# 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: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import csv import json import os import random import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # 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 Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://raw.githubusercontent.com/stockmarkteam/ner-wikipedia-dataset/main/ner.json" class NerWikipediaDatasetConfig(datasets.BuilderConfig): """BuilderConfig for NerWikipediaDataset.""" def __init__(self, **kwargs): """BuilderConfig for NerWikipediaDataset Args: **kwargs: keyword arguments forwarded to super. """ super(NerWikipediaDatasetConfig, self).__init__(**kwargs) # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class NerWikipediaDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") # 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="all", # version=VERSION, # description="This part of my dataset covers a first domain", # ), # ] # DEFAULT_CONFIG_NAME = "all" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset 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=datasets.Features( { "curid": datasets.Value("int32"), "text": datasets.Value("string"), "entities": datasets.Sequence( feature={ "name": datasets.Value(dtype="string"), "span": datasets.Sequence( feature=datasets.Value(dtype="int32"), length=2 ), "type": datasets.Value(dtype="string"), }, ) # These are the features of your dataset like images, labels ... } ), # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): # 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 data_dir = dl_manager.download_and_extract(_URL) # ダウンロードしたファイルを読み込み、全てのデータを取得 with open(data_dir, "r", encoding="utf-8") as f: data = json.load(f) # データをランダムにシャッフルする random.seed(42) random.shuffle(data) # 学習データ、開発データ、テストデータに分割する train_ratio = 0.8 validation_ratio = 0.1 num_examples = len(data) train_split = int(num_examples * train_ratio) validation_split = int(num_examples * (train_ratio + validation_ratio)) train_data = data[:train_split] validation_data = data[train_split:validation_split] test_data = data[validation_split:] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data": train_data, "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"data": validation_data, "split": "validation"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"data": test_data, "split": "test"}, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, data, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. for key, data in enumerate(data): yield key, { "curid": data["curid"], "text": data["text"], "entities": data["entities"], }