Datasets:
File size: 3,359 Bytes
3d8cd0a eee59a7 3d8cd0a 4190d81 3d8cd0a f65ec68 3d8cd0a e033a69 3d8cd0a 74f2d50 3d8cd0a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
"""xP3x-sample"""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@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}
}
"""
_DESCRIPTION = """\
A multilingual collection of Winograd Schemas in six languages \
that can be used for evaluation of cross-lingual commonsense reasoning capabilities.
"""
_DS = [
"apps.jsonl",
"bisect.jsonl",
"c3.jsonl",
"cmrc2018.jsonl",
"codecomplex.jsonl",
"code_contests.jsonl",
"code_docstring_corpus.jsonl",
"csl.jsonl",
"drcd.jsonl",
"flores.jsonl",
"github_jupyter_text_code_pairs.jsonl",
"great_code.jsonl",
"mbpp.jsonl",
"mlqa.jsonl",
"multi_eurlex.jsonl",
"neural_code_search.jsonl",
"paws_x.jsonl",
"python_state_changes.jsonl",
"tatoeba_mt.jsonl",
"tnews.jsonl",
"tydiqa_goldp.jsonl",
"tydiqa_primary.jsonl",
"wiki_lingua.jsonl",
"xcopa.jsonl",
"xlcost_text_to_code.jsonl",
"xlsum.jsonl",
"xlwic.jsonl",
"xnli.jsonl",
"xquad.jsonl",
]
_LICENSE = "Apache License 2.0"
_URL = "https://huggingface.co/datasets/Muennighoff/xP3x-sample/resolve/main/{ds}.jsonl"
_VERSION = datasets.Version("1.1.0", "")
class xP3x_sample(datasets.GeneratorBasedBuilder):
"""xP3x-sample"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=ds.replace(".jsonl", ""),
description=f'xP3x-sample {ds.replace(".jsonl", "")}',
version=_VERSION,
)
for ds in _DS
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"inputs": datasets.Value("string"),
"targets": datasets.Value("string"),
"language": datasets.Value("string"),
"split": datasets.Value("string"),
"template": datasets.Value("string"),
"dataset": datasets.Value("string"),
"config": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URL.format(ds=self.config.name))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={'filepath': downloaded_files}
)
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(eval(row))
yield id_, data
|