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	---
	tags:
	- translation
	- generated_from_trainer
	datasets:
	- cmu_hinglish_dog
	metrics:
	- bleu
	model-index:
	- name: t5-small_6_3-hi_en-to-en
	results:
	- task:
	name: Sequence-to-sequence Language Modeling
	type: text2text-generation
	dataset:
	name: cmu_hinglish_dog
	type: cmu_hinglish_dog
	args: hi_en-en
	metrics:
	- name: Bleu
	type: bleu
	value: 18.0863
	---

	<!-- 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. -->

	# t5-small_6_3-hi_en-to-en

	This model was trained from scratch on the cmu_hinglish_dog dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.3662
	- Bleu: 18.0863
	- Gen Len: 15.2708

	## Model description

	Model generated using:<br />
	```python make_student.py t5-small t5_small_6_3 6 3```<br />
	Check this [link](https://discuss.huggingface.co/t/questions-on-distilling-from-t5/1193/9) for more information.

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	Used cmu_hinglish_dog dataset. Please check this [link](https://huggingface.co/datasets/cmu_hinglish_dog) for dataset description

	## Translation:

	* Source: hi_en: The text in Hinglish
	* Target: en: The text in English



	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|
	\| No log \| 1.0 \| 126 \| 3.0601 \| 4.7146 \| 11.9904 \|
	\| No log \| 2.0 \| 252 \| 2.8885 \| 5.9584 \| 12.3418 \|
	\| No log \| 3.0 \| 378 \| 2.7914 \| 6.649 \| 12.3758 \|
	\| 3.4671 \| 4.0 \| 504 \| 2.7347 \| 7.3305 \| 12.3854 \|
	\| 3.4671 \| 5.0 \| 630 \| 2.6832 \| 8.3132 \| 12.4268 \|
	\| 3.4671 \| 6.0 \| 756 \| 2.6485 \| 8.339 \| 12.3641 \|
	\| 3.4671 \| 7.0 \| 882 \| 2.6096 \| 8.7269 \| 12.414 \|
	\| 3.0208 \| 8.0 \| 1008 \| 2.5814 \| 9.2163 \| 12.2675 \|
	\| 3.0208 \| 9.0 \| 1134 \| 2.5542 \| 9.448 \| 12.3875 \|
	\| 3.0208 \| 10.0 \| 1260 \| 2.5339 \| 9.9011 \| 12.4321 \|
	\| 3.0208 \| 11.0 \| 1386 \| 2.5043 \| 9.7529 \| 12.5149 \|
	\| 2.834 \| 12.0 \| 1512 \| 2.4848 \| 9.9606 \| 12.4193 \|
	\| 2.834 \| 13.0 \| 1638 \| 2.4737 \| 9.9368 \| 12.3673 \|
	\| 2.834 \| 14.0 \| 1764 \| 2.4458 \| 10.3182 \| 12.4352 \|
	\| 2.834 \| 15.0 \| 1890 \| 2.4332 \| 10.486 \| 12.4671 \|
	\| 2.7065 \| 16.0 \| 2016 \| 2.4239 \| 10.6921 \| 12.414 \|
	\| 2.7065 \| 17.0 \| 2142 \| 2.4064 \| 10.7426 \| 12.4607 \|
	\| 2.7065 \| 18.0 \| 2268 \| 2.3941 \| 11.0509 \| 12.4087 \|
	\| 2.7065 \| 19.0 \| 2394 \| 2.3826 \| 11.2407 \| 12.3386 \|
	\| 2.603 \| 20.0 \| 2520 \| 2.3658 \| 11.3711 \| 12.3992 \|
	\| 2.603 \| 21.0 \| 2646 \| 2.3537 \| 11.42 \| 12.5032 \|
	\| 2.603 \| 22.0 \| 2772 \| 2.3475 \| 12.0665 \| 12.5074 \|
	\| 2.603 \| 23.0 \| 2898 \| 2.3398 \| 12.0343 \| 12.4342 \|
	\| 2.5192 \| 24.0 \| 3024 \| 2.3298 \| 12.1011 \| 12.5096 \|
	\| 2.5192 \| 25.0 \| 3150 \| 2.3216 \| 12.2562 \| 12.4809 \|
	\| 2.5192 \| 26.0 \| 3276 \| 2.3131 \| 12.4585 \| 12.4427 \|
	\| 2.5192 \| 27.0 \| 3402 \| 2.3052 \| 12.7094 \| 12.534 \|
	\| 2.4445 \| 28.0 \| 3528 \| 2.2984 \| 12.7432 \| 12.5053 \|
	\| 2.4445 \| 29.0 \| 3654 \| 2.2920 \| 12.8409 \| 12.4501 \|
	\| 2.4445 \| 30.0 \| 3780 \| 2.2869 \| 12.6365 \| 12.4936 \|
	\| 2.4445 \| 31.0 \| 3906 \| 2.2777 \| 12.8523 \| 12.5234 \|
	\| 2.3844 \| 32.0 \| 4032 \| 2.2788 \| 12.9216 \| 12.4204 \|
	\| 2.3844 \| 33.0 \| 4158 \| 2.2710 \| 12.9568 \| 12.5064 \|
	\| 2.3844 \| 34.0 \| 4284 \| 2.2643 \| 12.9641 \| 12.4299 \|
	\| 2.3844 \| 35.0 \| 4410 \| 2.2621 \| 12.9787 \| 12.448 \|
	\| 2.3282 \| 36.0 \| 4536 \| 2.2554 \| 13.1264 \| 12.4374 \|
	\| 2.3282 \| 37.0 \| 4662 \| 2.2481 \| 13.1853 \| 12.4416 \|
	\| 2.3282 \| 38.0 \| 4788 \| 2.2477 \| 13.3259 \| 12.4119 \|
	\| 2.3282 \| 39.0 \| 4914 \| 2.2448 \| 13.2017 \| 12.4278 \|
	\| 2.2842 \| 40.0 \| 5040 \| 2.2402 \| 13.3772 \| 12.4437 \|
	\| 2.2842 \| 41.0 \| 5166 \| 2.2373 \| 13.2184 \| 12.414 \|
	\| 2.2842 \| 42.0 \| 5292 \| 2.2357 \| 13.5267 \| 12.4342 \|
	\| 2.2842 \| 43.0 \| 5418 \| 2.2310 \| 13.5754 \| 12.4087 \|
	\| 2.2388 \| 44.0 \| 5544 \| 2.2244 \| 13.653 \| 12.4427 \|
	\| 2.2388 \| 45.0 \| 5670 \| 2.2243 \| 13.6028 \| 12.431 \|
	\| 2.2388 \| 46.0 \| 5796 \| 2.2216 \| 13.7128 \| 12.4151 \|
	\| 2.2388 \| 47.0 \| 5922 \| 2.2231 \| 13.749 \| 12.4172 \|
	\| 2.2067 \| 48.0 \| 6048 \| 2.2196 \| 13.7256 \| 12.4034 \|
	\| 2.2067 \| 49.0 \| 6174 \| 2.2125 \| 13.8237 \| 12.396 \|
	\| 2.2067 \| 50.0 \| 6300 \| 2.2131 \| 13.6642 \| 12.4416 \|
	\| 2.2067 \| 51.0 \| 6426 \| 2.2115 \| 13.8876 \| 12.4119 \|
	\| 2.1688 \| 52.0 \| 6552 \| 2.2091 \| 14.0323 \| 12.4639 \|
	\| 2.1688 \| 53.0 \| 6678 \| 2.2082 \| 13.916 \| 12.3843 \|
	\| 2.1688 \| 54.0 \| 6804 \| 2.2071 \| 13.924 \| 12.3758 \|
	\| 2.1688 \| 55.0 \| 6930 \| 2.2046 \| 13.9563 \| 12.4416 \|
	\| 2.1401 \| 56.0 \| 7056 \| 2.2020 \| 14.0592 \| 12.483 \|
	\| 2.1401 \| 57.0 \| 7182 \| 2.2047 \| 13.8879 \| 12.4076 \|
	\| 2.1401 \| 58.0 \| 7308 \| 2.2018 \| 13.9267 \| 12.3949 \|
	\| 2.1401 \| 59.0 \| 7434 \| 2.1964 \| 14.0518 \| 12.4363 \|
	\| 2.1092 \| 60.0 \| 7560 \| 2.1926 \| 14.1518 \| 12.4883 \|
	\| 2.1092 \| 61.0 \| 7686 \| 2.1972 \| 14.132 \| 12.4034 \|
	\| 2.1092 \| 62.0 \| 7812 \| 2.1939 \| 14.2066 \| 12.4151 \|
	\| 2.1092 \| 63.0 \| 7938 \| 2.1905 \| 14.2923 \| 12.4459 \|
	\| 2.0932 \| 64.0 \| 8064 \| 2.1932 \| 14.2476 \| 12.3418 \|
	\| 2.0932 \| 65.0 \| 8190 \| 2.1925 \| 14.2057 \| 12.3907 \|
	\| 2.0932 \| 66.0 \| 8316 \| 2.1906 \| 14.2978 \| 12.4055 \|
	\| 2.0932 \| 67.0 \| 8442 \| 2.1903 \| 14.3276 \| 12.4427 \|
	\| 2.0706 \| 68.0 \| 8568 \| 2.1918 \| 14.4681 \| 12.4034 \|
	\| 2.0706 \| 69.0 \| 8694 \| 2.1882 \| 14.3751 \| 12.4225 \|
	\| 2.0706 \| 70.0 \| 8820 \| 2.1870 \| 14.5904 \| 12.4204 \|
	\| 2.0706 \| 71.0 \| 8946 \| 2.1865 \| 14.6409 \| 12.4512 \|
	\| 2.0517 \| 72.0 \| 9072 \| 2.1831 \| 14.6505 \| 12.4352 \|
	\| 2.0517 \| 73.0 \| 9198 \| 2.1835 \| 14.7485 \| 12.4363 \|
	\| 2.0517 \| 74.0 \| 9324 \| 2.1824 \| 14.7344 \| 12.4586 \|
	\| 2.0517 \| 75.0 \| 9450 \| 2.1829 \| 14.8097 \| 12.4575 \|
	\| 2.0388 \| 76.0 \| 9576 \| 2.1822 \| 14.6681 \| 12.4108 \|
	\| 2.0388 \| 77.0 \| 9702 \| 2.1823 \| 14.6421 \| 12.4342 \|
	\| 2.0388 \| 78.0 \| 9828 \| 2.1816 \| 14.7014 \| 12.4459 \|
	\| 2.0388 \| 79.0 \| 9954 \| 2.1810 \| 14.744 \| 12.4565 \|
	\| 2.0224 \| 80.0 \| 10080 \| 2.1839 \| 14.7889 \| 12.4437 \|
	\| 2.0224 \| 81.0 \| 10206 \| 2.1793 \| 14.802 \| 12.4565 \|
	\| 2.0224 \| 82.0 \| 10332 \| 2.1776 \| 14.7702 \| 12.4214 \|
	\| 2.0224 \| 83.0 \| 10458 \| 2.1809 \| 14.6772 \| 12.4236 \|
	\| 2.0115 \| 84.0 \| 10584 \| 2.1786 \| 14.709 \| 12.4214 \|
	\| 2.0115 \| 85.0 \| 10710 \| 2.1805 \| 14.7693 \| 12.3981 \|
	\| 2.0115 \| 86.0 \| 10836 \| 2.1790 \| 14.7628 \| 12.4172 \|
	\| 2.0115 \| 87.0 \| 10962 \| 2.1785 \| 14.7538 \| 12.3992 \|
	\| 2.0007 \| 88.0 \| 11088 \| 2.1788 \| 14.7493 \| 12.3726 \|
	\| 2.0007 \| 89.0 \| 11214 \| 2.1788 \| 14.8793 \| 12.4045 \|
	\| 2.0007 \| 90.0 \| 11340 \| 2.1786 \| 14.8318 \| 12.3747 \|
	\| 2.0007 \| 91.0 \| 11466 \| 2.1769 \| 14.8061 \| 12.4013 \|
	\| 1.9967 \| 92.0 \| 11592 \| 2.1757 \| 14.8108 \| 12.3843 \|
	\| 1.9967 \| 93.0 \| 11718 \| 2.1747 \| 14.8036 \| 12.379 \|
	\| 1.9967 \| 94.0 \| 11844 \| 2.1764 \| 14.7447 \| 12.3737 \|
	\| 1.9967 \| 95.0 \| 11970 \| 2.1759 \| 14.7759 \| 12.3875 \|
	\| 1.9924 \| 96.0 \| 12096 \| 2.1760 \| 14.7695 \| 12.3875 \|
	\| 1.9924 \| 97.0 \| 12222 \| 2.1762 \| 14.8022 \| 12.3769 \|
	\| 1.9924 \| 98.0 \| 12348 \| 2.1763 \| 14.7519 \| 12.3822 \|
	\| 1.9924 \| 99.0 \| 12474 \| 2.1760 \| 14.7756 \| 12.3832 \|
	\| 1.9903 \| 100.0 \| 12600 \| 2.1761 \| 14.7713 \| 12.3822 \|

	### Evaluation results

	\| Data Split \| Bleu \|
	\|:----------:\|:-------:\|
	\| Validation \| 17.8061 \|
	\| Test \| 18.0863 \|

	### Framework versions

	- Transformers 4.20.0.dev0
	- Pytorch 1.8.0
	- Datasets 2.1.0
	- Tokenizers 0.12.1

	---
	tags:
	- translation
	- generated_from_trainer
	datasets:
	- cmu_hinglish_dog
	metrics:
	- bleu
	model-index:
	- name: t5-small_6_3-hi_en-to-en
	results:
	- task:
	name: Sequence-to-sequence Language Modeling
	type: text2text-generation
	dataset:
	name: cmu_hinglish_dog
	type: cmu_hinglish_dog
	args: hi_en-en
	metrics:
	- name: Bleu
	type: bleu
	value: 18.0863
	---

	<!-- 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. -->

	# t5-small_6_3-hi_en-to-en

	This model was trained from scratch on the cmu_hinglish_dog dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.3662
	- Bleu: 18.0863
	- Gen Len: 15.2708

	## Model description

	Model generated using:<br />
	```python make_student.py t5-small t5_small_6_3 6 3```<br />
	Check this [link](https://discuss.huggingface.co/t/questions-on-distilling-from-t5/1193/9) for more information.

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	Used cmu_hinglish_dog dataset. Please check this [link](https://huggingface.co/datasets/cmu_hinglish_dog) for dataset description

	## Translation:

	* Source: hi_en: The text in Hinglish
	* Target: en: The text in English



	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|
	\| No log \| 1.0 \| 126 \| 3.0601 \| 4.7146 \| 11.9904 \|
	\| No log \| 2.0 \| 252 \| 2.8885 \| 5.9584 \| 12.3418 \|
	\| No log \| 3.0 \| 378 \| 2.7914 \| 6.649 \| 12.3758 \|
	\| 3.4671 \| 4.0 \| 504 \| 2.7347 \| 7.3305 \| 12.3854 \|
	\| 3.4671 \| 5.0 \| 630 \| 2.6832 \| 8.3132 \| 12.4268 \|
	\| 3.4671 \| 6.0 \| 756 \| 2.6485 \| 8.339 \| 12.3641 \|
	\| 3.4671 \| 7.0 \| 882 \| 2.6096 \| 8.7269 \| 12.414 \|
	\| 3.0208 \| 8.0 \| 1008 \| 2.5814 \| 9.2163 \| 12.2675 \|
	\| 3.0208 \| 9.0 \| 1134 \| 2.5542 \| 9.448 \| 12.3875 \|
	\| 3.0208 \| 10.0 \| 1260 \| 2.5339 \| 9.9011 \| 12.4321 \|
	\| 3.0208 \| 11.0 \| 1386 \| 2.5043 \| 9.7529 \| 12.5149 \|
	\| 2.834 \| 12.0 \| 1512 \| 2.4848 \| 9.9606 \| 12.4193 \|
	\| 2.834 \| 13.0 \| 1638 \| 2.4737 \| 9.9368 \| 12.3673 \|
	\| 2.834 \| 14.0 \| 1764 \| 2.4458 \| 10.3182 \| 12.4352 \|
	\| 2.834 \| 15.0 \| 1890 \| 2.4332 \| 10.486 \| 12.4671 \|
	\| 2.7065 \| 16.0 \| 2016 \| 2.4239 \| 10.6921 \| 12.414 \|
	\| 2.7065 \| 17.0 \| 2142 \| 2.4064 \| 10.7426 \| 12.4607 \|
	\| 2.7065 \| 18.0 \| 2268 \| 2.3941 \| 11.0509 \| 12.4087 \|
	\| 2.7065 \| 19.0 \| 2394 \| 2.3826 \| 11.2407 \| 12.3386 \|
	\| 2.603 \| 20.0 \| 2520 \| 2.3658 \| 11.3711 \| 12.3992 \|
	\| 2.603 \| 21.0 \| 2646 \| 2.3537 \| 11.42 \| 12.5032 \|
	\| 2.603 \| 22.0 \| 2772 \| 2.3475 \| 12.0665 \| 12.5074 \|
	\| 2.603 \| 23.0 \| 2898 \| 2.3398 \| 12.0343 \| 12.4342 \|
	\| 2.5192 \| 24.0 \| 3024 \| 2.3298 \| 12.1011 \| 12.5096 \|
	\| 2.5192 \| 25.0 \| 3150 \| 2.3216 \| 12.2562 \| 12.4809 \|
	\| 2.5192 \| 26.0 \| 3276 \| 2.3131 \| 12.4585 \| 12.4427 \|
	\| 2.5192 \| 27.0 \| 3402 \| 2.3052 \| 12.7094 \| 12.534 \|
	\| 2.4445 \| 28.0 \| 3528 \| 2.2984 \| 12.7432 \| 12.5053 \|
	\| 2.4445 \| 29.0 \| 3654 \| 2.2920 \| 12.8409 \| 12.4501 \|
	\| 2.4445 \| 30.0 \| 3780 \| 2.2869 \| 12.6365 \| 12.4936 \|
	\| 2.4445 \| 31.0 \| 3906 \| 2.2777 \| 12.8523 \| 12.5234 \|
	\| 2.3844 \| 32.0 \| 4032 \| 2.2788 \| 12.9216 \| 12.4204 \|
	\| 2.3844 \| 33.0 \| 4158 \| 2.2710 \| 12.9568 \| 12.5064 \|
	\| 2.3844 \| 34.0 \| 4284 \| 2.2643 \| 12.9641 \| 12.4299 \|
	\| 2.3844 \| 35.0 \| 4410 \| 2.2621 \| 12.9787 \| 12.448 \|
	\| 2.3282 \| 36.0 \| 4536 \| 2.2554 \| 13.1264 \| 12.4374 \|
	\| 2.3282 \| 37.0 \| 4662 \| 2.2481 \| 13.1853 \| 12.4416 \|
	\| 2.3282 \| 38.0 \| 4788 \| 2.2477 \| 13.3259 \| 12.4119 \|
	\| 2.3282 \| 39.0 \| 4914 \| 2.2448 \| 13.2017 \| 12.4278 \|
	\| 2.2842 \| 40.0 \| 5040 \| 2.2402 \| 13.3772 \| 12.4437 \|
	\| 2.2842 \| 41.0 \| 5166 \| 2.2373 \| 13.2184 \| 12.414 \|
	\| 2.2842 \| 42.0 \| 5292 \| 2.2357 \| 13.5267 \| 12.4342 \|
	\| 2.2842 \| 43.0 \| 5418 \| 2.2310 \| 13.5754 \| 12.4087 \|
	\| 2.2388 \| 44.0 \| 5544 \| 2.2244 \| 13.653 \| 12.4427 \|
	\| 2.2388 \| 45.0 \| 5670 \| 2.2243 \| 13.6028 \| 12.431 \|
	\| 2.2388 \| 46.0 \| 5796 \| 2.2216 \| 13.7128 \| 12.4151 \|
	\| 2.2388 \| 47.0 \| 5922 \| 2.2231 \| 13.749 \| 12.4172 \|
	\| 2.2067 \| 48.0 \| 6048 \| 2.2196 \| 13.7256 \| 12.4034 \|
	\| 2.2067 \| 49.0 \| 6174 \| 2.2125 \| 13.8237 \| 12.396 \|
	\| 2.2067 \| 50.0 \| 6300 \| 2.2131 \| 13.6642 \| 12.4416 \|
	\| 2.2067 \| 51.0 \| 6426 \| 2.2115 \| 13.8876 \| 12.4119 \|
	\| 2.1688 \| 52.0 \| 6552 \| 2.2091 \| 14.0323 \| 12.4639 \|
	\| 2.1688 \| 53.0 \| 6678 \| 2.2082 \| 13.916 \| 12.3843 \|
	\| 2.1688 \| 54.0 \| 6804 \| 2.2071 \| 13.924 \| 12.3758 \|
	\| 2.1688 \| 55.0 \| 6930 \| 2.2046 \| 13.9563 \| 12.4416 \|
	\| 2.1401 \| 56.0 \| 7056 \| 2.2020 \| 14.0592 \| 12.483 \|
	\| 2.1401 \| 57.0 \| 7182 \| 2.2047 \| 13.8879 \| 12.4076 \|
	\| 2.1401 \| 58.0 \| 7308 \| 2.2018 \| 13.9267 \| 12.3949 \|
	\| 2.1401 \| 59.0 \| 7434 \| 2.1964 \| 14.0518 \| 12.4363 \|
	\| 2.1092 \| 60.0 \| 7560 \| 2.1926 \| 14.1518 \| 12.4883 \|
	\| 2.1092 \| 61.0 \| 7686 \| 2.1972 \| 14.132 \| 12.4034 \|
	\| 2.1092 \| 62.0 \| 7812 \| 2.1939 \| 14.2066 \| 12.4151 \|
	\| 2.1092 \| 63.0 \| 7938 \| 2.1905 \| 14.2923 \| 12.4459 \|
	\| 2.0932 \| 64.0 \| 8064 \| 2.1932 \| 14.2476 \| 12.3418 \|
	\| 2.0932 \| 65.0 \| 8190 \| 2.1925 \| 14.2057 \| 12.3907 \|
	\| 2.0932 \| 66.0 \| 8316 \| 2.1906 \| 14.2978 \| 12.4055 \|
	\| 2.0932 \| 67.0 \| 8442 \| 2.1903 \| 14.3276 \| 12.4427 \|
	\| 2.0706 \| 68.0 \| 8568 \| 2.1918 \| 14.4681 \| 12.4034 \|
	\| 2.0706 \| 69.0 \| 8694 \| 2.1882 \| 14.3751 \| 12.4225 \|
	\| 2.0706 \| 70.0 \| 8820 \| 2.1870 \| 14.5904 \| 12.4204 \|
	\| 2.0706 \| 71.0 \| 8946 \| 2.1865 \| 14.6409 \| 12.4512 \|
	\| 2.0517 \| 72.0 \| 9072 \| 2.1831 \| 14.6505 \| 12.4352 \|
	\| 2.0517 \| 73.0 \| 9198 \| 2.1835 \| 14.7485 \| 12.4363 \|
	\| 2.0517 \| 74.0 \| 9324 \| 2.1824 \| 14.7344 \| 12.4586 \|
	\| 2.0517 \| 75.0 \| 9450 \| 2.1829 \| 14.8097 \| 12.4575 \|
	\| 2.0388 \| 76.0 \| 9576 \| 2.1822 \| 14.6681 \| 12.4108 \|
	\| 2.0388 \| 77.0 \| 9702 \| 2.1823 \| 14.6421 \| 12.4342 \|
	\| 2.0388 \| 78.0 \| 9828 \| 2.1816 \| 14.7014 \| 12.4459 \|
	\| 2.0388 \| 79.0 \| 9954 \| 2.1810 \| 14.744 \| 12.4565 \|
	\| 2.0224 \| 80.0 \| 10080 \| 2.1839 \| 14.7889 \| 12.4437 \|
	\| 2.0224 \| 81.0 \| 10206 \| 2.1793 \| 14.802 \| 12.4565 \|
	\| 2.0224 \| 82.0 \| 10332 \| 2.1776 \| 14.7702 \| 12.4214 \|
	\| 2.0224 \| 83.0 \| 10458 \| 2.1809 \| 14.6772 \| 12.4236 \|
	\| 2.0115 \| 84.0 \| 10584 \| 2.1786 \| 14.709 \| 12.4214 \|
	\| 2.0115 \| 85.0 \| 10710 \| 2.1805 \| 14.7693 \| 12.3981 \|
	\| 2.0115 \| 86.0 \| 10836 \| 2.1790 \| 14.7628 \| 12.4172 \|
	\| 2.0115 \| 87.0 \| 10962 \| 2.1785 \| 14.7538 \| 12.3992 \|
	\| 2.0007 \| 88.0 \| 11088 \| 2.1788 \| 14.7493 \| 12.3726 \|
	\| 2.0007 \| 89.0 \| 11214 \| 2.1788 \| 14.8793 \| 12.4045 \|
	\| 2.0007 \| 90.0 \| 11340 \| 2.1786 \| 14.8318 \| 12.3747 \|
	\| 2.0007 \| 91.0 \| 11466 \| 2.1769 \| 14.8061 \| 12.4013 \|
	\| 1.9967 \| 92.0 \| 11592 \| 2.1757 \| 14.8108 \| 12.3843 \|
	\| 1.9967 \| 93.0 \| 11718 \| 2.1747 \| 14.8036 \| 12.379 \|
	\| 1.9967 \| 94.0 \| 11844 \| 2.1764 \| 14.7447 \| 12.3737 \|
	\| 1.9967 \| 95.0 \| 11970 \| 2.1759 \| 14.7759 \| 12.3875 \|
	\| 1.9924 \| 96.0 \| 12096 \| 2.1760 \| 14.7695 \| 12.3875 \|
	\| 1.9924 \| 97.0 \| 12222 \| 2.1762 \| 14.8022 \| 12.3769 \|
	\| 1.9924 \| 98.0 \| 12348 \| 2.1763 \| 14.7519 \| 12.3822 \|
	\| 1.9924 \| 99.0 \| 12474 \| 2.1760 \| 14.7756 \| 12.3832 \|
	\| 1.9903 \| 100.0 \| 12600 \| 2.1761 \| 14.7713 \| 12.3822 \|

	### Evaluation results

	\| Data Split \| Bleu \|
	\|:----------:\|:-------:\|
	\| Validation \| 17.8061 \|
	\| Test \| 18.0863 \|

	### Framework versions

	- Transformers 4.20.0.dev0
	- Pytorch 1.8.0
	- Datasets 2.1.0
	- Tokenizers 0.12.1