albert-base-chinese-0407-ner / README.md

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	---
	language:
	- "List of ISO 639-1 code for your language"
	- zh
	widget:
	- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
	example_title: "範例ㄧ"
	- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
	example_title: "範例二"
	- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
	example_title: "範例三"

	---
	---
	license: gpl-3.0
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: albert-base-chinese-0407-ner
	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. -->

	# albert-base-chinese-0407-ner

	This model is a fine-tuned version of [ckiplab/albert-base-chinese](https://huggingface.co/ckiplab/albert-base-chinese) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0948
	- Precision: 0.8603
	- Recall: 0.8871
	- F1: 0.8735
	- Accuracy: 0.9704

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.3484 \| 0.05 \| 500 \| 0.5395 \| 0.1841 \| 0.1976 \| 0.1906 \| 0.8465 \|
	\| 0.3948 \| 0.09 \| 1000 \| 0.2910 \| 0.6138 \| 0.7113 \| 0.6590 \| 0.9263 \|
	\| 0.2388 \| 0.14 \| 1500 \| 0.2030 \| 0.6628 \| 0.7797 \| 0.7165 \| 0.9414 \|
	\| 0.1864 \| 0.18 \| 2000 \| 0.1729 \| 0.7490 \| 0.7935 \| 0.7706 \| 0.9498 \|
	\| 0.1754 \| 0.23 \| 2500 \| 0.1641 \| 0.7415 \| 0.7869 \| 0.7635 \| 0.9505 \|
	\| 0.1558 \| 0.28 \| 3000 \| 0.1532 \| 0.7680 \| 0.8002 \| 0.7838 \| 0.9530 \|
	\| 0.1497 \| 0.32 \| 3500 \| 0.1424 \| 0.7865 \| 0.8282 \| 0.8068 \| 0.9555 \|
	\| 0.1488 \| 0.37 \| 4000 \| 0.1373 \| 0.7887 \| 0.8111 \| 0.7997 \| 0.9553 \|
	\| 0.1361 \| 0.42 \| 4500 \| 0.1311 \| 0.7942 \| 0.8382 \| 0.8156 \| 0.9590 \|
	\| 0.1335 \| 0.46 \| 5000 \| 0.1264 \| 0.7948 \| 0.8423 \| 0.8179 \| 0.9596 \|
	\| 0.1296 \| 0.51 \| 5500 \| 0.1242 \| 0.8129 \| 0.8416 \| 0.8270 \| 0.9603 \|
	\| 0.1338 \| 0.55 \| 6000 \| 0.1315 \| 0.7910 \| 0.8588 \| 0.8235 \| 0.9586 \|
	\| 0.1267 \| 0.6 \| 6500 \| 0.1193 \| 0.8092 \| 0.8399 \| 0.8243 \| 0.9609 \|
	\| 0.1207 \| 0.65 \| 7000 \| 0.1205 \| 0.8021 \| 0.8469 \| 0.8239 \| 0.9601 \|
	\| 0.1214 \| 0.69 \| 7500 \| 0.1201 \| 0.7969 \| 0.8489 \| 0.8220 \| 0.9605 \|
	\| 0.1168 \| 0.74 \| 8000 \| 0.1134 \| 0.8087 \| 0.8607 \| 0.8339 \| 0.9620 \|
	\| 0.1162 \| 0.78 \| 8500 \| 0.1127 \| 0.8177 \| 0.8492 \| 0.8331 \| 0.9625 \|
	\| 0.1202 \| 0.83 \| 9000 \| 0.1283 \| 0.7986 \| 0.8550 \| 0.8259 \| 0.9580 \|
	\| 0.1135 \| 0.88 \| 9500 \| 0.1101 \| 0.8213 \| 0.8572 \| 0.8389 \| 0.9638 \|
	\| 0.1121 \| 0.92 \| 10000 \| 0.1097 \| 0.8190 \| 0.8588 \| 0.8384 \| 0.9635 \|
	\| 0.1091 \| 0.97 \| 10500 \| 0.1088 \| 0.8180 \| 0.8521 \| 0.8347 \| 0.9632 \|
	\| 0.1058 \| 1.02 \| 11000 \| 0.1085 \| 0.8136 \| 0.8716 \| 0.8416 \| 0.9630 \|
	\| 0.0919 \| 1.06 \| 11500 \| 0.1079 \| 0.8309 \| 0.8566 \| 0.8436 \| 0.9646 \|
	\| 0.0914 \| 1.11 \| 12000 \| 0.1079 \| 0.8423 \| 0.8542 \| 0.8482 \| 0.9656 \|
	\| 0.0921 \| 1.15 \| 12500 \| 0.1109 \| 0.8312 \| 0.8647 \| 0.8476 \| 0.9646 \|
	\| 0.0926 \| 1.2 \| 13000 \| 0.1240 \| 0.8413 \| 0.8488 \| 0.8451 \| 0.9637 \|
	\| 0.0914 \| 1.25 \| 13500 \| 0.1040 \| 0.8336 \| 0.8666 \| 0.8498 \| 0.9652 \|
	\| 0.0917 \| 1.29 \| 14000 \| 0.1032 \| 0.8352 \| 0.8707 \| 0.8526 \| 0.9662 \|
	\| 0.0928 \| 1.34 \| 14500 \| 0.1052 \| 0.8347 \| 0.8656 \| 0.8498 \| 0.9651 \|
	\| 0.0906 \| 1.38 \| 15000 \| 0.1032 \| 0.8399 \| 0.8619 \| 0.8507 \| 0.9662 \|
	\| 0.0903 \| 1.43 \| 15500 \| 0.1074 \| 0.8180 \| 0.8708 \| 0.8436 \| 0.9651 \|
	\| 0.0889 \| 1.48 \| 16000 \| 0.0990 \| 0.8367 \| 0.8713 \| 0.8537 \| 0.9670 \|
	\| 0.0914 \| 1.52 \| 16500 \| 0.1055 \| 0.8508 \| 0.8506 \| 0.8507 \| 0.9661 \|
	\| 0.0934 \| 1.57 \| 17000 \| 0.0979 \| 0.8326 \| 0.8740 \| 0.8528 \| 0.9669 \|
	\| 0.0898 \| 1.62 \| 17500 \| 0.1022 \| 0.8393 \| 0.8615 \| 0.8502 \| 0.9668 \|
	\| 0.0869 \| 1.66 \| 18000 \| 0.0962 \| 0.8484 \| 0.8762 \| 0.8621 \| 0.9682 \|
	\| 0.089 \| 1.71 \| 18500 \| 0.1008 \| 0.8447 \| 0.8714 \| 0.8579 \| 0.9674 \|
	\| 0.0927 \| 1.75 \| 19000 \| 0.0986 \| 0.8379 \| 0.8749 \| 0.8560 \| 0.9673 \|
	\| 0.0883 \| 1.8 \| 19500 \| 0.0965 \| 0.8518 \| 0.8749 \| 0.8632 \| 0.9688 \|
	\| 0.0965 \| 1.85 \| 20000 \| 0.0937 \| 0.8412 \| 0.8766 \| 0.8585 \| 0.9682 \|
	\| 0.0834 \| 1.89 \| 20500 \| 0.0920 \| 0.8451 \| 0.8862 \| 0.8652 \| 0.9687 \|
	\| 0.0817 \| 1.94 \| 21000 \| 0.0943 \| 0.8439 \| 0.8800 \| 0.8616 \| 0.9686 \|
	\| 0.088 \| 1.99 \| 21500 \| 0.0927 \| 0.8483 \| 0.8762 \| 0.8620 \| 0.9683 \|
	\| 0.0705 \| 2.03 \| 22000 \| 0.0993 \| 0.8525 \| 0.8783 \| 0.8652 \| 0.9690 \|
	\| 0.0709 \| 2.08 \| 22500 \| 0.0976 \| 0.8610 \| 0.8697 \| 0.8653 \| 0.9689 \|
	\| 0.0655 \| 2.12 \| 23000 \| 0.0997 \| 0.8585 \| 0.8665 \| 0.8625 \| 0.9683 \|
	\| 0.0656 \| 2.17 \| 23500 \| 0.0966 \| 0.8569 \| 0.8822 \| 0.8694 \| 0.9695 \|
	\| 0.0698 \| 2.22 \| 24000 \| 0.0955 \| 0.8604 \| 0.8775 \| 0.8689 \| 0.9696 \|
	\| 0.065 \| 2.26 \| 24500 \| 0.0971 \| 0.8614 \| 0.8780 \| 0.8696 \| 0.9697 \|
	\| 0.0653 \| 2.31 \| 25000 \| 0.0959 \| 0.8600 \| 0.8787 \| 0.8692 \| 0.9698 \|
	\| 0.0685 \| 2.35 \| 25500 \| 0.1001 \| 0.8610 \| 0.8710 \| 0.8659 \| 0.9690 \|
	\| 0.0684 \| 2.4 \| 26000 \| 0.0969 \| 0.8490 \| 0.8877 \| 0.8679 \| 0.9690 \|
	\| 0.0657 \| 2.45 \| 26500 \| 0.0954 \| 0.8532 \| 0.8832 \| 0.8680 \| 0.9696 \|
	\| 0.0668 \| 2.49 \| 27000 \| 0.0947 \| 0.8604 \| 0.8793 \| 0.8698 \| 0.9695 \|
	\| 0.0644 \| 2.54 \| 27500 \| 0.0989 \| 0.8527 \| 0.8790 \| 0.8656 \| 0.9696 \|
	\| 0.0685 \| 2.59 \| 28000 \| 0.0955 \| 0.8596 \| 0.8772 \| 0.8683 \| 0.9700 \|
	\| 0.0702 \| 2.63 \| 28500 \| 0.0937 \| 0.8585 \| 0.8837 \| 0.8709 \| 0.9700 \|
	\| 0.0644 \| 2.68 \| 29000 \| 0.0946 \| 0.8605 \| 0.8830 \| 0.8716 \| 0.9702 \|
	\| 0.065 \| 2.72 \| 29500 \| 0.0953 \| 0.8617 \| 0.8822 \| 0.8719 \| 0.9701 \|
	\| 0.063 \| 2.77 \| 30000 \| 0.0943 \| 0.8597 \| 0.8848 \| 0.8721 \| 0.9701 \|
	\| 0.0638 \| 2.82 \| 30500 \| 0.0941 \| 0.8619 \| 0.8846 \| 0.8731 \| 0.9702 \|
	\| 0.066 \| 2.86 \| 31000 \| 0.0942 \| 0.8608 \| 0.8847 \| 0.8726 \| 0.9701 \|
	\| 0.0589 \| 2.91 \| 31500 \| 0.0952 \| 0.8632 \| 0.8836 \| 0.8733 \| 0.9704 \|
	\| 0.0568 \| 2.95 \| 32000 \| 0.0948 \| 0.8603 \| 0.8871 \| 0.8735 \| 0.9704 \|


	### Framework versions

	- Transformers 4.18.0
	- Pytorch 1.10.0+cu111
	- Datasets 2.0.0
	- Tokenizers 0.11.6

	---
	language:
	- "List of ISO 639-1 code for your language"
	- zh
	widget:
	- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
	example_title: "範例ㄧ"
	- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
	example_title: "範例二"
	- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
	example_title: "範例三"

	---
	---
	license: gpl-3.0
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: albert-base-chinese-0407-ner
	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. -->

	# albert-base-chinese-0407-ner

	This model is a fine-tuned version of [ckiplab/albert-base-chinese](https://huggingface.co/ckiplab/albert-base-chinese) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0948
	- Precision: 0.8603
	- Recall: 0.8871
	- F1: 0.8735
	- Accuracy: 0.9704

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.3484 \| 0.05 \| 500 \| 0.5395 \| 0.1841 \| 0.1976 \| 0.1906 \| 0.8465 \|
	\| 0.3948 \| 0.09 \| 1000 \| 0.2910 \| 0.6138 \| 0.7113 \| 0.6590 \| 0.9263 \|
	\| 0.2388 \| 0.14 \| 1500 \| 0.2030 \| 0.6628 \| 0.7797 \| 0.7165 \| 0.9414 \|
	\| 0.1864 \| 0.18 \| 2000 \| 0.1729 \| 0.7490 \| 0.7935 \| 0.7706 \| 0.9498 \|
	\| 0.1754 \| 0.23 \| 2500 \| 0.1641 \| 0.7415 \| 0.7869 \| 0.7635 \| 0.9505 \|
	\| 0.1558 \| 0.28 \| 3000 \| 0.1532 \| 0.7680 \| 0.8002 \| 0.7838 \| 0.9530 \|
	\| 0.1497 \| 0.32 \| 3500 \| 0.1424 \| 0.7865 \| 0.8282 \| 0.8068 \| 0.9555 \|
	\| 0.1488 \| 0.37 \| 4000 \| 0.1373 \| 0.7887 \| 0.8111 \| 0.7997 \| 0.9553 \|
	\| 0.1361 \| 0.42 \| 4500 \| 0.1311 \| 0.7942 \| 0.8382 \| 0.8156 \| 0.9590 \|
	\| 0.1335 \| 0.46 \| 5000 \| 0.1264 \| 0.7948 \| 0.8423 \| 0.8179 \| 0.9596 \|
	\| 0.1296 \| 0.51 \| 5500 \| 0.1242 \| 0.8129 \| 0.8416 \| 0.8270 \| 0.9603 \|
	\| 0.1338 \| 0.55 \| 6000 \| 0.1315 \| 0.7910 \| 0.8588 \| 0.8235 \| 0.9586 \|
	\| 0.1267 \| 0.6 \| 6500 \| 0.1193 \| 0.8092 \| 0.8399 \| 0.8243 \| 0.9609 \|
	\| 0.1207 \| 0.65 \| 7000 \| 0.1205 \| 0.8021 \| 0.8469 \| 0.8239 \| 0.9601 \|
	\| 0.1214 \| 0.69 \| 7500 \| 0.1201 \| 0.7969 \| 0.8489 \| 0.8220 \| 0.9605 \|
	\| 0.1168 \| 0.74 \| 8000 \| 0.1134 \| 0.8087 \| 0.8607 \| 0.8339 \| 0.9620 \|
	\| 0.1162 \| 0.78 \| 8500 \| 0.1127 \| 0.8177 \| 0.8492 \| 0.8331 \| 0.9625 \|
	\| 0.1202 \| 0.83 \| 9000 \| 0.1283 \| 0.7986 \| 0.8550 \| 0.8259 \| 0.9580 \|
	\| 0.1135 \| 0.88 \| 9500 \| 0.1101 \| 0.8213 \| 0.8572 \| 0.8389 \| 0.9638 \|
	\| 0.1121 \| 0.92 \| 10000 \| 0.1097 \| 0.8190 \| 0.8588 \| 0.8384 \| 0.9635 \|
	\| 0.1091 \| 0.97 \| 10500 \| 0.1088 \| 0.8180 \| 0.8521 \| 0.8347 \| 0.9632 \|
	\| 0.1058 \| 1.02 \| 11000 \| 0.1085 \| 0.8136 \| 0.8716 \| 0.8416 \| 0.9630 \|
	\| 0.0919 \| 1.06 \| 11500 \| 0.1079 \| 0.8309 \| 0.8566 \| 0.8436 \| 0.9646 \|
	\| 0.0914 \| 1.11 \| 12000 \| 0.1079 \| 0.8423 \| 0.8542 \| 0.8482 \| 0.9656 \|
	\| 0.0921 \| 1.15 \| 12500 \| 0.1109 \| 0.8312 \| 0.8647 \| 0.8476 \| 0.9646 \|
	\| 0.0926 \| 1.2 \| 13000 \| 0.1240 \| 0.8413 \| 0.8488 \| 0.8451 \| 0.9637 \|
	\| 0.0914 \| 1.25 \| 13500 \| 0.1040 \| 0.8336 \| 0.8666 \| 0.8498 \| 0.9652 \|
	\| 0.0917 \| 1.29 \| 14000 \| 0.1032 \| 0.8352 \| 0.8707 \| 0.8526 \| 0.9662 \|
	\| 0.0928 \| 1.34 \| 14500 \| 0.1052 \| 0.8347 \| 0.8656 \| 0.8498 \| 0.9651 \|
	\| 0.0906 \| 1.38 \| 15000 \| 0.1032 \| 0.8399 \| 0.8619 \| 0.8507 \| 0.9662 \|
	\| 0.0903 \| 1.43 \| 15500 \| 0.1074 \| 0.8180 \| 0.8708 \| 0.8436 \| 0.9651 \|
	\| 0.0889 \| 1.48 \| 16000 \| 0.0990 \| 0.8367 \| 0.8713 \| 0.8537 \| 0.9670 \|
	\| 0.0914 \| 1.52 \| 16500 \| 0.1055 \| 0.8508 \| 0.8506 \| 0.8507 \| 0.9661 \|
	\| 0.0934 \| 1.57 \| 17000 \| 0.0979 \| 0.8326 \| 0.8740 \| 0.8528 \| 0.9669 \|
	\| 0.0898 \| 1.62 \| 17500 \| 0.1022 \| 0.8393 \| 0.8615 \| 0.8502 \| 0.9668 \|
	\| 0.0869 \| 1.66 \| 18000 \| 0.0962 \| 0.8484 \| 0.8762 \| 0.8621 \| 0.9682 \|
	\| 0.089 \| 1.71 \| 18500 \| 0.1008 \| 0.8447 \| 0.8714 \| 0.8579 \| 0.9674 \|
	\| 0.0927 \| 1.75 \| 19000 \| 0.0986 \| 0.8379 \| 0.8749 \| 0.8560 \| 0.9673 \|
	\| 0.0883 \| 1.8 \| 19500 \| 0.0965 \| 0.8518 \| 0.8749 \| 0.8632 \| 0.9688 \|
	\| 0.0965 \| 1.85 \| 20000 \| 0.0937 \| 0.8412 \| 0.8766 \| 0.8585 \| 0.9682 \|
	\| 0.0834 \| 1.89 \| 20500 \| 0.0920 \| 0.8451 \| 0.8862 \| 0.8652 \| 0.9687 \|
	\| 0.0817 \| 1.94 \| 21000 \| 0.0943 \| 0.8439 \| 0.8800 \| 0.8616 \| 0.9686 \|
	\| 0.088 \| 1.99 \| 21500 \| 0.0927 \| 0.8483 \| 0.8762 \| 0.8620 \| 0.9683 \|
	\| 0.0705 \| 2.03 \| 22000 \| 0.0993 \| 0.8525 \| 0.8783 \| 0.8652 \| 0.9690 \|
	\| 0.0709 \| 2.08 \| 22500 \| 0.0976 \| 0.8610 \| 0.8697 \| 0.8653 \| 0.9689 \|
	\| 0.0655 \| 2.12 \| 23000 \| 0.0997 \| 0.8585 \| 0.8665 \| 0.8625 \| 0.9683 \|
	\| 0.0656 \| 2.17 \| 23500 \| 0.0966 \| 0.8569 \| 0.8822 \| 0.8694 \| 0.9695 \|
	\| 0.0698 \| 2.22 \| 24000 \| 0.0955 \| 0.8604 \| 0.8775 \| 0.8689 \| 0.9696 \|
	\| 0.065 \| 2.26 \| 24500 \| 0.0971 \| 0.8614 \| 0.8780 \| 0.8696 \| 0.9697 \|
	\| 0.0653 \| 2.31 \| 25000 \| 0.0959 \| 0.8600 \| 0.8787 \| 0.8692 \| 0.9698 \|
	\| 0.0685 \| 2.35 \| 25500 \| 0.1001 \| 0.8610 \| 0.8710 \| 0.8659 \| 0.9690 \|
	\| 0.0684 \| 2.4 \| 26000 \| 0.0969 \| 0.8490 \| 0.8877 \| 0.8679 \| 0.9690 \|
	\| 0.0657 \| 2.45 \| 26500 \| 0.0954 \| 0.8532 \| 0.8832 \| 0.8680 \| 0.9696 \|
	\| 0.0668 \| 2.49 \| 27000 \| 0.0947 \| 0.8604 \| 0.8793 \| 0.8698 \| 0.9695 \|
	\| 0.0644 \| 2.54 \| 27500 \| 0.0989 \| 0.8527 \| 0.8790 \| 0.8656 \| 0.9696 \|
	\| 0.0685 \| 2.59 \| 28000 \| 0.0955 \| 0.8596 \| 0.8772 \| 0.8683 \| 0.9700 \|
	\| 0.0702 \| 2.63 \| 28500 \| 0.0937 \| 0.8585 \| 0.8837 \| 0.8709 \| 0.9700 \|
	\| 0.0644 \| 2.68 \| 29000 \| 0.0946 \| 0.8605 \| 0.8830 \| 0.8716 \| 0.9702 \|
	\| 0.065 \| 2.72 \| 29500 \| 0.0953 \| 0.8617 \| 0.8822 \| 0.8719 \| 0.9701 \|
	\| 0.063 \| 2.77 \| 30000 \| 0.0943 \| 0.8597 \| 0.8848 \| 0.8721 \| 0.9701 \|
	\| 0.0638 \| 2.82 \| 30500 \| 0.0941 \| 0.8619 \| 0.8846 \| 0.8731 \| 0.9702 \|
	\| 0.066 \| 2.86 \| 31000 \| 0.0942 \| 0.8608 \| 0.8847 \| 0.8726 \| 0.9701 \|
	\| 0.0589 \| 2.91 \| 31500 \| 0.0952 \| 0.8632 \| 0.8836 \| 0.8733 \| 0.9704 \|
	\| 0.0568 \| 2.95 \| 32000 \| 0.0948 \| 0.8603 \| 0.8871 \| 0.8735 \| 0.9704 \|


	### Framework versions

	- Transformers 4.18.0
	- Pytorch 1.10.0+cu111
	- Datasets 2.0.0
	- Tokenizers 0.11.6