modify file

ner-wikipedia-dataset.py

@@ -1,175 +1,102 @@
-# 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}
+from typing import Generator
+
+from datasets import (
+    BuilderConfig,
+    DatasetInfo,
+    DownloadManager,
+    Features,
+    GeneratorBasedBuilder,
+    Sequence,
+    Split,
+    SplitGenerator,
+    Value,
+    Version,
+)
+
+_CITATION = """
+@inproceedings{omi-2021-wikipedia,
+    title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築",
+    author = "近江 崇宏",
+    booktitle = "言語処理学会第27回年次大会",
+    year = "2021",
+    url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf",
 }
 """
-
-# 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)
+_DESCRIPTION = "This is a dataset of Wikipedia articles with named entity labels created by Stockmark Inc."
+_HOMEPAGE = "https://github.com/stockmarkteam/ner-wikipedia-dataset"
+_LICENSE = "CC-BY-SA 3.0"
 _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.
+class NerWikipediaDataset(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        BuilderConfig(
+            name="ner-wikipedia-dataset",
+            version=Version("2.0.0"),
+            description=_DESCRIPTION,
+        ),
+    ]
 
-    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.
+    def _info(self) -> DatasetInfo:
+        return DatasetInfo(
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=datasets.Features(
+            features=Features(
                 {
-                    "curid": datasets.Value("string"),
-                    "text": datasets.Value("string"),
+                    "curid": Value("string"),
+                    "text": Value("string"),
                     "entities": [
                         {
-                            "name": datasets.Value(dtype="string"),
-                            "span": datasets.Sequence(
-                                datasets.Value(dtype="int64"), length=2
-                            ),
-                            "type": datasets.Value(dtype="string"),
+                            "name": Value("string"),
+                            "span": Sequence(Value("int64"), length=2),
+                            "type": Value("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:
+    def _split_generators(
+        self, dl_manager: DownloadManager
+    ) -> list[SplitGenerator]:
+        dataset_dir = str(dl_manager.download_and_extract(_URL))
+        with open(dataset_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:]
-
+        num_data = len(data)
+        num_train_data = int(num_data * 0.8)
+        num_validation_data = (num_data - num_train_data) // 2
+        train_data = data[:num_train_data]
+        validation_data = data[
+            num_train_data : num_train_data + num_validation_data
+        ]
+        test_data = data[num_train_data + num_validation_data :]
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+            SplitGenerator(
+                name=Split.TRAIN,
                 gen_kwargs={"data": train_data},
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
+            SplitGenerator(
+                name=Split.VALIDATION,
                 gen_kwargs={"data": validation_data},
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
+            SplitGenerator(
+                name=Split.TEST,
                 gen_kwargs={"data": test_data},
             ),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, data):
-        # 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"],
+    def _generate_examples(self, data: list[dict[str, str]]) -> Generator:
+        for i, d in enumerate(data):
+            yield i, {
+                "curid": d["curid"],
+                "text": d["text"],
+                "entities": d["entities"],
             }