File size: 6,673 Bytes
9b3cf37 bc4b059 9b3cf37 e36bf23 9b3cf37 |
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 |
import csv
import os
import datasets
_CITATION = """\
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and
Pei, Jiaxin and
Wu, Hongtao and
Liu, Yiyu and
Li, Chenliang",
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.330",
pages = "3586--3596",
}
"""
_DESCRIPTION = """\
MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification,
question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to
inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the
merits held by MATINF.
"""
class MatinfConfig(datasets.BuilderConfig):
"""BuilderConfig for MATINF."""
def __init__(
self,
text_features,
label_column,
label_classes=None,
**kwargs,
):
"""BuilderConfig for MATINF.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
label_classes: `list[string]`, the list of classes if the label is
categorical. If not provided, then the label will be of type
`datasets.Value('float32')`.
**kwargs: keyword arguments forwarded to super.
"""
super(MatinfConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
class Matinf(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MatinfConfig(
name="age_classification",
text_features=["question", "description"],
label_column="class",
label_classes=["0-1岁", "1-2岁", "2-3岁"],
),
MatinfConfig(
name="topic_classification",
text_features=["question", "description"],
label_column="class",
label_classes=[
"产褥期保健",
"儿童过敏",
"动作发育",
"婴幼保健",
"婴幼心理",
"婴幼早教",
"婴幼期喂养",
"婴幼营养",
"孕期保健",
"家庭教育",
"幼儿园",
"未准父母",
"流产和不孕",
"疫苗接种",
"皮肤护理",
"宝宝上火",
"腹泻",
"婴幼常见病",
],
),
MatinfConfig(
name="summarization",
text_features=["description", "question"],
label_column=None,
),
MatinfConfig(
name="qa",
text_features=["question", "answer"],
label_column=None,
),
]
@property
def manual_download_instructions(self):
return (
"To use MATINF you have to download it manually. Please fill this google form ("
"https://forms.gle/nkH4LVE4iNQeDzsc9). You will receive a download link and a password once you "
"complete the form. Please extract all files in one folder and load the dataset with: "
"`datasets.load_dataset('matinf', data_dir='path/to/folder/folder_name')`"
)
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
if self.config.label_classes:
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
features["id"] = datasets.Value("int32")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
homepage="https://github.com/WHUIR/MATINF",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('matinf', data_dir=...)` that includes files unzipped from the MATINF zip. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(data_dir, "dev.csv")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
label_classes = self.config.label_classes
with open(filepath, encoding="utf8") as f:
reader = csv.DictReader(f)
for n, row in enumerate(reader):
example = {feat: row[feat] for feat in self.config.text_features}
example["id"] = row["id"]
if self.config.label_column:
label = row[self.config.label_column]
if label_classes and label not in label_classes:
continue # Split age/topic classification
example["label"] = label
# Filter out corrupted rows.
for value in example.values():
if value is None:
break
else:
yield example["id"], example
|