khalidalt commited on
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a56b673
1 Parent(s): 1a9413c

Update tydiqa-goldp.py

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  1. tydiqa-goldp.py +8 -31
tydiqa-goldp.py CHANGED
@@ -28,44 +28,21 @@ the use of translation (unlike MLQA and XQuAD).
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  _LANG = ["arabic", "bengali", "english", "finnish", "indonesian", "japanese", "korean", "russian", "swahili", "telugu", "thai"]
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- _PRIMARY_URLS = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/primary_tasks/{split}/{language}-{split}.jsonl"
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- _Secondary_URLS = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/{split}/{language}-{split}.jsonl"
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-
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  _VERSION = datasets.Version("1.1.0", "")
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  class tydiqa_GoldP(datasets.GeneratorBasedBuilder):
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  BUILDER_CONFIGS = [
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- TydiqaConfig(
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- name="primary_task",
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- description=textwrap.dedent(
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- """\
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- Passage selection task (SelectP): Given a list of the passages in the article, return either (a) the index of
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- the passage that answers the question or (b) NULL if no such passage exists.
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- Minimal answer span task (MinSpan): Given the full text of an article, return one of (a) the start and end
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- byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires
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- a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a
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- minimal answer for this question."""
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- ),
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- ),
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- TydiqaConfig(
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- name="secondary_task",
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- description=textwrap.dedent(
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- """Gold passage task (GoldP): Given a passage that is guaranteed to contain the
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- answer, predict the single contiguous span of characters that answers the question. This is more similar to
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- existing reading comprehension datasets (as opposed to the information-seeking task outlined above).
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- This task is constructed with two goals in mind: (1) more directly comparing with prior work and (2) providing
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- a simplified way for researchers to use TyDi QA by providing compatibility with existing code for SQuAD 1.1,
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- XQuAD, and MLQA. Toward these goals, the gold passage task differs from the primary task in several ways:
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- only the gold answer passage is provided rather than the entire Wikipedia article;
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- unanswerable questions have been discarded, similar to MLQA and XQuAD;
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- we evaluate with the SQuAD 1.1 metrics like XQuAD; and
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- Thai and Japanese are removed since the lack of whitespace breaks some tools.
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- """
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- ),
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  )
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  for lang in _LANG
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  ]
 
 
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  def _info(self):
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  # TODO(tydiqa): Specifies the datasets.DatasetInfo object
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@@ -108,7 +85,7 @@ class tydiqa_GoldP(datasets.GeneratorBasedBuilder):
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  splits = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev"}
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  data_urls = {
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- split: _Secondary_URLS.format(language=language, split=splits[split]) for split in splits
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  }
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  dl_paths = dl_manager.download(data_urls)
 
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  _LANG = ["arabic", "bengali", "english", "finnish", "indonesian", "japanese", "korean", "russian", "swahili", "telugu", "thai"]
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+ _URL = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/{split}/{language}-{split}.jsonl"
 
 
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  _VERSION = datasets.Version("1.1.0", "")
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  class tydiqa_GoldP(datasets.GeneratorBasedBuilder):
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  BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name=lang,
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+ description=f"tydiqa-GoldP language {lang}",
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+ version=_VERSION,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  )
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  for lang in _LANG
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  ]
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+
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+
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  def _info(self):
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  # TODO(tydiqa): Specifies the datasets.DatasetInfo object
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  splits = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev"}
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  data_urls = {
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+ split: _URL.format(language=language, split=splits[split]) for split in splits
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  }
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  dl_paths = dl_manager.download(data_urls)