# 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""" from __future__ import absolute_import, division, print_function import glob import json import os 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" _URLs = { "trex": "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz", "squad": "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz", "google_re": "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz", "conceptnet": "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.""" my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) if self.config.name == "trex": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": [os.path.join(data_dir, "relations.jsonl")] + list(glob.glob(os.path.join(data_dir, "TREx", "*"))), "split": "train", }, ), ] elif self.config.name == "google_re": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": [ os.path.join(data_dir, *f.split("/")) for f in [ "Google_RE/date_of_birth_test.jsonl", "Google_RE/place_of_birth_test.jsonl", "Google_RE/place_of_death_test.jsonl", ] ], "split": "train", }, ), ] elif self.config.name == "conceptnet": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "ConceptNet", "test.jsonl"), "split": "train", }, ), ] elif self.config.name == "squad": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "Squad", "test.jsonl"), "split": "train", }, ), ] def _generate_examples(self, filepath, split): """ Yields examples from the LAMA dataset. """ if self.config.name == "trex": paths = filepath relations_path = paths[0] paths = paths[1:] 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 for filepath in paths: with open(filepath, encoding="utf-8") as f: 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 self.config.name == "conceptnet": id_ = -1 with open(filepath, encoding="utf-8") as f: 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(""), } elif self.config.name == "squad": id_ = -1 with open(filepath, encoding="utf-8") as f: 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), } elif self.config.name == "google_re": id_ = -1 paths = filepath for filepath in paths: # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py if "place_of_birth" in filepath: pred = { "relation": "place_of_birth", "template": "[X] was born in [Y] .", "template_negated": "[X] was not born in [Y] .", } elif "date_of_birth" in filepath: 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] .", } with open(filepath, encoding="utf-8") as f: 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"]), }