Change documentation and remove unused field

JaQuAD.py

@@ -1,50 +1,50 @@
+'''Dataset loading script for JaQuAD.
+We refer to https://huggingface.co/datasets/squad_v2/blob/main/squad_v2.py
+'''
 import json
 import os
 
 import datasets
 
-
-_CITATION = """\
+_CITATION = '''
 @article{SkelterLabsInc:JaQuAD,
  title = {{JaQuAD}: Japanese Question Answering Dataset for Machine
           Reading Comprehension},
-author = {Byunghoon, So  and
-          Kyuhong, Byun  and
-          Kyungwon, Kang  and
-          Seongjin, Cho},
+author = {Byunghoon So  and
+          Kyuhong Byun  and
+          Kyungwon Kang  and
+          Seongjin Cho},
   year = {2022},
 }
-"""
-_DESCRIPTION = """\
-Japanese Question Answering Dataset (JaQuAD), released in 2022, is a
-human-annotated dataset created for Japanese Machine Reading Comprehension.
-JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.
-JaQuAD contains 39,696 question-answer pairs.
-Questions and answers are manually curated by human annotators.
-Contexts are collected from Japanese Wikipedia articles.
-"""
-_LICENSE = "CC BY-SA 3.0"
-_HOMEPAGE=""
-_URL = "https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/"
+'''
+_DESCRIPTION = '''Japanese Question Answering Dataset (JaQuAD), released in
+2022, is a human-annotated dataset created for Japanese Machine Reading
+Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in
+Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers
+are manually curated by human annotators. Contexts are collected from Japanese
+Wikipedia articles.
+'''
+_LICENSE = 'CC BY-SA 3.0'
+_HOMEPAGE = 'https://skelterlabs.com/en/'
+_URL = 'https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/'
 
 
 class JaQuAD(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.1.0")
+    VERSION = datasets.Version('0.1.0')
 
     def _info(self):
-
         features = datasets.Features({
-            "id": datasets.Value("string"),
-            "title": datasets.Value("string"),
-            "context": datasets.Value("string"),
-            "question": datasets.Value("string"),
-            "question_type": datasets.Value("string"),
-            "answers": datasets.features.Sequence({
-                "text": datasets.Value("string"),
-                "answer_start": datasets.Value("int32"),
-                "answer_type": datasets.Value("string"),
-            }),
+            'id': datasets.Value('string'),
+            'title': datasets.Value('string'),
+            'context': datasets.Value('string'),
+            'question': datasets.Value('string'),
+            'question_type': datasets.Value('string'),
+            'answers':
+                datasets.features.Sequence({
+                    'text': datasets.Value('string'),
+                    'answer_start': datasets.Value('int32'),
+                    'answer_type': datasets.Value('string'),
+                }),
         })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -56,49 +56,62 @@ class JaQuAD(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         urls_to_download = {
-            "train": [os.path.join(_URL, f"train/jaquad_train_{i:04d}.json") for i in range(30)],
-            "dev": [os.path.join(_URL, f"dev/jaquad_dev_{i:04d}.json") for i in range(4)],
+            'train': [
+                os.path.join(_URL, f'train/jaquad_train_{i:04d}.json')
+                for i in range(30)
+            ],
+            'dev': [
+                os.path.join(_URL, f'dev/jaquad_dev_{i:04d}.json')
+                for i in range(4)
+            ],
         }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": downloaded_files["train"]},
+                gen_kwargs={'filepaths': downloaded_files['train']},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepaths": downloaded_files["dev"]},
+                gen_kwargs={'filepaths': downloaded_files['dev']},
             ),
         ]
 
     def _generate_examples(self, filepaths):
         for filename in filepaths:
-            with open(filename, encoding='utf-8') as f:
-                jaquad = json.load(f)
+            with open(filename, encoding='utf-8') as ifile:
+                jaquad = json.load(ifile)
                 for article in jaquad['data']:
                     title = article.get('title', '').strip()
                     for paragraph in article['paragraphs']:
                         context = paragraph['context'].strip()
                         for qa in paragraph['qas']:
-                            qa_id = qa["id"]
-                            question = qa["question"].strip()
-                            question_type = qa["question_type"]
-
-                            answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                            answer_texts = [answer["text"].strip() for answer in qa["answers"]]
-                            answer_types = [answer["answer_type"] for answer in qa["answers"]]
+                            qa_id = qa['id']
+                            question = qa['question'].strip()
+                            question_type = qa['question_type']
 
-                            assert len(answer_starts) == len(answer_texts) == len(answer_types) == 1
+                            answer_starts = [
+                                answer['answer_start']
+                                for answer in qa['answers']
+                            ]
+                            answer_texts = [
+                                answer['text'].strip()
+                                for answer in qa['answers']
+                            ]
+                            answer_types = [
+                                answer['answer_type']
+                                for answer in qa['answers']
+                            ]
 
                             yield qa_id, {
-                                "title": title,
-                                "context": context,
-                                "question": question,
-                                "question_type": question_type,
-                                "id": qa_id,
-                                "answers": {
-                                    "text": answer_texts,
-                                    "answer_start": answer_starts,
-                                    "answer_type": answer_types,
+                                'title': title,
+                                'context': context,
+                                'question': question,
+                                'question_type': question_type,
+                                'id': qa_id,
+                                'answers': {
+                                    'text': answer_texts,
+                                    'answer_start': answer_starts,
+                                    'answer_type': answer_types,
                                 },
                             }

README.md

@@ -96,7 +96,7 @@ An example of 'validation':
     "question_type": "Multiple sentence reasoning",
     "answers": {
         "text": "イタセンパラ",
-        "answers_start": 0,
+        "answer_start": 0,
         "answer_type": "Object",
     },
 },
@@ -111,7 +111,7 @@ An example of 'validation':
 - `question_type`: a `string` feature.
 - `answers`: a dictionary feature containing:
   - `text`: a `string` feature.
-  - `answers_start`: a `int32` feature.
+  - `answer_start`: a `int32` feature.
   - `answer_type`: a `string` feature.
 
 ### Data Splitting