Datasets:
wiki_qa

Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
License: other
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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

Files changed (4) hide show
  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/0.1.0/dummy_data.zip +3 -0
  4. wiki_qa.py +97 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
dataset_infos.json ADDED
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+ {"default": {"description": "Wiki Question Answering corpus from Microsoft\n", "citation": "@InProceedings{YangYihMeek:EMNLP2015:WikiQA,\n author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},\n title = \"{WikiQA: A Challenge Dataset for Open-Domain Question Answering}\",\n journal = {Association for Computational Linguistics},\n year = 2015,\n doi = {10.18653/v1/D15-1237},\n pages = {2013\u20132018},\n}\n", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=52419", "license": "", "features": {"question_id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "document_title": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "wiki_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1337903, "num_examples": 6165, "dataset_name": "wiki_qa"}, "train": {"name": "train", "num_bytes": 4469148, "num_examples": 20360, "dataset_name": "wiki_qa"}, "validation": {"name": "validation", "num_bytes": 591833, "num_examples": 2733, "dataset_name": "wiki_qa"}}, "download_checksums": {"https://download.microsoft.com/download/E/5/f/E5FCFCEE-7005-4814-853D-DAA7C66507E0/WikiQACorpus.zip": {"num_bytes": 7094233, "checksum": "467c13f9e104552c0a9c16f41836ca8d89f9c0cc4b6e4355e104d5c3109ffa45"}}, "download_size": 7094233, "dataset_size": 6398884, "size_in_bytes": 13493117}}
dummy/0.1.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8d7238a3a8d7e6f18ef01eacdb01fdcd3ba855fbf9c95b9e30040c488301a741
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+ size 1766
wiki_qa.py ADDED
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+ """TODO(wiki_qa): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import csv
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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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+ # TODO(wiki_qa): BibTeX citation
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+ _CITATION = """\
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+ @InProceedings{YangYihMeek:EMNLP2015:WikiQA,
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+ author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
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+ title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
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+ journal = {Association for Computational Linguistics},
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+ year = 2015,
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+ doi = {10.18653/v1/D15-1237},
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+ pages = {2013–2018},
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+ }
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+ """
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+
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+ # TODO(wiki_qa):
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+ _DESCRIPTION = """\
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+ Wiki Question Answering corpus from Microsoft
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+ """
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+
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+ _DATA_URL = "https://download.microsoft.com/download/E/5/f/E5FCFCEE-7005-4814-853D-DAA7C66507E0/WikiQACorpus.zip" # 'https://www.microsoft.com/en-us/download/confirmation.aspx?id=52419'
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+
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+
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+ class WikiQa(datasets.GeneratorBasedBuilder):
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+ """TODO(wiki_qa): Short description of my dataset."""
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+
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+ # TODO(wiki_qa): 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(wiki_qa): 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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+ "question_id": datasets.Value("string"),
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+ "question": datasets.Value("string"),
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+ "document_title": datasets.Value("string"),
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+ "answer": datasets.Value("string"),
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+ "label": datasets.features.ClassLabel(num_classes=2),
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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://www.microsoft.com/en-us/download/details.aspx?id=52419",
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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(wiki_qa): 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(_DATA_URL)
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+ dl_dir = os.path.join(dl_dir, "WikiQACorpus")
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+ # dl_dir = os.path.join(dl_dir, '')
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "WikiQA-test.tsv")}
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "WikiQA-dev.tsv")}
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+ ),
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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(dl_dir, "WikiQA-train.tsv")},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Yields examples."""
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+ # TODO(wiki_qa): Yields (key, example) tuples from the dataset
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+
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+ with open(filepath, encoding="utf-8") as f:
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+ reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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+ for idx, row in enumerate(reader):
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+ yield idx, {
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+ "question_id": row["QuestionID"],
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+ "question": row["Question"],
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+ "document_title": row["DocumentTitle"],
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+ "answer": row["Sentence"],
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+ "label": row["Label"],
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+ }