Datasets:
lama

	# 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.
	"""The LAMA Dataset"""


	import json
	from fnmatch import fnmatch

	import datasets


	_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}
	}
	@inproceedings{petroni2020how,
	title={How Context Affects Language Models' Factual Predictions},
	author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
	booktitle={Automated Knowledge Base Construction},
	year={2020},
	url={https://openreview.net/forum?id=025X0zPfn}
	}
	"""


	_DESCRIPTION = """LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA.
	"""

	_HOMEPAGE = "https://github.com/facebookresearch/LAMA"

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

	_RELATIONS_URL = "https://s3.amazonaws.com/datasets.huggingface.co/lama/relations.jsonl"

	_DATA_URL = "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz"


	class Lama(datasets.GeneratorBasedBuilder):
	"""Lama Dataset"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="trex", version=VERSION, description="The TRex part of the Lama dataset"),
	datasets.BuilderConfig(name="squad", version=VERSION, description="The Squad part of the Lama dataset"),
	datasets.BuilderConfig(
	name="google_re", version=VERSION, description="The Google_re part of the Lama dataset"
	),
	datasets.BuilderConfig(
	name="conceptnet", version=VERSION, description="The Conceptnet part of the Lama dataset"
	),
	]

	DEFAULT_CONFIG_NAME = "trex"

	def _info(self):
	if self.config.name == "trex":
	features = datasets.Features(
	{
	"uuid": datasets.Value("string"),
	"obj_uri": datasets.Value("string"),
	"obj_label": datasets.Value("string"),
	"sub_uri": datasets.Value("string"),
	"sub_label": datasets.Value("string"),
	"predicate_id": datasets.Value("string"),
	"sub_surface": datasets.Value("string"),
	"obj_surface": datasets.Value("string"),
	"masked_sentence": datasets.Value("string"),
	"template": datasets.Value("string"),
	"template_negated": datasets.Value("string"),
	"label": datasets.Value("string"),
	"description": datasets.Value("string"),
	"type": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)
	elif self.config.name == "conceptnet":
	features = datasets.Features(
	{
	"uuid": datasets.Value("string"),
	"sub": datasets.Value("string"),
	"obj": datasets.Value("string"),
	"pred": datasets.Value("string"),
	"obj_label": datasets.Value("string"),
	"masked_sentence": datasets.Value("string"),
	"negated": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)
	elif self.config.name == "squad":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"sub_label": datasets.Value("string"),
	"obj_label": datasets.Value("string"),
	"negated": datasets.Value("string"),
	"masked_sentence": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)
	elif self.config.name == "google_re":
	features = datasets.Features(
	{
	"pred": datasets.Value("string"),
	"sub": datasets.Value("string"),
	"obj": datasets.Value("string"),
	"evidences": datasets.Value("string"),
	"judgments": datasets.Value("string"),
	"sub_w": datasets.Value("string"),
	"sub_label": datasets.Value("string"),
	"sub_aliases": datasets.Value("string"),
	"obj_w": datasets.Value("string"),
	"obj_label": datasets.Value("string"),
	"obj_aliases": datasets.Value("string"),
	"uuid": datasets.Value("string"),
	"masked_sentence": datasets.Value("string"),
	"template": datasets.Value("string"),
	"template_negated": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	archive = dl_manager.download(_DATA_URL)
	if self.config.name == "trex":
	relations_path = dl_manager.download(_RELATIONS_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": ["TREx/*"],
	"files": dl_manager.iter_archive(archive),
	"relations_path": relations_path,
	},
	),
	]
	elif self.config.name == "google_re":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": [
	"Google_RE/date_of_birth_test.jsonl",
	"Google_RE/place_of_birth_test.jsonl",
	"Google_RE/place_of_death_test.jsonl",
	],
	"files": dl_manager.iter_archive(archive),
	},
	),
	]
	elif self.config.name == "conceptnet":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": ["ConceptNet/test.jsonl"],
	"files": dl_manager.iter_archive(archive),
	},
	),
	]
	elif self.config.name == "squad":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": ["Squad/test.jsonl"],
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, filepaths, files, relations_path=None):
	"""Yields examples from the LAMA dataset."""
	filepaths = list(filepaths)
	if self.config.name == "trex":
	all_rels = {}
	with open(relations_path, encoding="utf-8") as f:
	for row in f:
	data = json.loads(row)
	all_rels[data["relation"]] = data
	id_ = -1
	inside_trec_directory = False
	for path, f in files:
	if any(fnmatch(path, pattern) for pattern in filepaths):
	inside_trec_directory = True
	for row in f:
	data = json.loads(row)
	pred = all_rels.get(data["predicate_id"], {})
	for evidences in data["evidences"]:
	id_ += 1
	yield id_, {
	"uuid": str(data["uuid"]),
	"obj_uri": str(data["obj_uri"]),
	"obj_label": str(data["obj_label"]),
	"sub_uri": str(data["sub_uri"]),
	"sub_label": str(data["sub_label"]),
	"predicate_id": str(data["predicate_id"]),
	"sub_surface": str(evidences["sub_surface"]),
	"obj_surface": str(evidences["obj_surface"]),
	"masked_sentence": str(evidences["masked_sentence"]),
	"template": str(pred.get("template", "")),
	"template_negated": str(pred.get("template_negated", "")),
	"label": str(pred.get("label", "")),
	"description": str(pred.get("description", "")),
	"type": str(pred.get("type", "")),
	}
	elif inside_trec_directory:
	break
	elif self.config.name == "conceptnet":
	id_ = -1
	for path, f in files:
	if not filepaths:
	break
	if path in list(filepaths):
	for row in f:
	data = json.loads(row)
	if data.get("negated") is not None:
	for masked_sentence, negated in zip(data["masked_sentences"], data["negated"]):
	id_ += 1
	yield id_, {
	"uuid": str(data["uuid"]),
	"sub": str(data.get("sub", "")),
	"obj": str(data.get("obj", "")),
	"pred": str(data["pred"]),
	"obj_label": str(data["obj_label"]),
	"masked_sentence": str(masked_sentence),
	"negated": str(negated),
	}
	else:
	for masked_sentence in data["masked_sentences"]:
	id_ += 1
	yield id_, {
	"uuid": str(data["uuid"]),
	"sub": str(data.get("sub", "")),
	"obj": str(data.get("obj", "")),
	"pred": str(data["pred"]),
	"obj_label": str(data["obj_label"]),
	"masked_sentence": str(masked_sentence),
	"negated": str(""),
	}
	filepaths.remove(path)
	elif self.config.name == "squad":
	id_ = -1
	for path, f in files:
	if not filepaths:
	break
	if path in filepaths:
	for row in f:
	data = json.loads(row)
	for masked_sentence in data["masked_sentences"]:
	id_ += 1
	yield id_, {
	"id": str(data["id"]),
	"sub_label": str(data["sub_label"]),
	"obj_label": str(data["obj_label"]),
	"negated": str(data.get("negated", "")),
	"masked_sentence": str(masked_sentence),
	}
	filepaths.remove(path)
	elif self.config.name == "google_re":
	id_ = -1
	for path, f in files:
	if path in filepaths:
	if not filepaths:
	break
	if path in filepaths:
	# from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py
	if "place_of_birth" in path:
	pred = {
	"relation": "place_of_birth",
	"template": "[X] was born in [Y] .",
	"template_negated": "[X] was not born in [Y] .",
	}
	elif "date_of_birth" in path:
	pred = {
	"relation": "date_of_birth",
	"template": "[X] (born [Y]).",
	"template_negated": "[X] (not born [Y]).",
	}
	else:
	pred = {
	"relation": "place_of_death",
	"template": "[X] died in [Y] .",
	"template_negated": "[X] did not die in [Y] .",
	}
	for row in f:
	data = json.loads(row)
	for masked_sentence in data["masked_sentences"]:
	id_ += 1
	yield id_, {
	"pred": str(data["pred"]),
	"sub": str(data["sub"]),
	"obj": str(data["obj"]),
	"evidences": str(data["evidences"]),
	"judgments": str(data["judgments"]),
	"sub_w": str(data["sub_w"]),
	"sub_label": str(data["sub_label"]),
	"sub_aliases": str(data["sub_aliases"]),
	"obj_w": str(data["obj_w"]),
	"obj_label": str(data["obj_label"]),
	"obj_aliases": str(data["obj_aliases"]),
	"uuid": str(data["uuid"]),
	"masked_sentence": str(masked_sentence),
	"template": str(pred["template"]),
	"template_negated": str(pred["template_negated"]),
	}
	filepaths.remove(path)