albertvillanova HF staff commited on
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1251ea3
1 Parent(s): ffd96bd

Delete loading script

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  1. drop.py +0 -202
drop.py DELETED
@@ -1,202 +0,0 @@
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- """TODO(drop): Add a description here."""
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-
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-
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- import json
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- import os
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @inproceedings{Dua2019DROP,
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- author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
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- title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
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- booktitle={Proc. of NAACL},
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- year={2019}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
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- . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
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- question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
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- sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
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- necessary for prior datasets.
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- """
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- _URL = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
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-
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-
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- class AnswerParsingError(Exception):
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- pass
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-
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-
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- class DropDateObject:
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- """
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- Custom parser for date answers in DROP.
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- A date answer is a dict <date> with at least one of day|month|year.
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-
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- Example: date == {
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- 'day': '9',
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- 'month': 'March',
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- 'year': '2021'
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- }
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-
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- This dict is parsed and flattend to '{day} {month} {year}', not including
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- blank values.
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-
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- Example: str(DropDateObject(date)) == '9 March 2021'
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- """
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-
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- def __init__(self, dict_date):
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- self.year = dict_date.get("year", "")
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- self.month = dict_date.get("month", "")
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- self.day = dict_date.get("day", "")
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-
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- def __iter__(self):
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- yield from [self.day, self.month, self.year]
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-
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- def __bool__(self):
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- return any(self)
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-
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- def __repr__(self):
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- return " ".join(self).strip()
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-
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-
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- class Drop(datasets.GeneratorBasedBuilder):
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- """TODO(drop): Short description of my dataset."""
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-
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- # TODO(drop): Set up version.
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- VERSION = datasets.Version("0.1.0")
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-
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- def _info(self):
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- # TODO(drop): Specifies the datasets.DatasetInfo object
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # datasets.features.FeatureConnectors
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- features=datasets.Features(
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- {
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- "section_id": datasets.Value("string"),
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- "query_id": datasets.Value("string"),
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- "passage": datasets.Value("string"),
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- "question": datasets.Value("string"),
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- "answers_spans": datasets.features.Sequence(
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- {"spans": datasets.Value("string"), "types": datasets.Value("string")}
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- )
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- # These are the features of your dataset like images, labels ...
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- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage="https://allennlp.org/drop",
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- # TODO(drop): Downloads the data and defines the splits
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- # dl_manager is a datasets.download.DownloadManager that can be used to
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- # download and extract URLs
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- dl_dir = dl_manager.download_and_extract(_URL)
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- data_dir = os.path.join(dl_dir, "drop_dataset")
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_train.json"), "split": "train"},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_dev.json"), "split": "validation"},
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- ),
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- ]
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-
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- def _generate_examples(self, filepath, split):
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- """Yields examples."""
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- # TODO(drop): Yields (key, example) tuples from the dataset
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- with open(filepath, mode="r", encoding="utf-8") as f:
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- data = json.load(f)
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- id_ = 0
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- for i, (section_id, section) in enumerate(data.items()):
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- for j, qa in enumerate(section["qa_pairs"]):
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-
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- example = {
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- "section_id": section_id,
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- "query_id": qa["query_id"],
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- "passage": section["passage"],
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- "question": qa["question"],
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- }
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-
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- if split == "train":
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- answers = [qa["answer"]]
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- else:
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- answers = qa["validated_answers"]
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-
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- try:
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- example["answers_spans"] = self.build_answers(answers)
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- yield id_, example
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- id_ += 1
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- except AnswerParsingError:
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- # This is expected for 9 examples of train
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- # and 1 of validation.
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- continue
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-
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- @staticmethod
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- def _raise(message):
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- """
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- Raise a custom AnswerParsingError, to be sure to only catch our own
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- errors. Messages are irrelavant for this script, but are written to
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- ease understanding the code.
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- """
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- raise AnswerParsingError(message)
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-
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- def build_answers(self, answers):
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-
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- returned_answers = {
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- "spans": list(),
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- "types": list(),
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- }
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- for answer in answers:
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- date = DropDateObject(answer["date"])
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-
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- if answer["number"] != "":
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- # sanity checks
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- if date:
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- self._raise("This answer is both number and date!")
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- if len(answer["spans"]):
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- self._raise("This answer is both number and text!")
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-
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- returned_answers["spans"].append(answer["number"])
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- returned_answers["types"].append("number")
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-
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- elif date:
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- # sanity check
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- if len(answer["spans"]):
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- self._raise("This answer is both date and text!")
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-
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- returned_answers["spans"].append(str(date))
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- returned_answers["types"].append("date")
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-
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- # won't triger if len(answer['spans']) == 0
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- for span in answer["spans"]:
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- # sanity checks
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- if answer["number"] != "":
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- self._raise("This answer is both text and number!")
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- if date:
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- self._raise("This answer is both text and date!")
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-
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- returned_answers["spans"].append(span)
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- returned_answers["types"].append("span")
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-
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- # sanity check
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- _len = len(returned_answers["spans"])
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- if not _len:
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- self._raise("Empty answer.")
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- if any(len(l) != _len for _, l in returned_answers.items()):
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- self._raise("Something went wrong while parsing answer values/types")
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-
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- return returned_answers