Datasets:

vadis
/

sv-ident

	# 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,
	}