add a code to acquire dataset

README.md

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+---
+annotations_creators:
+- crowdsourced
+language:
+- ja
+language_creators:
+- crowdsourced
+license: []
+multilinguality:
+- monolingual
+pretty_name: ner-wikipedia-dataset
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- NER
+task_categories:
+- token-classification
+task_ids: []
+---

ner-wikipedia-dataset.py

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+# 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 = {
+    "all": "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"],
+            }