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Update files from the datasets library (from 1.1.3)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.1.3

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  1. .gitattributes +27 -0
  2. asnq.py +151 -0
  3. dataset_infos.json +1 -0
  4. dummy/1.0.0/dummy_data.zip +3 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
asnq.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """Answer-Sentence Natural Questions (ASNQ)
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+
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+ ASNQ is a dataset for answer sentence selection derived from Google's
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+ Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). It converts
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+ NQ's dataset into an AS2 (answer-sentence-selection) format.
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+
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+ The dataset details can be found in the paper at
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+ https://arxiv.org/abs/1911.04118
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+
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+ The dataset can be downloaded at
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+ https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar
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+ """
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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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+ _CITATION = """\
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+ @article{garg2019tanda,
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+ title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
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+ author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
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+ year={2019},
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+ eprint={1911.04118},
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ ASNQ is a dataset for answer sentence selection derived from
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+ Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
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+
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+ Each example contains a question, candidate sentence, label indicating whether or not
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+ the sentence answers the question, and two additional features --
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+ sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
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+ candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.
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+
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+ For more details please see
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+ https://arxiv.org/pdf/1911.04118.pdf
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+
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+ and
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+
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+ https://research.google/pubs/pub47761/
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+ """
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+
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+ _URL = "https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar"
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+
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+
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+ class ASNQ(datasets.GeneratorBasedBuilder):
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+ """ASNQ is a dataset for answer sentence selection derived
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+ ASNQ is a dataset for answer sentence selection derived from
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+ Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
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+
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+ The dataset details can be found in the paper:
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+ https://arxiv.org/abs/1911.04118
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+ """
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ def _info(self):
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+
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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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+ # This defines the different columns of the dataset and their types
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+ features=datasets.Features(
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+ {
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+ "question": datasets.Value("string"),
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+ "sentence": datasets.Value("string"),
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+ "label": datasets.ClassLabel(names=["neg", "pos"]),
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+ "sentence_in_long_answer": datasets.Value("bool"),
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+ "short_answer_in_sentence": datasets.Value("bool"),
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+ }
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+ ),
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+ # No default supervised_keys
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage="https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
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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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+ # 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, "data", "asnq")
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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={
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+ "filepath": os.path.join(data_dir, "train.tsv"),
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+ "split": "train",
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+ },
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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={
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+ "filepath": os.path.join(data_dir, "dev.tsv"),
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+ "split": "dev",
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+ },
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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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+
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+ Original dataset contains labels '1', '2', '3' and '4', with labels
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+ '1', '2' and '3' considered negative (sentence does not answer the question),
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+ and label '4' considered positive (sentence does answer the question).
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+ We map these labels to two classes, returning the other properties as additional
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+ features."""
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+
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+ # Mapping of dataset's original labels to a tuple of
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+ # (label, sentence_in_long_answer, short_answer_in_sentence)
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+ label_map = {
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+ "1": ("neg", False, False),
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+ "2": ("neg", False, True),
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+ "3": ("neg", True, False),
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+ "4": ("pos", True, True),
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+ }
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+ with open(filepath, encoding="utf-8") as tsvfile:
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+ tsvreader = csv.reader(tsvfile, delimiter="\t")
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+ for id_, row in enumerate(tsvreader):
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+ question, sentence, orig_label = row
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+ label, sentence_in_long_answer, short_answer_in_sentence = label_map[orig_label]
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+ yield id_, {
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+ "question": question,
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+ "sentence": sentence,
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+ "label": label,
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+ "sentence_in_long_answer": sentence_in_long_answer,
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+ "short_answer_in_sentence": short_answer_in_sentence,
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+ }
dataset_infos.json ADDED
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+ {"default": {"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features -- \nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the \ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see \nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand \n\nhttps://research.google/pubs/pub47761/\n", "citation": "@article{garg2019tanda,\n title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n year={2019},\n eprint={1911.04118},\n}\n", "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sentence_in_long_answer": {"dtype": "bool", "id": null, "_type": "Value"}, "short_answer_in_sentence": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "asnq", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3656881376, "num_examples": 20377568, "dataset_name": "asnq"}, "validation": {"name": "validation", "num_bytes": 168005155, "num_examples": 930062, "dataset_name": "asnq"}}, "download_checksums": {"https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar": {"num_bytes": 3563857920, "checksum": "4211d3e507e7cfa345a9eea3c5222b7d79fd963cf27407555c5558c37344ddf1"}}, "download_size": 3563857920, "post_processing_size": null, "dataset_size": 3824886531, "size_in_bytes": 7388744451}}
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