Datasets:
Tasks:
Text Classification
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 4,402 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Survey Variable Identification (SV-Ident) Corpus."""
import csv
import random
import datasets
# TODO: Add BibTeX citation
_CITATION = """\
@misc{sv-ident,
author={vadis-project},
title={SV-Ident},
year={2022},
url={https://github.com/vadis-project/sv-ident},
}
"""
_DESCRIPTION = """\
The SV-Ident corpus (version 0.3) is a collection of 4,248 expert-annotated English
and German sentences from social science publications, supporting the task of
multi-label text classification.
"""
_HOMEPAGE = "https://github.com/vadis-project/sv-ident"
# TODO: Add the licence
# _LICENSE = ""
_URL = "https://raw.githubusercontent.com/vadis-project/sv-ident/a8e71bba570f628c460e2b542d4cc645e4eb7d03/data/train/"
_URLS = {
"train": _URL+"train.tsv",
"dev": _URL+"val.tsv",
# "trial": "https://github.com/vadis-project/sv-ident/tree/9962c3274444ce84c59d42e2a6f8c0958ed15a26/data/trial",
}
class SVIdent(datasets.GeneratorBasedBuilder):
"""Survey Variable Identification (SV-Ident) Corpus."""
VERSION = datasets.Version("0.3.0")
def _info(self):
features = datasets.Features(
{
"sentence": datasets.Value("string"),
"is_variable": datasets.ClassLabel(names=["0", "1"]),
"variable": datasets.Sequence(datasets.Value(dtype="string")),
"research_data": datasets.Sequence(datasets.Value(dtype="string")),
"doc_id": datasets.Value("string"),
"uuid": datasets.Value("string"),
"lang": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("sentence", "is_variable"),
homepage=_HOMEPAGE,
# license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_files = dl_manager.download(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": downloaded_files["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": downloaded_files["dev"],
},
)
]
def _generate_examples(self, filepath):
"""Yields examples."""
data = []
with open(filepath, newline="", encoding="utf-8") as csvfile:
reader = csv.reader(csvfile, delimiter="\t")
next(reader, None) # skip the headers
for row in reader:
data.append(row)
seed = 42
random.seed(seed)
random.shuffle(data)
for id_, example in enumerate(data):
sentence = example[0]
is_variable = example[1]
variable = example[2] if example[2] != "" else []
if variable:
variable = variable.split(";") if ";" in variable else [variable]
research_data = example[3] if example[3] != "" else []
if research_data:
research_data = research_data.split(";") if ";" in research_data else [research_data]
doc_id = example[4]
uuid = example[5]
lang = example[6]
yield id_, {
"sentence": sentence,
"is_variable": is_variable,
"variable": variable,
"research_data": research_data,
"doc_id": doc_id,
"uuid": uuid,
"lang": lang,
}
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