Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
text-simplification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
File size: 9,643 Bytes
b212506 dd1a88d b212506 59b1700 b212506 d02a874 b212506 d02a874 b212506 340e117 b212506 59b1700 b212506 59b1700 b212506 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{jiang-etal-2020-neural,
title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
author = "Jiang, Chao and
Maddela, Mounica and
Lan, Wuwei and
Zhong, Yang and
Xu, Wei",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.709",
doi = "10.18653/v1/2020.acl-main.709",
pages = "7943--7960",
}
"""
_DESCRIPTION = """\
WikiAuto provides a set of aligned sentences from English Wikipedia and Simple
English Wikipedia as a resource to train sentence simplification systems.
The authors first crowd-sourced a set of manual alignments between sentences in
a subset of the Simple English Wikipedia and their corresponding versions in
English Wikipedia (this corresponds to the manual config in this version of the
dataset), then trained a neural CRF system to predict these alignments.
The trained alignment prediction model was then applied to the other articles in
Simple English Wikipedia with an English counterpart to create a larger corpus
of aligned sentences (corresponding to the auto and auto_acl configs here).
"""
_URLs = {
"train": "train.tsv",
"validation": "valid.tsv",
"test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip",
"test_contract": "benchmarks/contract-benchmark.tsv",
"test_wiki": "benchmarks/wiki-benchmark.tsv",
}
# Add Asset files.
_URLs[
"test_asset_orig"
] = "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.orig"
for i in range(10):
_URLs[
f"test_asset_{i}"
] = f"https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.simp.{i}"
class WikiAuto(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_CONFIG_NAME = "wiki_auto_asset_turk"
def _info(self):
features = datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"source": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=datasets.info.SupervisedKeysData(
input="source", output="target"
),
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs)
challenge_sets = [
(
"challenge_train_sample",
"train_wiki_auto_asset_turk_RandomSample500.json",
),
(
"challenge_validation_sample",
"validation_wiki_auto_asset_turk_RandomSample500.json",
),
(
"challenge_test_asset_backtranslation",
"test_asset_wiki_auto_asset_turk_BackTranslation.json",
),
(
"challenge_test_asset_bfp02",
"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
),
(
"challenge_test_asset_bfp05",
"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
),
(
"challenge_test_asset_nopunc",
"test_asset_wiki_auto_asset_turk_WithoutPunctuation.json",
),
(
"challenge_test_turk_backtranslation",
"detok_test_turk_wiki_auto_asset_turk_BackTranslation.json",
),
(
"challenge_test_turk_bfp02",
"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
),
(
"challenge_test_turk_bfp05",
"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
),
(
"challenge_test_turk_nopunc",
"detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json",
),
]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": dl_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dl_dir["validation"],
"split": "validation",
},
),
datasets.SplitGenerator(
name="test_asset",
gen_kwargs={
"filepath": "",
"split": "test_asset",
"filepaths": [dl_dir["test_asset_orig"]]
+ [dl_dir[f"test_asset_{i}"] for i in range(10)],
},
),
datasets.SplitGenerator(
name="test_turk",
gen_kwargs={
"filepath": dl_dir["test_turk"],
"split": "test_turk",
},
),
datasets.SplitGenerator(
name="test_contract",
gen_kwargs={
"filepath": dl_dir["test_contract"],
"split": "test_contract",
},
),
datasets.SplitGenerator(
name="test_wiki",
gen_kwargs={
"filepath": dl_dir["test_wiki"],
"split": "test_wiki",
},
),
] + [
datasets.SplitGenerator(
name=challenge_split,
gen_kwargs={
"filepath": os.path.join(
dl_dir["challenge_set"], "wiki_auto_asset_turk", filename
),
"split": challenge_split,
},
)
for challenge_split, filename in challenge_sets
]
def _generate_examples(self, filepath, split, filepaths=None, lang=None):
"""Yields examples."""
if split in ["train", "validation"]:
keys = [
"source",
"target",
]
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
values = line.strip().split("\t")
assert (
len(values) == 2
), f"Not enough fields in ---- {line} --- {values}"
example = dict([(k, val) for k, val in zip(keys, values)])
example["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
example["gem_parent_id"] = example["gem_id"]
example["references"] = (
[] if split == "train" else [example["target"]]
)
yield id_, example
elif split == "test_turk":
examples = json.load(open(filepath, encoding="utf-8"))
for id_, example in enumerate(examples):
example["gem_parent_id"] = example["gem_id"]
for k in ["source_id", "target_id"]:
if k in example:
del example[k]
yield id_, example
elif split == "test_asset":
files = [open(f_name, encoding="utf-8") for f_name in filepaths]
for id_, lines in enumerate(zip(*files)):
yield id_, {
"gem_id": f"wiki_auto_asset_turk-{split}-{id_}",
"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}",
"target": lines[1].strip(),
"source": lines[0].strip(),
"references": [line.strip() for line in lines[1:]],
}
elif split == "test_wiki" or split == "test_contract":
with open(filepath, 'r') as f:
reader = csv.DictReader(f, delimiter="\t")
for id_, entry in enumerate(reader):
yield id_, {
"gem_id": f"wiki_auto_asset_turk-{split}-{id_}",
"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}",
"target": entry["simple"],
"source": entry["complex"],
"references": [entry["simple"]],
}
else:
exples = json.load(open(filepath, encoding="utf-8"))
if isinstance(exples, dict):
assert len(exples) == 1, "multiple entries found"
exples = list(exples.values())[0]
for id_, exple in enumerate(exples):
exple["gem_parent_id"] = exple["gem_id"]
exple["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
for k in ["source_id", "target_id"]:
if k in exple:
del exple[k]
yield id_, exple
|