Datasets:
numeric_fused_head

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""NFH: Numeric Fused-Heads."""


	import csv
	import json

	import datasets


	_CITATION = """\
	@article{elazar_head,
	author = {Elazar, Yanai and Goldberg, Yoav},
	title = {Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {7},
	number = {},
	pages = {519-535},
	year = {2019},
	doi = {10.1162/tacl\\_a\\_00280},
	URL = {https://doi.org/10.1162/tacl_a_00280},
	}
	"""

	_DESCRIPTION = """\
	Fused Head constructions are noun phrases in which the head noun is \
	missing and is said to be "fused" with its dependent modifier. This \
	missing information is implicit and is important for sentence understanding.\
	The missing heads are easily filled in by humans, but pose a challenge for \
	computational models.

	For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a \
	Fused-Head, and the missing head is apples, which appear earlier in the sentence.

	This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).
	"""

	_HOMEPAGE = "https://nlp.biu.ac.il/~lazary/fh/"

	_LICENSE = "MIT"

	_URLs = {
	"identification": {
	"train": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/train.tsv",
	"test": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/test.tsv",
	"dev": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/dev.tsv",
	},
	"resolution": {
	"train": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_train.jsonl",
	"test": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_test.jsonl",
	"dev": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_dev.jsonl",
	},
	}


	class NumericFusedHead(datasets.GeneratorBasedBuilder):
	"""NFH: Numeric Fused-Heads"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="identification", description="Identify NFH anchors in a sentence"),
	datasets.BuilderConfig(name="resolution", description="Identify the head for the numeric anchor"),
	]

	def _info(self):
	if self.config.name == "identification":
	features = datasets.Features(
	{
	"tokens": datasets.Sequence(datasets.Value("string")),
	"start_index": datasets.Value("int32"),
	"end_index": datasets.Value("int32"),
	"label": datasets.features.ClassLabel(names=["neg", "pos"]),
	}
	)
	else:
	features = datasets.Features(
	{
	"tokens": datasets.Sequence(datasets.Value("string")),
	"line_indices": datasets.Sequence(datasets.Value("int32")),
	"head": datasets.Sequence(datasets.Value("string")),
	"speakers": datasets.Sequence(datasets.Value("string")),
	"anchors_indices": datasets.Sequence(datasets.Value("int32")),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_files = dl_manager.download_and_extract(_URLs[self.config.name])
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	if self.config.name == "identification":
	r = csv.DictReader(f, delimiter="\t")
	for id_, row in enumerate(r):
	data = {
	"tokens": row["text"].split("_SEP_"),
	"start_index": row["ind_s"],
	"end_index": row["ind_e"],
	"label": "neg" if row["y"] == "0" else "pos",
	}
	yield id_, data
	else:
	for id_, row in enumerate(f):
	data = json.loads(row)
	yield id_, {
	"tokens": data["tokens"],
	"line_indices": data["line_indices"],
	"head": [str(s) for s in data["head"]],
	"speakers": [str(s) for s in data["speakers"]],
	"anchors_indices": data["anchors_indices"],
	}