数据集:
bigscience/xP3all
xP3(跨语言公共提示池)是一个包含46种语言和16种自然语言处理任务的提示和数据集的集合。它用于训练BLOOMZ和mT0,这两个多语言语言模型能够零编程对几十种语言的人类指令进行跟随。
Name | Explanation | Example models |
---|---|---|
1234321 | Mixture of 17 tasks in 277 languages with English prompts | WIP - Join us at Project Aya @ 1235321 to help! |
1236321 | Mixture of 13 training tasks in 46 languages with English prompts | 1237321 & 1238321 |
1239321 | Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English) | 12310321 & 12311321 |
12312321 | xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts | |
12313321 | 12314321 processed version of xP3 | 1237321 |
12316321 | Repreprocessed version of the English-only 12317321 with 8 training tasks | 12318321 & 12319321 |
"train"的一个示例如下:
{ "inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?", "targets": "Yes" }
数据字段在所有拆分中都相同:
下表总结了每种语言的大小(从merged_{lang}.jsonl文件中计算得出)。由于诸如tw之类的语言只是来自Flores的单个句子翻译样本,因此它们的字节百分比明显低于样本百分比。
Language | Kilobytes | % | Samples | % |
---|---|---|---|---|
tw | 106288 | 0.11 | 265071 | 0.33 |
bm | 107056 | 0.11 | 265180 | 0.33 |
ak | 108096 | 0.11 | 265071 | 0.33 |
ca | 110608 | 0.11 | 271191 | 0.33 |
eu | 113008 | 0.11 | 281199 | 0.35 |
fon | 113072 | 0.11 | 265063 | 0.33 |
st | 114080 | 0.11 | 265063 | 0.33 |
ki | 115040 | 0.12 | 265180 | 0.33 |
tum | 116032 | 0.12 | 265063 | 0.33 |
wo | 122560 | 0.12 | 365063 | 0.45 |
ln | 126304 | 0.13 | 365060 | 0.45 |
as | 156256 | 0.16 | 265063 | 0.33 |
or | 161472 | 0.16 | 265063 | 0.33 |
kn | 165456 | 0.17 | 265063 | 0.33 |
ml | 175040 | 0.18 | 265864 | 0.33 |
rn | 192992 | 0.19 | 318189 | 0.39 |
nso | 229712 | 0.23 | 915051 | 1.13 |
tn | 235536 | 0.24 | 915054 | 1.13 |
lg | 235936 | 0.24 | 915021 | 1.13 |
rw | 249360 | 0.25 | 915043 | 1.13 |
ts | 250256 | 0.25 | 915044 | 1.13 |
sn | 252496 | 0.25 | 865056 | 1.07 |
xh | 254672 | 0.26 | 915058 | 1.13 |
zu | 263712 | 0.26 | 915061 | 1.13 |
ny | 272128 | 0.27 | 915063 | 1.13 |
ig | 325232 | 0.33 | 950097 | 1.17 |
yo | 352784 | 0.35 | 918416 | 1.13 |
ne | 393680 | 0.39 | 315754 | 0.39 |
pa | 523248 | 0.52 | 339210 | 0.42 |
gu | 560688 | 0.56 | 347499 | 0.43 |
sw | 566656 | 0.57 | 1130481 | 1.4 |
mr | 666240 | 0.67 | 417269 | 0.52 |
bn | 832720 | 0.83 | 428843 | 0.53 |
ta | 926912 | 0.93 | 415433 | 0.51 |
te | 1343232 | 1.35 | 584590 | 0.72 |
ur | 1918272 | 1.92 | 855756 | 1.06 |
vi | 3102512 | 3.11 | 1672106 | 2.07 |
code | 4330752 | 4.34 | 2707724 | 3.34 |
hi | 4403568 | 4.41 | 1554667 | 1.92 |
zh | 4599440 | 4.61 | 3589234 | 4.43 |
id | 4612256 | 4.62 | 2643418 | 3.27 |
ar | 4683456 | 4.69 | 2160181 | 2.67 |
fr | 6591120 | 6.6 | 5316403 | 6.57 |
pt | 6886800 | 6.9 | 3752156 | 4.63 |
es | 8587920 | 8.6 | 5413205 | 6.69 |
en | 39252528 | 39.33 | 32740750 | 40.44 |
total | 99807184 | 100.0 | 80956089 | 100.0 |
该数据集使用 Apache 2.0 许可发布。
@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} }
感谢 promptsource 的贡献者为此数据集添加了许多提示。