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README.md

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----
-annotations_creators:
-- crowdsourced
-language:
-- en
-language_creators:
-- found
-license:
-- cc-by-nc-sa-3.0
-multilinguality:
-- monolingual
-pretty_name: Answer Sentence Natural Questions (ASNQ)
-size_categories:
-- 10M<n<100M
-source_datasets:
-- extended|natural_questions
-task_categories:
-- multiple-choice
-task_ids:
-- multiple-choice-qa
-paperswithcode_id: asnq
-dataset_info:
-  features:
-  - name: question
-    dtype: string
-  - name: sentence
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: neg
-          1: pos
-  - name: sentence_in_long_answer
-    dtype: bool
-  - name: short_answer_in_sentence
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 3656881376
-    num_examples: 20377568
-  - name: validation
-    num_bytes: 168005155
-    num_examples: 930062
-  download_size: 3563857920
-  dataset_size: 3824886531
----
-
-# Dataset Card for "asnq"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq](https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://arxiv.org/abs/1911.04118)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 3398.76 MB
-- **Size of the generated dataset:** 3647.70 MB
-- **Total amount of disk used:** 7046.46 MB
-
-### Dataset Summary
-
-ASNQ is a dataset for answer sentence selection derived from
-Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
-
-Each example contains a question, candidate sentence, label indicating whether or not
-the sentence answers the question, and two additional features --
-sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
-candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.
-
-For more details please see
-https://arxiv.org/abs/1911.04118
-
-and
-
-https://research.google/pubs/pub47761/
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### default
-
-- **Size of downloaded dataset files:** 3398.76 MB
-- **Size of the generated dataset:** 3647.70 MB
-- **Total amount of disk used:** 7046.46 MB
-
-An example of 'validation' looks as follows.
-```
-{
-    "label": 0,
-    "question": "when did somewhere over the rainbow come out",
-    "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .",
-    "sentence_in_long_answer": false,
-    "short_answer_in_sentence": false
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `question`: a `string` feature.
-- `sentence`: a `string` feature.
-- `label`: a classification label, with possible values including `neg` (0), `pos` (1).
-- `sentence_in_long_answer`: a `bool` feature.
-- `short_answer_in_sentence`: a `bool` feature.
-
-### Data Splits
-
-| name  | train  |validation|
-|-------|-------:|---------:|
-|default|20377568|    930062|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
-https://github.com/alexa/wqa_tanda/blob/master/LICENSE
-
-### Citation Information
-
-```
-@article{Garg_2020,
-   title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
-   volume={34},
-   ISSN={2159-5399},
-   url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
-   DOI={10.1609/aaai.v34i05.6282},
-   number={05},
-   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
-   publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
-   author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
-   year={2020},
-   month={Apr},
-   pages={7780–7788}
-}
-```
-
-### Contributions
-
-Thanks to [@mkserge](https://github.com/mkserge) for adding this dataset.

asnq.py

@@ -1,150 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""Answer-Sentence Natural Questions (ASNQ)
-
-ASNQ is a dataset for answer sentence selection derived from Google's
-Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). It converts
-NQ's dataset into an AS2 (answer-sentence-selection) format.
-
-The dataset details can be found in the paper at
-https://arxiv.org/abs/1911.04118
-
-The dataset can be downloaded at
-https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar
-"""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{garg2019tanda,
-    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
-    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
-    year={2019},
-    eprint={1911.04118},
-}
-"""
-
-_DESCRIPTION = """\
-ASNQ is a dataset for answer sentence selection derived from
-Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
-
-Each example contains a question, candidate sentence, label indicating whether or not
-the sentence answers the question, and two additional features --
-sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
-candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.
-
-For more details please see
-https://arxiv.org/pdf/1911.04118.pdf
-
-and
-
-https://research.google/pubs/pub47761/
-"""
-
-_URL = "https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar"
-
-
-class ASNQ(datasets.GeneratorBasedBuilder):
-    """ASNQ is a dataset for answer sentence selection derived
-    ASNQ is a dataset for answer sentence selection derived from
-    Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
-
-    The dataset details can be found in the paper:
-    https://arxiv.org/abs/1911.04118
-    """
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=datasets.Features(
-                {
-                    "question": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["neg", "pos"]),
-                    "sentence_in_long_answer": datasets.Value("bool"),
-                    "short_answer_in_sentence": datasets.Value("bool"),
-                }
-            ),
-            # No default supervised_keys
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "data", "asnq")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.tsv"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.tsv"),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples.
-
-        Original dataset contains labels '1', '2', '3' and '4', with labels
-        '1', '2' and '3' considered negative (sentence does not answer the question),
-        and label '4' considered positive (sentence does answer the question).
-        We map these labels to two classes, returning the other properties as additional
-        features."""
-
-        # Mapping of dataset's original labels to a tuple of
-        # (label, sentence_in_long_answer, short_answer_in_sentence)
-        label_map = {
-            "1": ("neg", False, False),
-            "2": ("neg", False, True),
-            "3": ("neg", True, False),
-            "4": ("pos", True, True),
-        }
-        with open(filepath, encoding="utf-8") as tsvfile:
-            tsvreader = csv.reader(tsvfile, delimiter="\t")
-            for id_, row in enumerate(tsvreader):
-                question, sentence, orig_label = row
-                label, sentence_in_long_answer, short_answer_in_sentence = label_map[orig_label]
-                yield id_, {
-                    "question": question,
-                    "sentence": sentence,
-                    "label": label,
-                    "sentence_in_long_answer": sentence_in_long_answer,
-                    "short_answer_in_sentence": short_answer_in_sentence,
-                }

dataset_infos.json

@@ -1 +0,0 @@
-{"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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