# 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. """Brand-Product Relation Extraction Corpora""" import json import datasets # DONE: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{inproceedings, author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka}, year = {2020}, month = {05}, pages = {}, title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations} } """ # DONE: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ Dataset consisting of Polish language texts annotated to recognize brand-product relations. """ # DONE: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/736" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # 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 = { "tele": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_tele_export.json", "electro": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_electro_export.json", "cosmetics": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_cosmetics_export.json", "banking": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_banking_export.json", } _CATEGORIES = { "tele": "telecommunications", "electro": "electronics", "cosmetics": "cosmetics", "banking": "banking", } _ALL_CATEGORIES = "all" _VERSION = "1.1.0" class BprecConfig(datasets.BuilderConfig): """BuilderConfig for BprecConfig.""" def __init__(self, categories=None, **kwargs): super(BprecConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs), self.categories = categories # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class Bprec(datasets.GeneratorBasedBuilder): """Brand-Product Relation Extraction Corpora in Polish""" BUILDER_CONFIGS = [ BprecConfig( name=_ALL_CATEGORIES, categories=_CATEGORIES, description="A collection of Polish language texts annotated to recognize brand-product relations", ) ] + [ BprecConfig( name=cat, categories=[cat], description=f"{_CATEGORIES[cat]} examples from a collection of Polish language texts annotated to recognize brand-product relations", ) for cat in _CATEGORIES ] BUILDER_CONFIG_CLASS = BprecConfig DEFAULT_CONFIG_NAME = _ALL_CATEGORIES def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = datasets.Features( { "id": datasets.Value("int32"), "category": datasets.Value("string"), "text": datasets.Value("string"), "ner": datasets.features.Sequence( { "source": { "from": datasets.Value("int32"), "text": datasets.Value("string"), "to": datasets.Value("int32"), "type": datasets.features.ClassLabel( names=[ "PRODUCT_NAME", "PRODUCT_NAME_IMP", "PRODUCT_NO_BRAND", "BRAND_NAME", "BRAND_NAME_IMP", "VERSION", "PRODUCT_ADJ", "BRAND_ADJ", "LOCATION", "LOCATION_IMP", ] ), }, "target": { "from": datasets.Value("int32"), "text": datasets.Value("string"), "to": datasets.Value("int32"), "type": datasets.features.ClassLabel( names=[ "PRODUCT_NAME", "PRODUCT_NAME_IMP", "PRODUCT_NO_BRAND", "BRAND_NAME", "BRAND_NAME_IMP", "VERSION", "PRODUCT_ADJ", "BRAND_ADJ", "LOCATION", "LOCATION_IMP", ] ), }, } ), } ) 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.""" # 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 _my_urls = [_URLs[cat] for cat in self.config.categories] downloaded_files = dl_manager.download_and_extract(_my_urls) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filedirs": downloaded_files}), ] def _generate_examples(self, filedirs, split="tele"): """Yields examples.""" # TODO: 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) cats = [cat for cat in self.config.categories] for cat, filepath in zip(cats, filedirs): with open(filepath, "r", encoding="utf-8") as f: data = json.load(f) for key in data.keys(): example = data[key] id_ = example.get("id") text = example.get("text") ner = example.get("ner") yield id_, { "id": id_, "category": cat, "text": text, "ner": ner, }