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	---
	tags:
	- generated_from_trainer
	datasets:
	- xsum
	metrics:
	- rouge
	model-index:
	- name: t5-small-finetuned_xsum
	results:
	- task:
	name: Sequence-to-sequence Language Modeling
	type: text2text-generation
	dataset:
	name: xsum
	type: xsum
	args: default
	metrics:
	- name: Rouge1
	type: rouge
	value: 34.0559
	---

	<!-- 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-finetuned_xsum

	This model is a fine-tuned version of [pki/t5-small-finetuned_xsum](https://huggingface.co/pki/t5-small-finetuned_xsum) on the xsum dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.0479
	- Rouge1: 34.0559
	- Rouge2: 12.7506
	- Rougel: 27.6762
	- Rougelsum: 27.68
	- Gen Len: 18.7924

	## 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: 2e-05
	- 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: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \|
	\|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\|
	\| 2.1176 \| 1.0 \| 12753 \| 2.0913 \| 33.1548 \| 11.8434 \| 26.7805 \| 26.7751 \| 18.7805 \|
	\| 2.1019 \| 2.0 \| 25506 \| 2.0875 \| 33.231 \| 11.9329 \| 26.8674 \| 26.861 \| 18.7992 \|
	\| 2.1044 \| 3.0 \| 38259 \| 2.0846 \| 33.3643 \| 11.9807 \| 26.9817 \| 26.9764 \| 18.773 \|
	\| 2.0874 \| 4.0 \| 51012 \| 2.0832 \| 33.3562 \| 12.0681 \| 27.0178 \| 27.0189 \| 18.7988 \|
	\| 2.0791 \| 5.0 \| 63765 \| 2.0803 \| 33.38 \| 12.081 \| 27.0368 \| 27.0344 \| 18.7844 \|
	\| 2.0894 \| 6.0 \| 76518 \| 2.0787 \| 33.2549 \| 11.9662 \| 26.8674 \| 26.8669 \| 18.7975 \|
	\| 2.0802 \| 7.0 \| 89271 \| 2.0777 \| 33.3978 \| 12.0828 \| 27.0461 \| 27.0443 \| 18.7757 \|
	\| 2.0719 \| 8.0 \| 102024 \| 2.0743 \| 33.4083 \| 12.1141 \| 27.0523 \| 27.0457 \| 18.7928 \|
	\| 2.0782 \| 9.0 \| 114777 \| 2.0748 \| 33.3673 \| 12.1637 \| 27.0696 \| 27.0663 \| 18.7902 \|
	\| 2.0736 \| 10.0 \| 127530 \| 2.0713 \| 33.5771 \| 12.2219 \| 27.1707 \| 27.1706 \| 18.7945 \|
	\| 2.0816 \| 11.0 \| 140283 \| 2.0703 \| 33.5099 \| 12.2069 \| 27.1822 \| 27.1835 \| 18.8002 \|
	\| 2.057 \| 12.0 \| 153036 \| 2.0693 \| 33.5853 \| 12.2427 \| 27.2096 \| 27.2109 \| 18.806 \|
	\| 2.0584 \| 13.0 \| 165789 \| 2.0676 \| 33.4883 \| 12.2674 \| 27.1582 \| 27.154 \| 18.7857 \|
	\| 2.0475 \| 14.0 \| 178542 \| 2.0662 \| 33.5529 \| 12.2765 \| 27.1897 \| 27.1901 \| 18.79 \|
	\| 2.0426 \| 15.0 \| 191295 \| 2.0643 \| 33.6543 \| 12.3545 \| 27.2946 \| 27.2928 \| 18.8036 \|
	\| 2.0373 \| 16.0 \| 204048 \| 2.0648 \| 33.6671 \| 12.349 \| 27.2649 \| 27.2707 \| 18.7905 \|
	\| 2.0178 \| 17.0 \| 216801 \| 2.0637 \| 33.6794 \| 12.4545 \| 27.3015 \| 27.3079 \| 18.7948 \|
	\| 2.0235 \| 18.0 \| 229554 \| 2.0626 \| 33.7635 \| 12.423 \| 27.3475 \| 27.3446 \| 18.7892 \|
	\| 2.0296 \| 19.0 \| 242307 \| 2.0622 \| 33.7574 \| 12.4651 \| 27.3879 \| 27.3882 \| 18.8134 \|
	\| 2.0319 \| 20.0 \| 255060 \| 2.0595 \| 33.9093 \| 12.5389 \| 27.5003 \| 27.5001 \| 18.7915 \|
	\| 2.0208 \| 21.0 \| 267813 \| 2.0583 \| 33.7875 \| 12.4912 \| 27.4243 \| 27.4332 \| 18.7982 \|
	\| 2.0151 \| 22.0 \| 280566 \| 2.0581 \| 33.8516 \| 12.4805 \| 27.46 \| 27.4647 \| 18.816 \|
	\| 2.0188 \| 23.0 \| 293319 \| 2.0575 \| 33.7744 \| 12.4548 \| 27.381 \| 27.382 \| 18.802 \|
	\| 2.0087 \| 24.0 \| 306072 \| 2.0579 \| 33.8953 \| 12.4984 \| 27.4675 \| 27.4727 \| 18.7819 \|
	\| 2.0186 \| 25.0 \| 318825 \| 2.0557 \| 33.7766 \| 12.4414 \| 27.4025 \| 27.4024 \| 18.8005 \|
	\| 2.0051 \| 26.0 \| 331578 \| 2.0555 \| 33.8973 \| 12.5796 \| 27.5338 \| 27.5339 \| 18.8153 \|
	\| 2.0024 \| 27.0 \| 344331 \| 2.0557 \| 33.8709 \| 12.5116 \| 27.4684 \| 27.4664 \| 18.7911 \|
	\| 1.9947 \| 28.0 \| 357084 \| 2.0545 \| 33.8499 \| 12.5242 \| 27.4677 \| 27.4716 \| 18.8025 \|
	\| 1.9931 \| 29.0 \| 369837 \| 2.0545 \| 33.7957 \| 12.5272 \| 27.4129 \| 27.4174 \| 18.8 \|
	\| 1.9826 \| 30.0 \| 382590 \| 2.0548 \| 33.9723 \| 12.6665 \| 27.5598 \| 27.5662 \| 18.7958 \|
	\| 1.999 \| 31.0 \| 395343 \| 2.0522 \| 33.9702 \| 12.6435 \| 27.5788 \| 27.579 \| 18.795 \|
	\| 1.9872 \| 32.0 \| 408096 \| 2.0525 \| 33.9546 \| 12.638 \| 27.5985 \| 27.5949 \| 18.7976 \|
	\| 1.991 \| 33.0 \| 420849 \| 2.0520 \| 33.9792 \| 12.6073 \| 27.5686 \| 27.5707 \| 18.8056 \|
	\| 2.0044 \| 34.0 \| 433602 \| 2.0504 \| 34.0736 \| 12.6511 \| 27.647 \| 27.6472 \| 18.8093 \|
	\| 1.9972 \| 35.0 \| 446355 \| 2.0513 \| 34.0506 \| 12.711 \| 27.6533 \| 27.6537 \| 18.7984 \|
	\| 1.9901 \| 36.0 \| 459108 \| 2.0504 \| 33.9991 \| 12.638 \| 27.626 \| 27.6272 \| 18.7996 \|
	\| 1.9742 \| 37.0 \| 471861 \| 2.0507 \| 33.9357 \| 12.6636 \| 27.5673 \| 27.5716 \| 18.8064 \|
	\| 1.984 \| 38.0 \| 484614 \| 2.0502 \| 33.9476 \| 12.6589 \| 27.58 \| 27.5813 \| 18.8037 \|
	\| 1.9864 \| 39.0 \| 497367 \| 2.0499 \| 34.0733 \| 12.7198 \| 27.6926 \| 27.6992 \| 18.8061 \|
	\| 1.9734 \| 40.0 \| 510120 \| 2.0492 \| 33.9483 \| 12.6486 \| 27.5571 \| 27.5598 \| 18.8033 \|
	\| 1.9895 \| 41.0 \| 522873 \| 2.0490 \| 33.9753 \| 12.684 \| 27.6058 \| 27.6086 \| 18.8011 \|
	\| 1.964 \| 42.0 \| 535626 \| 2.0487 \| 33.9528 \| 12.6376 \| 27.576 \| 27.5824 \| 18.7919 \|
	\| 1.9849 \| 43.0 \| 548379 \| 2.0487 \| 33.9868 \| 12.6936 \| 27.6116 \| 27.6158 \| 18.7966 \|
	\| 1.9798 \| 44.0 \| 561132 \| 2.0491 \| 34.0379 \| 12.7161 \| 27.6227 \| 27.6315 \| 18.7889 \|
	\| 1.9837 \| 45.0 \| 573885 \| 2.0473 \| 34.0046 \| 12.6559 \| 27.5931 \| 27.5988 \| 18.7996 \|
	\| 1.9556 \| 46.0 \| 586638 \| 2.0483 \| 34.0378 \| 12.712 \| 27.6346 \| 27.6446 \| 18.7942 \|
	\| 1.9844 \| 47.0 \| 599391 \| 2.0479 \| 34.0301 \| 12.7121 \| 27.6492 \| 27.6554 \| 18.7999 \|
	\| 1.9869 \| 48.0 \| 612144 \| 2.0474 \| 34.0463 \| 12.7151 \| 27.6542 \| 27.6604 \| 18.7919 \|
	\| 1.9851 \| 49.0 \| 624897 \| 2.0476 \| 34.0549 \| 12.7384 \| 27.6542 \| 27.6555 \| 18.7924 \|
	\| 1.9912 \| 50.0 \| 637650 \| 2.0479 \| 34.0559 \| 12.7506 \| 27.6762 \| 27.68 \| 18.7924 \|



	### Framework versions

	- Transformers 4.12.0.dev0
	- Pytorch 1.10.1
	- Datasets 1.14.0
	- Tokenizers 0.10.3