Datasets:

ekinakyurek
/

ftrace

	# 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
	"""The FTRACE benchmark."""


	import json
	import os
	import textwrap

	import datasets

	_FTRACE_CITATION = """\
	"""

	_FTRACE_DESCRIPTION = """\
	Factual Tracing Dataset that contains queries and abstracts, and their corresponding ground truth.
	"""

	_FTRACE_ABSTRACTS_DESCRIPTION = """\
	Abstracts based on TREx dataset.
	"""

	_FTRACE_ABSTRACTS_LICENSE = """\
	Creative Commons Attribution-ShareAlike 4.0 International License.
	see https://creativecommons.org/licenses/by-sa/4.0/"""

	_FTRACE_ABSTRACTS_CITATION = """\
	@inproceedings{elsahar2018t,
	title={T-rex: A large scale alignment of natural language with knowledge base triples},
	author={Elsahar, Hady and Vougiouklis, Pavlos and Remaci, Arslen and Gravier, Christophe and Hare, Jonathon and Laforest, Frederique and Simperl, Elena},
	booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
	year={2018}
	}"""

	_FTRACE_QUERIES_DESCRIPTION = """\
	Queries based on LAMA dataset.
	"""

	_FTRACE_QUERIES_CITATION = """\
	@inproceedings{petroni2019language,
	title={Language Models as Knowledge Bases?},
	author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
	booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
	year={2019}
	}"""

	FTRACE_QUERIES_LICENSE = """\
	The Creative Commons Attribution-Noncommercial 4.0 International License.
	see https://github.com/facebookresearch/LAMA/blob/master/LICENSE"""


	class FTRACEConfig(datasets.BuilderConfig):
	"""BuilderConfig for FTRACE."""

	def __init__(
	self,
	features,
	data_url,
	citation,
	license,
	url,
	**kwargs,
	):
	"""BuilderConfig for FTRACE.
	Args:
	features: `list[string]`, list of the features that will appear in the
	feature dict. Should not include "label".
	data_url: `string`, url to download the zip file from.
	citation: `string`, citation for the data set.
	url: `string`, url for information about the data set.
	**kwargs: keyword arguments forwarded to super.
	"""
	# Version history:
	# 0.0.2: Initial version.
	super(FTRACEConfig, self).__init__(
	version=datasets.Version("0.0.2"), **kwargs
	)
	self.features = features
	self.data_url = data_url
	self.citation = citation
	self.license = license
	self.url = url


	class FTRACE(datasets.GeneratorBasedBuilder):
	"""The SuperFTRACE benchmark."""

	BUILDER_CONFIGS = [
	FTRACEConfig(
	name="abstracts",
	description=_FTRACE_ABSTRACTS_DESCRIPTION,
	features=[
	"inputs_pretokenized",
	"targets_pretokenized",
	"masked_uri",
	"masked_type",
	"facts",
	"id",
	"example_uris",
	"page_uri",
	],
	data_url="https://people.csail.mit.edu/akyurek/ftrace/abstracts.zip",
	citation=textwrap.dedent(_FTRACE_ABSTRACTS_CITATION),
	license=_FTRACE_ABSTRACTS_LICENSE,
	url="https://hadyelsahar.github.io/t-rex/",
	),
	FTRACEConfig(
	name="queries",
	description=_FTRACE_QUERIES_DESCRIPTION,
	features=[
	"inputs_pretokenized",
	"targets_pretokenized",
	"uuid",
	"obj_uri",
	"sub_uri",
	"predicate_id",
	"sub_surface",
	"obj_surface",
	],
	data_url="https://people.csail.mit.edu/akyurek/ftrace/queries.zip",
	citation=textwrap.dedent(_FTRACE_QUERIES_CITATION),
	license=FTRACE_QUERIES_LICENSE,
	url="https://github.com/facebookresearch/LAMA",
	),
	]

	def _info(self):
	features = {
	feature: datasets.Value("string")
	for feature in self.config.features
	}

	return datasets.DatasetInfo(
	description=_FTRACE_DESCRIPTION + self.config.description,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + _FTRACE_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
	task_name = _get_task_name_from_data_url(self.config.data_url)
	dl_dir = os.path.join(dl_dir, task_name)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_file": os.path.join(dl_dir, "train.jsonl"),
	"split": "train",
	},
	),
	]

	def _generate_examples(self, data_file, split):
	with open(data_file, encoding="utf-8") as f:
	for idx, line in enumerate(f):
	row = json.loads(line)
	yield idx, row


	def _get_task_name_from_data_url(data_url):
	if "queries" in data_url:
	return "queries"
	elif "abstracts" in data_url:
	return "abstracts"

	return "queries"