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README.md

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+<!--
+ Copyright 2021 san kim
+ 
+ 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.
+-->
+
+# KorQuAD
+

korquad.py

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+# Copyright 2021 san kim
+# 
+# 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.
+
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors.
+#
+# 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.
+
+# modified by kimsan0622@keti.re.kr
+"""korquad dataset."""
+import os
+import json
+import copy
+import glob
+import hashlib
+import functools
+
+import datasets
+
+# KorQuad: https://korquad.github.io/
+# ---------------------------------------------
+_KORQUAD_URL='https://korquad.github.io/'
+# https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_v1.0_dev.json
+_KORQUAD_ROOT='https://github.com/korquad/korquad.github.io/raw/master/dataset/'
+_KORQUADV1_TRAIN_LINK=[os.path.join(_KORQUAD_ROOT, 'KorQuAD_v1.0_train.json')]
+_KORQUADV1_DEV_LINK=[os.path.join(_KORQUAD_ROOT, 'KorQuAD_v1.0_dev.json')]
+_KORQUADV1_DEFAULT_SPLIT={'train': _KORQUADV1_TRAIN_LINK, 'dev': _KORQUADV1_DEV_LINK}
+_KORQUADV1_DESCRIPTION = """
+KorQuAD1.0
+"""
+_KORQUADV1_CITATION = """
+@article{DBLP:journals/corr/abs-1909-07005,
+  author    = {Seungyoung Lim and
+               Myungji Kim and
+               Jooyoul Lee},
+  title     = {KorQuAD1.0: Korean {QA} Dataset for Machine Reading Comprehension},
+  journal   = {CoRR},
+  volume    = {abs/1909.07005},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1909.07005},
+  archivePrefix = {arXiv},
+  eprint    = {1909.07005},
+  timestamp = {Mon, 23 Sep 2019 18:07:15 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-07005.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_2.1/train/KorQuAD_2.1_train_00.zip
+_KORQUADV2_TRAIN_LINK=[os.path.join(_KORQUAD_ROOT,'KorQuAD_2.1/train', 'KorQuAD_2.1_train_{0:02d}.zip'.format(idx)) for idx in range(13)]
+_KORQUADV2_DEV_LINK=[os.path.join(_KORQUAD_ROOT,'KorQuAD_2.1/dev', 'KorQuAD_2.1_dev_{0:02d}.zip'.format(idx)) for idx in range(2)]
+_KORQUADV2_DEFAULT_SPLIT={'train': _KORQUADV2_TRAIN_LINK, 'dev': _KORQUADV2_DEV_LINK}
+_KORQUADV2_DESCRIPTION = """
+KorQuAD2.1
+"""
+_KORQUADV2_CITATION = """
+김영민, 임승영, 이현정, 박소윤, 김명지. (2020). KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋. 정보과학회논문지, 47(6), 577-586.
+"""
+
+
+SQUADLIKE_FEATURES = datasets.Features({
+    "id":
+        datasets.Value("string"),
+    "title":
+        datasets.Value("string"),
+    "context":
+        datasets.Value("string"),
+    "question":
+        datasets.Value("string"),
+    "answers":
+        datasets.Sequence({
+            "text": datasets.Value("string"),
+            "answer_start": datasets.Value("int32"),
+        }),
+})
+
+# adopted from question_answering in tensorflow_datasets 
+def generate_squadlike_examples(filepath):
+  """Parses a SQuAD-like JSON, yielding examples with `SQUADLIKE_FEATURES`."""
+  # We first re-group the answers, which may be flattened (e.g., by XTREME).
+  qas = {}
+  with open(filepath) as f:
+    squad = json.load(f)
+    for article in squad["data"]:
+      title = article.get("title", "")
+      for paragraph in article["paragraphs"]:
+        context = paragraph["context"]
+        for qa in paragraph["qas"]:
+          qa["title"] = title
+          qa["context"] = context
+          id_ = qa["id"]
+          if id_ in qas:
+            qas[id_]["answers"].extend(qa["answers"])
+          else:
+            qas[id_] = qa
+
+    for id_, qa in qas.items():
+      answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+      answers = [answer["text"] for answer in qa["answers"]]
+      yield id_, {
+          "title": qa["title"],
+          "context": qa["context"],
+          "question": qa["question"],
+          "id": id_,
+          "answers": {
+              "answer_start": answer_starts,
+              "text": answers,
+          },
+      }
+
+_KORQUADV2_KEY_MAP={'context':'context', 'answer_start': 'answer_start', 'text':'text'}
+_KORQUADV2_HTML_KEY_MAP={'context':'raw_html', 'answer_start': 'html_answer_start', 'text':'html_answer_text'}
+
+def generate_korquadv2_examples(filepath, KEY_MAP):
+  qas = {}
+  with open(filepath) as f:
+    squad = json.load(f)
+    for article in squad["data"]:
+      title = article.get("title", "").strip()
+      context = article[KEY_MAP['context']]
+      for qa in article["qas"]:
+        qa["title"] = title
+        qa["context"] = context
+        id_ = qa["id"]
+        qa["answers"] = [copy.deepcopy(qa["answer"])]
+        del qa["answer"]
+        qas[id_] = qa
+
+    for id_, qa in qas.items():
+      answer_starts = [answer[KEY_MAP['answer_start']] for answer in qa["answers"]]
+      answers = [answer[KEY_MAP['text']] for answer in qa["answers"]]
+      yield id_, {
+          "title": qa["title"],
+          "context": qa["context"],
+          "question": qa["question"].strip(),
+          "id": id_,
+          "answers": {
+              "answer_start": answer_starts,
+              "text": answers,
+          },
+      }
+
+_KORQUAD_MANUAL_SPLIT = {
+  'source': {
+    datasets.Split.TRAIN: ['train'],
+    datasets.Split.VALIDATION: ['train'],
+    datasets.Split.TEST: ['dev'],
+  },
+  'split': {
+    datasets.Split.TRAIN: lambda x: x % 10 != 0,
+    datasets.Split.VALIDATION: lambda x: x % 10 == 0,
+    datasets.Split.TEST: lambda x: True,
+}}
+
+def _update_split(file_dict, split_dict):
+  source_dict = split_dict['source']
+  return_dict = {}
+  for k, v in source_dict.items():
+    flist = []
+    for vv in v:
+      flist.extend(file_dict[vv] if isinstance(file_dict[vv], list) else [file_dict[vv]])
+    return_dict[k] = flist
+  return return_dict
+
+def _hash_text(text):
+  return hashlib.md5(text.encode("utf-8")).hexdigest()
+
+def _filter_fn_hash_id(uid, split_fn):
+  hash_id = _hash_text(str(uid))
+  val = int(hash_id, 16)
+  return split_fn(val)
+
+
+_VERSION = datasets.Version('1.0.0', "")
+
+class KorquadConfig(datasets.BuilderConfig):
+  def __init__( self,
+                name,
+                data_url,
+                description,
+                citation,
+                manual_split=None,
+                **kwargs):
+    super(KorquadConfig, self).__init__(
+      name=name,
+      version=_VERSION,
+      **kwargs
+    )
+    self.data_url=data_url
+    self.description=description
+    self.citation=citation
+    self.manual_split=manual_split
+
+class Korquad(datasets.GeneratorBasedBuilder):
+  """DatasetBuilder for korquad dataset."""
+  RELEASE_NOTES = {
+      '1.0.0': 'Initial release.',
+  }
+
+  BUILDER_CONFIGS = [
+    KorquadConfig(
+      'v1.0',
+      data_url=_KORQUADV1_DEFAULT_SPLIT,
+      description=_KORQUADV1_DESCRIPTION,
+      citation=_KORQUADV1_CITATION,
+    ),
+    KorquadConfig(
+      'v1.0.split',
+      data_url=_KORQUADV1_DEFAULT_SPLIT,
+      description=_KORQUADV1_DESCRIPTION,
+      citation=_KORQUADV1_CITATION,
+      manual_split=_KORQUAD_MANUAL_SPLIT,
+    ),
+    KorquadConfig(
+      'v2.1',
+      data_url=_KORQUADV2_DEFAULT_SPLIT,
+      description=_KORQUADV2_DESCRIPTION,
+      citation=_KORQUADV2_CITATION,
+    ),
+    KorquadConfig(
+      'v2.1.split',
+      data_url=_KORQUADV2_DEFAULT_SPLIT,
+      description=_KORQUADV2_DESCRIPTION,
+      citation=_KORQUADV2_CITATION,
+      manual_split=_KORQUAD_MANUAL_SPLIT,
+    ),
+    KorquadConfig(
+      'v2.1.html',
+      data_url=_KORQUADV2_DEFAULT_SPLIT,
+      description=_KORQUADV2_DESCRIPTION,
+      citation=_KORQUADV2_CITATION,
+    ),
+    KorquadConfig(
+      'v2.1.html.split',
+      data_url=_KORQUADV2_DEFAULT_SPLIT,
+      description=_KORQUADV2_DESCRIPTION,
+      citation=_KORQUADV2_CITATION,
+      manual_split=_KORQUAD_MANUAL_SPLIT,
+    ),
+  ]
+
+  def _info(self) -> datasets.DatasetInfo:
+    """Returns the dataset metadata."""
+    features_dict = SQUADLIKE_FEATURES
+
+    return datasets.DatasetInfo(
+        description=self.config.description,
+        features=features_dict,
+        homepage=_KORQUAD_URL,
+        citation=self.config.citation,
+    )
+
+  def _split_generators(self, dl_manager: datasets.DownloadManager):
+    """Returns SplitGenerators."""
+
+    path_kv = {k:dl_manager.download_and_extract(v) for k, v in self.config.data_url.items()}
+    if not self.config.name.startswith("v1.0"):
+      for k, v in path_kv.items():
+        file_names = []
+        for vv in v:
+          file_names.extend(glob.glob(os.path.join(vv, "*.json")))
+        path_kv[k] = file_names
+
+    if self.config.manual_split is not None:
+      path_kv = _update_split(path_kv, self.config.manual_split)
+      split_fn = self.config.manual_split['split']
+      #return {k:self._generate_examples(v, split_fn[k]) for k, v in path_kv.items()}
+      return [datasets.SplitGenerator(name=k, gen_kwargs={'path_list': v, 'split_fn': split_fn[k]}) for k, v in path_kv.items()]
+
+    # TODO(korquad): Returns the Dict[split names, Iterator[Key, Example]]
+    #return {k:self._generate_examples(v) for k, v in path_kv.items()}
+    return [datasets.SplitGenerator(name=k, gen_kwargs={'path_list': v}) for k, v in path_kv.items()]
+
+  def _generate_examples(self, path_list, split_fn=None):
+    """Yields examples."""
+    # TODO(korquad): Yields (key, example) tuples from the dataset
+    if self.config.name.startswith("v2.1.html"):
+      gen_fn = functools.partial(generate_korquadv2_examples, KEY_MAP=_KORQUADV2_HTML_KEY_MAP)
+    elif self.config.name.startswith("v2.1"):
+      gen_fn = functools.partial(generate_korquadv2_examples, KEY_MAP=_KORQUADV2_KEY_MAP)
+    else:
+      gen_fn = generate_squadlike_examples
+    
+    if split_fn is not None:
+      split_filter = functools.partial(_filter_fn_hash_id, split_fn=split_fn)
+    else:
+      split_filter = lambda x: True
+    
+    _hash_set = set()
+
+    for fpath in path_list:
+      for example in iter(gen_fn(fpath)):
+        uid, _ = example
+        if split_filter(str(uid)) and str(uid) not in _hash_set:
+            _hash_set.add(str(uid))
+            yield example
+
+# tfds build --data_dir ../../tmp/tensorflow_datasets --config v1.0.split