# coding=utf-8 # Copyright 2022 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 Google-IISc Distant Supervision (GIDS) dataset for distantly-supervised relation extraction""" import csv import datasets _CITATION = """\ @inproceedings{bassignana-plank-2022-crossre, title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction", author = "Bassignana, Elisa and Plank, Barbara", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", year = "2022", publisher = "Association for Computational Linguistics" } """ _DESCRIPTION = """\ Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction. GIDS is seeded from the human-judged Google relation extraction corpus. """ _HOMEPAGE = "" _LICENSE = "" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "train": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/train.tsv", "validation": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/dev.tsv", "test": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/test.tsv", } _VERSION = datasets.Version("1.0.0") _CLASS_LABELS = [ "NA", "/people/person/education./education/education/institution", "/people/person/education./education/education/degree", "/people/person/place_of_birth", "/people/deceased_person/place_of_death" ] def replace_underscore_in_span(text, start, end): cleaned_text = text[:start] + text[start:end].replace("_", " ") + text[end:] return cleaned_text class GIDS(datasets.GeneratorBasedBuilder): """Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction.""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name="gids", version=_VERSION, description="GIDS dataset." ), datasets.BuilderConfig( name="gids_formatted", version=_VERSION, description="Formatted GIDS dataset." ), ] DEFAULT_CONFIG_NAME = "gids" # type: ignore def _info(self): if self.config.name == "gids_formatted": features = datasets.Features( { "token": datasets.Sequence(datasets.Value("string")), "subj_start": datasets.Value("int32"), "subj_end": datasets.Value("int32"), "obj_start": datasets.Value("int32"), "obj_end": datasets.Value("int32"), "relation": datasets.ClassLabel(names=_CLASS_LABELS), } ) else: features = datasets.Features( { "sentence": datasets.Value("string"), "subj_id": datasets.Value("string"), "obj_id": datasets.Value("string"), "subj_text": datasets.Value("string"), "obj_text": datasets.Value("string"), "relation": datasets.ClassLabel(names=_CLASS_LABELS) } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive downloaded_files = dl_manager.download_and_extract(_URLs) return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]}) for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] def _generate_examples(self, filepath): """Yields examples.""" # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. # It is in charge of opening the given file and yielding (key, example) tuples from the dataset # The key is not important, it's more here for legacy reason (legacy from tfds) if self.config.name == "gids_formatted": from spacy.lang.en import English word_splitter = English() else: word_splitter = None with open(filepath, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t") for id_, example in enumerate(data): text = example[5].strip()[:-9].strip() # remove '###END###' from text, subj_text = example[2] obj_text = example[3] rel_type = example[4] if self.config.name == "gids_formatted": subj_char_start = text.find(subj_text) assert subj_char_start != -1, f"Did not find <{subj_text}> in the text" subj_char_end = subj_char_start + len(subj_text) obj_char_start = text.find(obj_text) assert obj_char_start != -1, f"Did not find <{obj_text}> in the text" obj_char_end = obj_char_start + len(obj_text) text = replace_underscore_in_span(text, subj_char_start, subj_char_end) text = replace_underscore_in_span(text, obj_char_start, obj_char_end) doc = word_splitter(text) word_tokens = [t.text for t in doc] subj_span = doc.char_span(subj_char_start, subj_char_end, alignment_mode="expand") obj_span = doc.char_span(obj_char_start, obj_char_end, alignment_mode="expand") yield id_, { "token": word_tokens, "subj_start": subj_span.start, "subj_end": subj_span.end, "obj_start": obj_span.start, "obj_end": obj_span.end, "relation": rel_type, } else: yield id_, { "sentence": text, "subj_id": example[0], "obj_id": example[1], "subj_text": subj_text, "obj_text": obj_text, "relation": rel_type, }