# 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. """TODO: Add a description here.""" import re import gzip import json import datasets from pathlib import Path # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = "" _DESCRIPTION = """\ French Wikipedia dataset for Entity Linking """ _HOMEPAGE = "https://github.com/GaaH/frwiki_el" _LICENSE = "WTFPL" _URLs = { "frwiki": "data/corpus.jsonl.gz", "entities": "data/entities.jsonl.gz", } _NER_CLASS_LABELS = [ "B", "I", "O", ] _ENTITY_TYPES = [ "DATE", "PERSON", "GEOLOC", "ORG", "OTHER", ] def item_to_el_features(item, title2qid): res = { "title": item['name'].replace("_", " "), "wikidata_id": item['wikidata_id'], "wikipedia_id": item['wikipedia_id'], "wikidata_url": item['wikidata_url'], "wikipedia_url": item['wikipedia_url'], } text_dict = { "words": [], "ner": [], "el": [], } entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]" # start index of the previous text i = 0 text = item['text'] for m in re.finditer(entity_pattern, text): mention_title = m.group(1) mention = m.group(2) mention_qid = title2qid.get(mention_title.replace("_", " "), "unknown") mention_words = mention.split() j = m.start(0) prev_text = text[i:j].split() len_prev_text = len(prev_text) text_dict["words"].extend(prev_text) text_dict["ner"].extend(["O"] * len_prev_text) text_dict["el"].extend([None] * len_prev_text) text_dict["words"].extend(mention_words) len_mention_tail = len(mention_words) - 1 text_dict["ner"].extend(["B"] + ["I"] * len_mention_tail) text_dict["el"].extend([mention_qid] + [mention_qid] * len_mention_tail) i = m.end(0) tail = text[i:].split() len_tail = len(tail) text_dict["words"].extend(tail) text_dict["ner"].extend(["O"] * len_tail) text_dict["el"].extend([None] * len_tail) res.update(text_dict) return res class FrwikiElDataset(datasets.GeneratorBasedBuilder): """ """ VERSION = datasets.Version("0.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="frwiki", version=VERSION, description="The frwiki dataset for Entity Linking"), datasets.BuilderConfig(name="entities", version=VERSION, description="Entities and their descriptions"), ] # It's not mandatory to have a default configuration. Just use one if it make sense. DEFAULT_CONFIG_NAME = "frwiki" def _info(self): if self.config.name == "frwiki": features = datasets.Features({ "name": datasets.Value("string"), "wikidata_id": datasets.Value("string"), "wikipedia_id": datasets.Value("string"), "wikipedia_url": datasets.Value("string"), "wikidata_url": datasets.Value("string"), "words": [datasets.Value("string")], "ner": [datasets.ClassLabel(names=_NER_CLASS_LABELS)], "el": [datasets.Value("string")], }) elif self.config.name == "entities": features = datasets.Features({ "name": datasets.Value("string"), "wikidata_id": datasets.Value("string"), "wikipedia_id": datasets.Value("string"), "wikipedia_url": datasets.Value("string"), "wikidata_url": datasets.Value("string"), "description": datasets.Value("string"), }) 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 # Here we define them above because they are different between the two configurations features=features, # 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.""" # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # 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 filepath = _URLs[self.config.name] # data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "path": filepath, } ) ] def _generate_examples(self, path): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. # entities_path = Path(data_dir, "entities.jsonl.gz") # corpus_path = Path(data_dir, "corpus.jsonl.gz") def _identiy(x): return x with gzip.open(path, "rt", encoding="UTF-8") as crps_file: for id, line in enumerate(crps_file): item = json.loads(line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy) yield id, item