ner-wikipedia-dataset.py

# 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 MS MARCO."""

    def __init__(self, **kwargs):
        """BuilderConfig for MS MARCO
        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.ALL, gen_kwargs={"data": data, "split": "all"}
            ),
            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": "" if split == "test" else data["entities"],
            }