Update README.md

338c78a over 2 years ago

8.04 kB

	---
	tags:
	- summarization
	- mT5
	language:
	- zh
	widget:
	- text: "專家稱維康桑格研究所(Wellcome Sanger Institute)的上述研究發現「令人震驚」而且「發人深省」。基因變異指關於我們身體成長和管理的相關指令，也就是DNA當中發生的變化。長期以來，變異一直被當作癌症的根源，但是數十年來關於變異是否對衰老有重要影響一直存在爭論。桑格研究所的研究人員說他們得到了「第一個試驗性證據」，證明了兩者的關係。他們分析了預期壽命各異的物種基因變異的不同速度。研究人員分析了貓、黑白疣猴、狗、雪貂、長頸鹿、馬、人、獅子、裸鼴鼠、兔子、老鼠、環尾狐猴和老虎等十幾種動物的DNA。發表在《自然》雜誌上的研究顯示，老鼠在短暫的生命當中每年經歷了將近800次變異，老鼠的壽命一般不到４年。"

	---

	---
	license: apache-2.0
	tags:
	- Summarization
	metrics:
	- rouge
	model-index:
	- name: best_model_test_0423_small
	results: []

	---
	# best_model_test_0423_small

	This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.6341
	- Rouge1: 18.7681
	- Rouge2: 6.3762
	- Rougel: 18.6081
	- Rougelsum: 18.6173
	- Gen Len: 22.1086

	## 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: 2
	- eval_batch_size: 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\|
	\| 5.8165 \| 0.05 \| 1000 \| 3.6541 \| 11.6734 \| 3.9865 \| 11.5734 \| 11.5375 \| 18.0056 \|
	\| 4.306 \| 0.1 \| 2000 \| 3.4291 \| 12.0417 \| 3.8419 \| 11.9231 \| 11.9223 \| 16.8948 \|
	\| 4.1091 \| 0.16 \| 3000 \| 3.3643 \| 13.661 \| 4.5171 \| 13.5123 \| 13.5076 \| 19.4016 \|
	\| 3.9637 \| 0.21 \| 4000 \| 3.2574 \| 13.8443 \| 4.1761 \| 13.689 \| 13.6927 \| 18.4288 \|
	\| 3.8205 \| 0.26 \| 5000 \| 3.2434 \| 13.5371 \| 4.3639 \| 13.3551 \| 13.3552 \| 21.5776 \|
	\| 3.7262 \| 0.31 \| 6000 \| 3.1690 \| 14.3668 \| 4.8048 \| 14.2191 \| 14.1906 \| 21.5548 \|
	\| 3.6887 \| 0.36 \| 7000 \| 3.0657 \| 14.3265 \| 4.436 \| 14.212 \| 14.205 \| 20.89 \|
	\| 3.6337 \| 0.42 \| 8000 \| 3.0318 \| 14.6809 \| 4.8345 \| 14.5378 \| 14.5331 \| 20.3651 \|
	\| 3.5443 \| 0.47 \| 9000 \| 3.0554 \| 15.3372 \| 4.9163 \| 15.1794 \| 15.1781 \| 21.7742 \|
	\| 3.5203 \| 0.52 \| 10000 \| 2.9793 \| 14.9278 \| 4.9656 \| 14.7491 \| 14.743 \| 20.8113 \|
	\| 3.4936 \| 0.57 \| 11000 \| 3.0079 \| 15.7705 \| 5.1453 \| 15.5582 \| 15.5756 \| 23.4274 \|
	\| 3.4592 \| 0.62 \| 12000 \| 2.9721 \| 15.0201 \| 5.1612 \| 14.8508 \| 14.8198 \| 22.7007 \|
	\| 3.377 \| 0.67 \| 13000 \| 3.0112 \| 15.9595 \| 5.1133 \| 15.78 \| 15.7774 \| 23.4427 \|
	\| 3.4158 \| 0.73 \| 14000 \| 2.9239 \| 14.7984 \| 5.051 \| 14.6943 \| 14.6581 \| 21.6009 \|
	\| 3.378 \| 0.78 \| 15000 \| 2.8897 \| 16.5128 \| 5.1923 \| 16.3523 \| 16.3265 \| 22.0828 \|
	\| 3.3231 \| 0.83 \| 16000 \| 2.9347 \| 16.9997 \| 5.5524 \| 16.8534 \| 16.8737 \| 22.5807 \|
	\| 3.3268 \| 0.88 \| 17000 \| 2.9116 \| 16.0261 \| 5.4226 \| 15.9234 \| 15.914 \| 23.6988 \|
	\| 3.3127 \| 0.93 \| 18000 \| 2.8610 \| 16.6255 \| 5.3554 \| 16.4729 \| 16.4569 \| 22.9481 \|
	\| 3.2664 \| 0.99 \| 19000 \| 2.8606 \| 17.7703 \| 5.9475 \| 17.6229 \| 17.6259 \| 23.4423 \|
	\| 3.1718 \| 1.04 \| 20000 \| 2.8764 \| 17.301 \| 5.6262 \| 17.122 \| 17.1104 \| 23.0093 \|
	\| 3.0987 \| 1.09 \| 21000 \| 2.8282 \| 16.4718 \| 5.2077 \| 16.3394 \| 16.3401 \| 20.9697 \|
	\| 3.1486 \| 1.14 \| 22000 \| 2.8235 \| 18.5594 \| 5.9469 \| 18.3882 \| 18.3799 \| 22.7291 \|
	\| 3.1435 \| 1.19 \| 23000 \| 2.8261 \| 18.111 \| 6.0309 \| 17.9593 \| 17.9613 \| 22.9612 \|
	\| 3.1049 \| 1.25 \| 24000 \| 2.8068 \| 17.124 \| 5.5675 \| 16.9714 \| 16.9876 \| 22.5558 \|
	\| 3.1357 \| 1.3 \| 25000 \| 2.8014 \| 17.3916 \| 5.8671 \| 17.2148 \| 17.2502 \| 23.0075 \|
	\| 3.0904 \| 1.35 \| 26000 \| 2.7790 \| 17.419 \| 5.6689 \| 17.3125 \| 17.3058 \| 22.1492 \|
	\| 3.0877 \| 1.4 \| 27000 \| 2.7462 \| 17.0605 \| 5.4735 \| 16.9414 \| 16.9378 \| 21.7522 \|
	\| 3.0694 \| 1.45 \| 28000 \| 2.7563 \| 17.752 \| 5.8889 \| 17.5967 \| 17.619 \| 23.2005 \|
	\| 3.0498 \| 1.51 \| 29000 \| 2.7521 \| 17.9056 \| 5.7754 \| 17.7624 \| 17.7836 \| 21.9369 \|
	\| 3.0566 \| 1.56 \| 30000 \| 2.7468 \| 18.6531 \| 6.0538 \| 18.5397 \| 18.5038 \| 22.2358 \|
	\| 3.0489 \| 1.61 \| 31000 \| 2.7450 \| 18.4869 \| 5.9297 \| 18.3139 \| 18.3169 \| 22.0108 \|
	\| 3.0247 \| 1.66 \| 32000 \| 2.7449 \| 18.5192 \| 5.9966 \| 18.3721 \| 18.3569 \| 22.2071 \|
	\| 2.9877 \| 1.71 \| 33000 \| 2.7160 \| 18.1655 \| 5.9294 \| 18.0304 \| 18.0836 \| 21.4595 \|
	\| 3.0383 \| 1.76 \| 34000 \| 2.7202 \| 18.4959 \| 6.2413 \| 18.3363 \| 18.3431 \| 22.9732 \|
	\| 3.041 \| 1.82 \| 35000 \| 2.6948 \| 17.5306 \| 5.8119 \| 17.4011 \| 17.4149 \| 21.9435 \|
	\| 2.9285 \| 1.87 \| 36000 \| 2.6957 \| 18.6418 \| 6.1394 \| 18.514 \| 18.4823 \| 22.5174 \|
	\| 3.0556 \| 1.92 \| 37000 \| 2.7000 \| 18.7387 \| 6.0585 \| 18.5761 \| 18.574 \| 22.9315 \|
	\| 3.0033 \| 1.97 \| 38000 \| 2.6974 \| 17.9387 \| 6.1387 \| 17.8271 \| 17.8111 \| 22.4726 \|
	\| 2.9207 \| 2.02 \| 39000 \| 2.6998 \| 18.6073 \| 6.1906 \| 18.3891 \| 18.4103 \| 23.0274 \|
	\| 2.8922 \| 2.08 \| 40000 \| 2.6798 \| 18.4017 \| 6.2244 \| 18.2321 \| 18.2296 \| 22.0697 \|
	\| 2.8938 \| 2.13 \| 41000 \| 2.6666 \| 18.8016 \| 6.2066 \| 18.6411 \| 18.6353 \| 21.7017 \|
	\| 2.9124 \| 2.18 \| 42000 \| 2.6606 \| 18.7544 \| 6.3533 \| 18.5923 \| 18.5739 \| 21.4303 \|
	\| 2.8597 \| 2.23 \| 43000 \| 2.6947 \| 18.8672 \| 6.4526 \| 18.7416 \| 18.7482 \| 22.3352 \|
	\| 2.8435 \| 2.28 \| 44000 \| 2.6738 \| 18.9405 \| 6.356 \| 18.7791 \| 18.7729 \| 21.9081 \|
	\| 2.8672 \| 2.34 \| 45000 \| 2.6734 \| 18.7509 \| 6.3991 \| 18.6175 \| 18.5828 \| 21.8869 \|
	\| 2.899 \| 2.39 \| 46000 \| 2.6575 \| 18.5529 \| 6.3489 \| 18.4139 \| 18.401 \| 21.7694 \|
	\| 2.8616 \| 2.44 \| 47000 \| 2.6485 \| 18.7563 \| 6.268 \| 18.6368 \| 18.6253 \| 21.5685 \|
	\| 2.8937 \| 2.49 \| 48000 \| 2.6486 \| 18.6525 \| 6.3426 \| 18.5184 \| 18.5129 \| 22.3337 \|
	\| 2.8446 \| 2.54 \| 49000 \| 2.6572 \| 18.6529 \| 6.2655 \| 18.4915 \| 18.4764 \| 22.3331 \|
	\| 2.8676 \| 2.59 \| 50000 \| 2.6608 \| 19.0913 \| 6.494 \| 18.929 \| 18.9233 \| 22.132 \|
	\| 2.8794 \| 2.65 \| 51000 \| 2.6583 \| 18.7648 \| 6.459 \| 18.6276 \| 18.6125 \| 22.2414 \|
	\| 2.8836 \| 2.7 \| 52000 \| 2.6512 \| 18.7243 \| 6.3865 \| 18.5848 \| 18.5763 \| 22.2551 \|
	\| 2.8174 \| 2.75 \| 53000 \| 2.6409 \| 18.9393 \| 6.3914 \| 18.7733 \| 18.7715 \| 22.1243 \|
	\| 2.8494 \| 2.8 \| 54000 \| 2.6396 \| 18.6126 \| 6.4389 \| 18.4673 \| 18.4516 \| 21.7638 \|
	\| 2.9025 \| 2.85 \| 55000 \| 2.6341 \| 18.7681 \| 6.3762 \| 18.6081 \| 18.6173 \| 22.1086 \|
	\| 2.8754 \| 2.91 \| 56000 \| 2.6388 \| 19.0828 \| 6.5203 \| 18.9334 \| 18.9285 \| 22.3497 \|
	\| 2.8489 \| 2.96 \| 57000 \| 2.6375 \| 18.9219 \| 6.4922 \| 18.763 \| 18.7437 \| 21.9321 \|


	### Framework versions

	- Transformers 4.18.0
	- Pytorch 1.10.1+cu113
	- Datasets 2.0.0
	- Tokenizers 0.11.6

	---
	tags:
	- summarization
	- mT5
	language:
	- zh
	widget:
	- text: "專家稱維康桑格研究所(Wellcome Sanger Institute)的上述研究發現「令人震驚」而且「發人深省」。基因變異指關於我們身體成長和管理的相關指令，也就是DNA當中發生的變化。長期以來，變異一直被當作癌症的根源，但是數十年來關於變異是否對衰老有重要影響一直存在爭論。桑格研究所的研究人員說他們得到了「第一個試驗性證據」，證明了兩者的關係。他們分析了預期壽命各異的物種基因變異的不同速度。研究人員分析了貓、黑白疣猴、狗、雪貂、長頸鹿、馬、人、獅子、裸鼴鼠、兔子、老鼠、環尾狐猴和老虎等十幾種動物的DNA。發表在《自然》雜誌上的研究顯示，老鼠在短暫的生命當中每年經歷了將近800次變異，老鼠的壽命一般不到４年。"

	---

	---
	license: apache-2.0
	tags:
	- Summarization
	metrics:
	- rouge
	model-index:
	- name: best_model_test_0423_small
	results: []

	---
	# best_model_test_0423_small

	This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.6341
	- Rouge1: 18.7681
	- Rouge2: 6.3762
	- Rougel: 18.6081
	- Rougelsum: 18.6173
	- Gen Len: 22.1086

	## 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: 2
	- eval_batch_size: 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\|
	\| 5.8165 \| 0.05 \| 1000 \| 3.6541 \| 11.6734 \| 3.9865 \| 11.5734 \| 11.5375 \| 18.0056 \|
	\| 4.306 \| 0.1 \| 2000 \| 3.4291 \| 12.0417 \| 3.8419 \| 11.9231 \| 11.9223 \| 16.8948 \|
	\| 4.1091 \| 0.16 \| 3000 \| 3.3643 \| 13.661 \| 4.5171 \| 13.5123 \| 13.5076 \| 19.4016 \|
	\| 3.9637 \| 0.21 \| 4000 \| 3.2574 \| 13.8443 \| 4.1761 \| 13.689 \| 13.6927 \| 18.4288 \|
	\| 3.8205 \| 0.26 \| 5000 \| 3.2434 \| 13.5371 \| 4.3639 \| 13.3551 \| 13.3552 \| 21.5776 \|
	\| 3.7262 \| 0.31 \| 6000 \| 3.1690 \| 14.3668 \| 4.8048 \| 14.2191 \| 14.1906 \| 21.5548 \|
	\| 3.6887 \| 0.36 \| 7000 \| 3.0657 \| 14.3265 \| 4.436 \| 14.212 \| 14.205 \| 20.89 \|
	\| 3.6337 \| 0.42 \| 8000 \| 3.0318 \| 14.6809 \| 4.8345 \| 14.5378 \| 14.5331 \| 20.3651 \|
	\| 3.5443 \| 0.47 \| 9000 \| 3.0554 \| 15.3372 \| 4.9163 \| 15.1794 \| 15.1781 \| 21.7742 \|
	\| 3.5203 \| 0.52 \| 10000 \| 2.9793 \| 14.9278 \| 4.9656 \| 14.7491 \| 14.743 \| 20.8113 \|
	\| 3.4936 \| 0.57 \| 11000 \| 3.0079 \| 15.7705 \| 5.1453 \| 15.5582 \| 15.5756 \| 23.4274 \|
	\| 3.4592 \| 0.62 \| 12000 \| 2.9721 \| 15.0201 \| 5.1612 \| 14.8508 \| 14.8198 \| 22.7007 \|
	\| 3.377 \| 0.67 \| 13000 \| 3.0112 \| 15.9595 \| 5.1133 \| 15.78 \| 15.7774 \| 23.4427 \|
	\| 3.4158 \| 0.73 \| 14000 \| 2.9239 \| 14.7984 \| 5.051 \| 14.6943 \| 14.6581 \| 21.6009 \|
	\| 3.378 \| 0.78 \| 15000 \| 2.8897 \| 16.5128 \| 5.1923 \| 16.3523 \| 16.3265 \| 22.0828 \|
	\| 3.3231 \| 0.83 \| 16000 \| 2.9347 \| 16.9997 \| 5.5524 \| 16.8534 \| 16.8737 \| 22.5807 \|
	\| 3.3268 \| 0.88 \| 17000 \| 2.9116 \| 16.0261 \| 5.4226 \| 15.9234 \| 15.914 \| 23.6988 \|
	\| 3.3127 \| 0.93 \| 18000 \| 2.8610 \| 16.6255 \| 5.3554 \| 16.4729 \| 16.4569 \| 22.9481 \|
	\| 3.2664 \| 0.99 \| 19000 \| 2.8606 \| 17.7703 \| 5.9475 \| 17.6229 \| 17.6259 \| 23.4423 \|
	\| 3.1718 \| 1.04 \| 20000 \| 2.8764 \| 17.301 \| 5.6262 \| 17.122 \| 17.1104 \| 23.0093 \|
	\| 3.0987 \| 1.09 \| 21000 \| 2.8282 \| 16.4718 \| 5.2077 \| 16.3394 \| 16.3401 \| 20.9697 \|
	\| 3.1486 \| 1.14 \| 22000 \| 2.8235 \| 18.5594 \| 5.9469 \| 18.3882 \| 18.3799 \| 22.7291 \|
	\| 3.1435 \| 1.19 \| 23000 \| 2.8261 \| 18.111 \| 6.0309 \| 17.9593 \| 17.9613 \| 22.9612 \|
	\| 3.1049 \| 1.25 \| 24000 \| 2.8068 \| 17.124 \| 5.5675 \| 16.9714 \| 16.9876 \| 22.5558 \|
	\| 3.1357 \| 1.3 \| 25000 \| 2.8014 \| 17.3916 \| 5.8671 \| 17.2148 \| 17.2502 \| 23.0075 \|
	\| 3.0904 \| 1.35 \| 26000 \| 2.7790 \| 17.419 \| 5.6689 \| 17.3125 \| 17.3058 \| 22.1492 \|
	\| 3.0877 \| 1.4 \| 27000 \| 2.7462 \| 17.0605 \| 5.4735 \| 16.9414 \| 16.9378 \| 21.7522 \|
	\| 3.0694 \| 1.45 \| 28000 \| 2.7563 \| 17.752 \| 5.8889 \| 17.5967 \| 17.619 \| 23.2005 \|
	\| 3.0498 \| 1.51 \| 29000 \| 2.7521 \| 17.9056 \| 5.7754 \| 17.7624 \| 17.7836 \| 21.9369 \|
	\| 3.0566 \| 1.56 \| 30000 \| 2.7468 \| 18.6531 \| 6.0538 \| 18.5397 \| 18.5038 \| 22.2358 \|
	\| 3.0489 \| 1.61 \| 31000 \| 2.7450 \| 18.4869 \| 5.9297 \| 18.3139 \| 18.3169 \| 22.0108 \|
	\| 3.0247 \| 1.66 \| 32000 \| 2.7449 \| 18.5192 \| 5.9966 \| 18.3721 \| 18.3569 \| 22.2071 \|
	\| 2.9877 \| 1.71 \| 33000 \| 2.7160 \| 18.1655 \| 5.9294 \| 18.0304 \| 18.0836 \| 21.4595 \|
	\| 3.0383 \| 1.76 \| 34000 \| 2.7202 \| 18.4959 \| 6.2413 \| 18.3363 \| 18.3431 \| 22.9732 \|
	\| 3.041 \| 1.82 \| 35000 \| 2.6948 \| 17.5306 \| 5.8119 \| 17.4011 \| 17.4149 \| 21.9435 \|
	\| 2.9285 \| 1.87 \| 36000 \| 2.6957 \| 18.6418 \| 6.1394 \| 18.514 \| 18.4823 \| 22.5174 \|
	\| 3.0556 \| 1.92 \| 37000 \| 2.7000 \| 18.7387 \| 6.0585 \| 18.5761 \| 18.574 \| 22.9315 \|
	\| 3.0033 \| 1.97 \| 38000 \| 2.6974 \| 17.9387 \| 6.1387 \| 17.8271 \| 17.8111 \| 22.4726 \|
	\| 2.9207 \| 2.02 \| 39000 \| 2.6998 \| 18.6073 \| 6.1906 \| 18.3891 \| 18.4103 \| 23.0274 \|
	\| 2.8922 \| 2.08 \| 40000 \| 2.6798 \| 18.4017 \| 6.2244 \| 18.2321 \| 18.2296 \| 22.0697 \|
	\| 2.8938 \| 2.13 \| 41000 \| 2.6666 \| 18.8016 \| 6.2066 \| 18.6411 \| 18.6353 \| 21.7017 \|
	\| 2.9124 \| 2.18 \| 42000 \| 2.6606 \| 18.7544 \| 6.3533 \| 18.5923 \| 18.5739 \| 21.4303 \|
	\| 2.8597 \| 2.23 \| 43000 \| 2.6947 \| 18.8672 \| 6.4526 \| 18.7416 \| 18.7482 \| 22.3352 \|
	\| 2.8435 \| 2.28 \| 44000 \| 2.6738 \| 18.9405 \| 6.356 \| 18.7791 \| 18.7729 \| 21.9081 \|
	\| 2.8672 \| 2.34 \| 45000 \| 2.6734 \| 18.7509 \| 6.3991 \| 18.6175 \| 18.5828 \| 21.8869 \|
	\| 2.899 \| 2.39 \| 46000 \| 2.6575 \| 18.5529 \| 6.3489 \| 18.4139 \| 18.401 \| 21.7694 \|
	\| 2.8616 \| 2.44 \| 47000 \| 2.6485 \| 18.7563 \| 6.268 \| 18.6368 \| 18.6253 \| 21.5685 \|
	\| 2.8937 \| 2.49 \| 48000 \| 2.6486 \| 18.6525 \| 6.3426 \| 18.5184 \| 18.5129 \| 22.3337 \|
	\| 2.8446 \| 2.54 \| 49000 \| 2.6572 \| 18.6529 \| 6.2655 \| 18.4915 \| 18.4764 \| 22.3331 \|
	\| 2.8676 \| 2.59 \| 50000 \| 2.6608 \| 19.0913 \| 6.494 \| 18.929 \| 18.9233 \| 22.132 \|
	\| 2.8794 \| 2.65 \| 51000 \| 2.6583 \| 18.7648 \| 6.459 \| 18.6276 \| 18.6125 \| 22.2414 \|
	\| 2.8836 \| 2.7 \| 52000 \| 2.6512 \| 18.7243 \| 6.3865 \| 18.5848 \| 18.5763 \| 22.2551 \|
	\| 2.8174 \| 2.75 \| 53000 \| 2.6409 \| 18.9393 \| 6.3914 \| 18.7733 \| 18.7715 \| 22.1243 \|
	\| 2.8494 \| 2.8 \| 54000 \| 2.6396 \| 18.6126 \| 6.4389 \| 18.4673 \| 18.4516 \| 21.7638 \|
	\| 2.9025 \| 2.85 \| 55000 \| 2.6341 \| 18.7681 \| 6.3762 \| 18.6081 \| 18.6173 \| 22.1086 \|
	\| 2.8754 \| 2.91 \| 56000 \| 2.6388 \| 19.0828 \| 6.5203 \| 18.9334 \| 18.9285 \| 22.3497 \|
	\| 2.8489 \| 2.96 \| 57000 \| 2.6375 \| 18.9219 \| 6.4922 \| 18.763 \| 18.7437 \| 21.9321 \|


	### Framework versions

	- Transformers 4.18.0
	- Pytorch 1.10.1+cu113
	- Datasets 2.0.0
	- Tokenizers 0.11.6