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# 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)
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