| 5.0 \|
	\| 4.3563 \| 11.0 \| 66 \| 7.2286 \| 0.3581 \| 8.619 \|
	\| 4.4193 \| 12.0 \| 72 \| 7.3526 \| 0.0 \| 5.0 \|
	\| 4.4508 \| 13.0 \| 78 \| 7.4000 \| 0.0 \| 5.0 \|
	\| 4.115 \| 14.0 \| 84 \| 7.4140 \| 0.0 \| 5.0 \|
	\| 4.1807 \| 15.0 \| 90 \| 7.4866 \| 0.0 \| 5.0 \|
	\| 3.8422 \| 16.0 \| 96 \| 7.6149 \| 0.3839 \| 9.0 \|
	\| 4.1567 \| 17.0 \| 102 \| 7.5413 \| 0.2035 \| 8.8095 \|
	\| 4.3236 \| 18.0 \| 108 \| 7.5256 \| 0.2104 \| 9.0 \|
	\| 4.3343 \| 19.0 \| 114 \| 7.5449 \| 0.149 \| 8.4286 \|
	\| 4.3139 \| 20.0 \| 120 \| 7.4758 \| 0.0 \| 5.0 \|
	\| 3.1706 \| 21.0 \| 126 \| 7.5896 \| 0.0274 \| 130.9048 \|
	\| 3.0241 \| 22.0 \| 132 \| 7.8300 \| 0.2142 \| 7.9524 \|
	\| 4.5364 \| 23.0 \| 138 \| 7.8698 \| 0.0515 \| 5.2857 \|
	\| 5.4824 \| 24.0 \| 144 \| 7.8732 \| 0.0364 \| 192.0952 \|
	\| 3.8072 \| 25.0 \| 150 \| 7.7993 \| 0.0 \| 5.0 \|
	\| 3.9879 \| 26.0 \| 156 \| 7.7222 \| 0.0746 \| 200.0 \|
	\| 4.0397 \| 27.0 \| 162 \| 7.6906 \| 0.0436 \| 146.0476 \|
	\| 3.7429 \| 28.0 \| 168 \| 7.7814 \| 0.0 \| 6.8095 \|
	\| 3.7498 \| 29.0 \| 174 \| 7.8873 \| 0.2861 \| 8.0 \|
	\| 4.1991 \| 30.0 \| 180 \| 8.0400 \| 0.3032 \| 13.5714 \|
	\| 5.4424 \| 31.0 \| 186 \| 7.9368 \| 0.2537 \| 15.1905 \|
	\| 3.6523 \| 32.0 \| 192 \| 7.8529 \| 0.3288 \| 7.1905 \|
	\| 5.5908 \| 33.0 \| 198 \| 7.8531 \| 0.087 \| 5.8571 \|
	\| 3.8218 \| 34.0 \| 204 \| 7.7538 \| 0.2073 \| 7.8571 \|
	\| 3.8408 \| 35.0 \| 210 \| 7.6796 \| 0.1027 \| 7.381 \|
	\| 3.2347 \| 36.0 \| 216 \| 7.8281 \| 0.1662 \| 8.9524 \|
	\| 4.0158 \| 37.0 \| 222 \| 7.8108 \| 0.1907 \| 23.9524 \|
	\| 4.2395 \| 38.0 \| 228 \| 7.7778 \| 0.4592 \| 19.4286 \|
	\| 3.1863 \| 39.0 \| 234 \| 7.8962 \| 0.3148 \| 16.1429 \|
	\| 3.5706 \| 40.0 \| 240 \| 8.2310 \| 0.2962 \| 33.7619 \|
	\| 3.8174 \| 41.0 \| 246 \| 8.0290 \| 0.2864 \| 14.1429 \|
	\| 3.6144 \| 42.0 \| 252 \| 7.9235 \| 0.2737 \| 11.8095 \|
	\| 3.914 \| 43.0 \| 258 \| 7.9920 \| 0.286 \| 15.5714 \|
	\| 3.9245 \| 44.0 \| 264 \| 7.9770 \| 0.1251 \| 35.8571 \|
	\| 3.223 \| 45.0 \| 270 \| 8.1701 \| 0.1428 \| 32.1429 \|
	\| 3.5751 \| 46.0 \| 276 \| 8.2573 \| 0.2497 \| 19.9048 \|
	\| 3.7939 \| 47.0 \| 282 \| 8.2825 \| 0.0571 \| 110.9524 \|
	\| 3.8968 \| 48.0 \| 288 \| 8.4263 \| 0.0702 \| 200.0 \|
	\| 2.2186 \| 49.0 \| 294 \| 8.3673 \| 0.2356 \| 107.5714 \|
	\| 3.1794 \| 50.0 \| 300 \| 8.2041 \| 0.2142 \| 38.5238 \|
	\| 3.3098 \| 51.0 \| 306 \| 8.2863 \| 0.0349 \| 113.3333 \|
	\| 3.7869 \| 52.0 \| 312 \| 8.3350 \| 0.0655 \| 95.2857 \|
	\| 3.7239 \| 53.0 \| 318 \| 8.2509 \| 0.025 \| 179.7143 \|
	\| 3.5206 \| 54.0 \| 324 \| 8.2301 \| 0.074 \| 75.9524 \|
	\| 3.2225 \| 55.0 \| 330 \| 8.1540 \| 0.0242 \| 173.5238 \|
	\| 2.6646 \| 56.0 \| 336 \| 8.1574 \| 0.3081 \| 91.2381 \|
	\| 3.3487 \| 57.0 \| 342 \| 8.1095 \| 0.0597 \| 115.6667 \|
	\| 3.2801 \| 58.0 \| 348 \| 8.1534 \| 0.1796 \| 39.8095 \|
	\| 2.7653 \| 59.0 \| 354 \| 8.2800 \| 0.0423 \| 82.0476 \|
	\| 3.3158 \| 60.0 \| 360 \| 8.2560 \| 0.0437 \| 116.4762 \|
	\| 2.5549 \| 61.0 \| 366 \| 8.2070 \| 0.0348 \| 164.2857 \|
	\| 2.9411 \| 62.0 \| 372 \| 8.2850 \| 0.3249 \| 12.381 \|
	\| 2.965 \| 63.0 \| 378 \| 8.3497 \| 0.0352 \| 117.1429 \|
	\| 3.4553 \| 64.0 \| 384 \| 8.3532 \| 0.0739 \| 145.9524 \|
	\| 3.1656 \| 65.0 \| 390 \| 8.3229 \| 0.1993 \| 102.5714 \|
	\| 3.3285 \| 66.0 \| 396 \| 8.3454 \| 0.2297 \| 46.9524 \|
	\| 2.7365 \| 67.0 \| 402 \| 8.4989 \| 0.2246 \| 39.381 \|
	\| 3.1372 \| 68.0 \| 408 \| 8.4935 \| 0.0444 \| 115.2381 \|
	\| 2.3018 \| 69.0 \| 414 \| 8.4543 \| 0.0552 \| 113.8571 \|
	\| 2.5972 \| 70.0 \| 420 \| 8.4092 \| 0.245 \| 15.3333 \|
	\| 5.2476 \| 71.0 \| 426 \| 8.3573 \| 0.2629 \| 32.0476 \|
	\| 2.4894 \| 72.0 \| 432 \| 8.3228 \| 0.2863 \| 42.5238 \|
	\| 3.9303 \| 73.0 \| 438 \| 8.3295 \| 0.5382 \| 36.7619 \|
	\| 3.8135 \| 74.0 \| 444 \| 8.3803 \| 0.2421 \| 41.8095 \|
	\| 2.36 \| 75.0 \| 450 \| 8.4558 \| 0.1325 \| 58.381 \|
	\| 2.7095 \| 76.0 \| 456 \| 8.5280 \| 0.2592 \| 68.9524 \|
	\| 2.0011 \| 77.0 \| 462 \| 8.4020 \| 0.2997 \| 58.2381 \|
	\| 1.9209 \| 78.0 \| 468 \| 8.4449 \| 0.1838 \| 43.7143 \|
	\| 3.3766 \| 79.0 \| 474 \| 8.5564 \| 0.2789 \| 24.9048 \|
	\| 3.4283 \| 80.0 \| 480 \| 8.5476 \| 0.264 \| 35.7143 \|
	\| 2.8935 \| 81.0 \| 486 \| 8.5057 \| 0.0633 \| 79.8095 \|
	\| 2.5961 \| 82.0 \| 492 \| 8.4756 \| 0.0648 \| 92.9524 \|
	\| 3.999 \| 83.0 \| 498 \| 8.4273 \| 0.1558 \| 68.4286 \|
	\| 3.612 \| 84.0 \| 504 \| 8.3825 \| 0.1379 \| 52.9524 \|
	\| 2.5813 \| 85.0 \| 510 \| 8.3289 \| 0.1275 \| 42.0 \|
	\| 2.8265 \| 86.0 \| 516 \| 8.3150 \| 0.2806 \| 22.9048 \|
	\| 3.1955 \| 87.0 \| 522 \| 8.3218 \| 0.2976 \| 17.4762 \|
	\| 2.7654 \| 88.0 \| 528 \| 8.3135 \| 0.2878 \| 35.619 \|
	\| 3.7539 \| 89.0 \| 534 \| 8.3157 \| 0.0896 \| 48.4762 \|
	\| 1.8882 \| 90.0 \| 540 \| 8.3397 \| 0.0897 \| 57.7619 \|
	\| 2.5795 \| 91.0 \| 546 \| 8.3700 \| 0.069 \| 79.1905 \|
	\| 1.9473 \| 92.0 \| 552 \| 8.4195 \| 0.1347 \| 152.4762 \|
	\| 2.349 \| 93.0 \| 558 \| 8.4513 \| 0.0239 \| 183.619 \|
	\| 3.1561 \| 94.0 \| 564 \| 8.4664 \| 0.0234 \| 192.4286 \|
	\| 2.9355 \| 95.0 \| 570 \| 8.4679 \| 0.1186 \| 167.8571 \|
	\| 2.5661 \| 96.0 \| 576 \| 8.4588 \| 0.1833 \| 110.9524 \|
	\| 3.1005 \| 97.0 \| 582 \| 8.4478 \| 0.0432 \| 124.8571 \|
	\| 2.7184 \| 98.0 \| 588 \| 8.4399 \| 0.0589 \| 84.9048 \|
	\| 2.8431 \| 99.0 \| 594 \| 8.4340 \| 0.1961 \| 103.9524 \|
	\| 2.9269 \| 100.0 \| 600 \| 8.4343 \| 0.0488 \| 93.2857 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 1.13.1+cu117
	- Datasets 2.16.1
	- Tokenizers 0.15.0

	---
	license: mit
	base_model: facebook/m2m100_1.2B
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: cs_m2m_0.001_50_v0.2
	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. -->

	# cs_m2m_0.001_50_v0.2

	This model is a fine-tuned version of [facebook/m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 8.4343
	- Bleu: 0.0488
	- Gen Len: 93.2857

	## 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: 0.001
	- 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
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\|
	\| 5.0853 \| 1.0 \| 6 \| 6.9325 \| 0.0 \| 5.0 \|
	\| 4.3538 \| 2.0 \| 12 \| 7.0396 \| 0.1923 \| 7.5714 \|
	\| 4.6426 \| 3.0 \| 18 \| 7.0321 \| 0.1563 \| 42.1429 \|
	\| 5.1737 \| 4.0 \| 24 \| 7.0390 \| 0.0335 \| 103.5238 \|
	\| 3.9214 \| 5.0 \| 30 \| 7.0585 \| 0.0 \| 5.0 \|
	\| 4.7309 \| 6.0 \| 36 \| 7.1597 \| 0.1313 \| 7.7619 \|
	\| 4.3458 \| 7.0 \| 42 \| 7.1875 \| 0.0 \| 5.0 \|
	\| 4.1409 \| 8.0 \| 48 \| 7.1934 \| 0.308 \| 18.1429 \|
	\| 3.8187 \| 9.0 \| 54 \| 7.1696 \| 0.0 \| 5.0 \|
	\| 3.9459 \| 10.0 \| 60 \| 7.1153 \| 0.0 \| 5.0 \|
	\| 4.3563 \| 11.0 \| 66 \| 7.2286 \| 0.3581 \| 8.619 \|
	\| 4.4193 \| 12.0 \| 72 \| 7.3526 \| 0.0 \| 5.0 \|
	\| 4.4508 \| 13.0 \| 78 \| 7.4000 \| 0.0 \| 5.0 \|
	\| 4.115 \| 14.0 \| 84 \| 7.4140 \| 0.0 \| 5.0 \|
	\| 4.1807 \| 15.0 \| 90 \| 7.4866 \| 0.0 \| 5.0 \|
	\| 3.8422 \| 16.0 \| 96 \| 7.6149 \| 0.3839 \| 9.0 \|
	\| 4.1567 \| 17.0 \| 102 \| 7.5413 \| 0.2035 \| 8.8095 \|
	\| 4.3236 \| 18.0 \| 108 \| 7.5256 \| 0.2104 \| 9.0 \|
	\| 4.3343 \| 19.0 \| 114 \| 7.5449 \| 0.149 \| 8.4286 \|
	\| 4.3139 \| 20.0 \| 120 \| 7.4758 \| 0.0 \| 5.0 \|
	\| 3.1706 \| 21.0 \| 126 \| 7.5896 \| 0.0274 \| 130.9048 \|
	\| 3.0241 \| 22.0 \| 132 \| 7.8300 \| 0.2142 \| 7.9524 \|
	\| 4.5364 \| 23.0 \| 138 \| 7.8698 \| 0.0515 \| 5.2857 \|
	\| 5.4824 \| 24.0 \| 144 \| 7.8732 \| 0.0364 \| 192.0952 \|
	\| 3.8072 \| 25.0 \| 150 \| 7.7993 \| 0.0 \| 5.0 \|
	\| 3.9879 \| 26.0 \| 156 \| 7.7222 \| 0.0746 \| 200.0 \|
	\| 4.0397 \| 27.0 \| 162 \| 7.6906 \| 0.0436 \| 146.0476 \|
	\| 3.7429 \| 28.0 \| 168 \| 7.7814 \| 0.0 \| 6.8095 \|
	\| 3.7498 \| 29.0 \| 174 \| 7.8873 \| 0.2861 \| 8.0 \|
	\| 4.1991 \| 30.0 \| 180 \| 8.0400 \| 0.3032 \| 13.5714 \|
	\| 5.4424 \| 31.0 \| 186 \| 7.9368 \| 0.2537 \| 15.1905 \|
	\| 3.6523 \| 32.0 \| 192 \| 7.8529 \| 0.3288 \| 7.1905 \|
	\| 5.5908 \| 33.0 \| 198 \| 7.8531 \| 0.087 \| 5.8571 \|
	\| 3.8218 \| 34.0 \| 204 \| 7.7538 \| 0.2073 \| 7.8571 \|
	\| 3.8408 \| 35.0 \| 210 \| 7.6796 \| 0.1027 \| 7.381 \|
	\| 3.2347 \| 36.0 \| 216 \| 7.8281 \| 0.1662 \| 8.9524 \|
	\| 4.0158 \| 37.0 \| 222 \| 7.8108 \| 0.1907 \| 23.9524 \|
	\| 4.2395 \| 38.0 \| 228 \| 7.7778 \| 0.4592 \| 19.4286 \|
	\| 3.1863 \| 39.0 \| 234 \| 7.8962 \| 0.3148 \| 16.1429 \|
	\| 3.5706 \| 40.0 \| 240 \| 8.2310 \| 0.2962 \| 33.7619 \|
	\| 3.8174 \| 41.0 \| 246 \| 8.0290 \| 0.2864 \| 14.1429 \|
	\| 3.6144 \| 42.0 \| 252 \| 7.9235 \| 0.2737 \| 11.8095 \|
	\| 3.914 \| 43.0 \| 258 \| 7.9920 \| 0.286 \| 15.5714 \|
	\| 3.9245 \| 44.0 \| 264 \| 7.9770 \| 0.1251 \| 35.8571 \|
	\| 3.223 \| 45.0 \| 270 \| 8.1701 \| 0.1428 \| 32.1429 \|
	\| 3.5751 \| 46.0 \| 276 \| 8.2573 \| 0.2497 \| 19.9048 \|
	\| 3.7939 \| 47.0 \| 282 \| 8.2825 \| 0.0571 \| 110.9524 \|
	\| 3.8968 \| 48.0 \| 288 \| 8.4263 \| 0.0702 \| 200.0 \|
	\| 2.2186 \| 49.0 \| 294 \| 8.3673 \| 0.2356 \| 107.5714 \|
	\| 3.1794 \| 50.0 \| 300 \| 8.2041 \| 0.2142 \| 38.5238 \|
	\| 3.3098 \| 51.0 \| 306 \| 8.2863 \| 0.0349 \| 113.3333 \|
	\| 3.7869 \| 52.0 \| 312 \| 8.3350 \| 0.0655 \| 95.2857 \|
	\| 3.7239 \| 53.0 \| 318 \| 8.2509 \| 0.025 \| 179.7143 \|
	\| 3.5206 \| 54.0 \| 324 \| 8.2301 \| 0.074 \| 75.9524 \|
	\| 3.2225 \| 55.0 \| 330 \| 8.1540 \| 0.0242 \| 173.5238 \|
	\| 2.6646 \| 56.0 \| 336 \| 8.1574 \| 0.3081 \| 91.2381 \|
	\| 3.3487 \| 57.0 \| 342 \| 8.1095 \| 0.0597 \| 115.6667 \|
	\| 3.2801 \| 58.0 \| 348 \| 8.1534 \| 0.1796 \| 39.8095 \|
	\| 2.7653 \| 59.0 \| 354 \| 8.2800 \| 0.0423 \| 82.0476 \|
	\| 3.3158 \| 60.0 \| 360 \| 8.2560 \| 0.0437 \| 116.4762 \|
	\| 2.5549 \| 61.0 \| 366 \| 8.2070 \| 0.0348 \| 164.2857 \|
	\| 2.9411 \| 62.0 \| 372 \| 8.2850 \| 0.3249 \| 12.381 \|
	\| 2.965 \| 63.0 \| 378 \| 8.3497 \| 0.0352 \| 117.1429 \|
	\| 3.4553 \| 64.0 \| 384 \| 8.3532 \| 0.0739 \| 145.9524 \|
	\| 3.1656 \| 65.0 \| 390 \| 8.3229 \| 0.1993 \| 102.5714 \|
	\| 3.3285 \| 66.0 \| 396 \| 8.3454 \| 0.2297 \| 46.9524 \|
	\| 2.7365 \| 67.0 \| 402 \| 8.4989 \| 0.2246 \| 39.381 \|
	\| 3.1372 \| 68.0 \| 408 \| 8.4935 \| 0.0444 \| 115.2381 \|
	\| 2.3018 \| 69.0 \| 414 \| 8.4543 \| 0.0552 \| 113.8571 \|
	\| 2.5972 \| 70.0 \| 420 \| 8.4092 \| 0.245 \| 15.3333 \|
	\| 5.2476 \| 71.0 \| 426 \| 8.3573 \| 0.2629 \| 32.0476 \|
	\| 2.4894 \| 72.0 \| 432 \| 8.3228 \| 0.2863 \| 42.5238 \|
	\| 3.9303 \| 73.0 \| 438 \| 8.3295 \| 0.5382 \| 36.7619 \|
	\| 3.8135 \| 74.0 \| 444 \| 8.3803 \| 0.2421 \| 41.8095 \|
	\| 2.36 \| 75.0 \| 450 \| 8.4558 \| 0.1325 \| 58.381 \|
	\| 2.7095 \| 76.0 \| 456 \| 8.5280 \| 0.2592 \| 68.9524 \|
	\| 2.0011 \| 77.0 \| 462 \| 8.4020 \| 0.2997 \| 58.2381 \|
	\| 1.9209 \| 78.0 \| 468 \| 8.4449 \| 0.1838 \| 43.7143 \|
	\| 3.3766 \| 79.0 \| 474 \| 8.5564 \| 0.2789 \| 24.9048 \|
	\| 3.4283 \| 80.0 \| 480 \| 8.5476 \| 0.264 \| 35.7143 \|
	\| 2.8935 \| 81.0 \| 486 \| 8.5057 \| 0.0633 \| 79.8095 \|
	\| 2.5961 \| 82.0 \| 492 \| 8.4756 \| 0.0648 \| 92.9524 \|
	\| 3.999 \| 83.0 \| 498 \| 8.4273 \| 0.1558 \| 68.4286 \|
	\| 3.612 \| 84.0 \| 504 \| 8.3825 \| 0.1379 \| 52.9524 \|
	\| 2.5813 \| 85.0 \| 510 \| 8.3289 \| 0.1275 \| 42.0 \|
	\| 2.8265 \| 86.0 \| 516 \| 8.3150 \| 0.2806 \| 22.9048 \|
	\| 3.1955 \| 87.0 \| 522 \| 8.3218 \| 0.2976 \| 17.4762 \|
	\| 2.7654 \| 88.0 \| 528 \| 8.3135 \| 0.2878 \| 35.619 \|
	\| 3.7539 \| 89.0 \| 534 \| 8.3157 \| 0.0896 \| 48.4762 \|
	\| 1.8882 \| 90.0 \| 540 \| 8.3397 \| 0.0897 \| 57.7619 \|
	\| 2.5795 \| 91.0 \| 546 \| 8.3700 \| 0.069 \| 79.1905 \|
	\| 1.9473 \| 92.0 \| 552 \| 8.4195 \| 0.1347 \| 152.4762 \|
	\| 2.349 \| 93.0 \| 558 \| 8.4513 \| 0.0239 \| 183.619 \|
	\| 3.1561 \| 94.0 \| 564 \| 8.4664 \| 0.0234 \| 192.4286 \|
	\| 2.9355 \| 95.0 \| 570 \| 8.4679 \| 0.1186 \| 167.8571 \|
	\| 2.5661 \| 96.0 \| 576 \| 8.4588 \| 0.1833 \| 110.9524 \|
	\| 3.1005 \| 97.0 \| 582 \| 8.4478 \| 0.0432 \| 124.8571 \|
	\| 2.7184 \| 98.0 \| 588 \| 8.4399 \| 0.0589 \| 84.9048 \|
	\| 2.8431 \| 99.0 \| 594 \| 8.4340 \| 0.1961 \| 103.9524 \|
	\| 2.9269 \| 100.0 \| 600 \| 8.4343 \| 0.0488 \| 93.2857 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 1.13.1+cu117
	- Datasets 2.16.1
	- Tokenizers 0.15.0