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README.md

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----
-annotations_creators:
-- crowdsourced
-language:
-- en
-language_creators:
-- found
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-pretty_name: Question Answering via Sentence Composition (QASC)
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- question-answering
-- multiple-choice
-task_ids:
-- extractive-qa
-- multiple-choice-qa
-paperswithcode_id: qasc
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: choices
-    sequence:
-    - name: text
-      dtype: string
-    - name: label
-      dtype: string
-  - name: answerKey
-    dtype: string
-  - name: fact1
-    dtype: string
-  - name: fact2
-    dtype: string
-  - name: combinedfact
-    dtype: string
-  - name: formatted_question
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 393683
-    num_examples: 920
-  - name: train
-    num_bytes: 4919377
-    num_examples: 8134
-  - name: validation
-    num_bytes: 562352
-    num_examples: 926
-  download_size: 1616514
-  dataset_size: 5875412
----
-
-# Dataset Card for "qasc"
-
-## 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://allenai.org/data/qasc](https://allenai.org/data/qasc)
-- **Repository:** https://github.com/allenai/qasc/
-- **Paper:** [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473)
-- **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:** 1.54 MB
-- **Size of the generated dataset:** 5.60 MB
-- **Total amount of disk used:** 7.14 MB
-
-### Dataset Summary
-
-QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
-questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
-
-### 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:** 1.54 MB
-- **Size of the generated dataset:** 5.60 MB
-- **Total amount of disk used:** 7.14 MB
-
-An example of 'validation' looks as follows.
-```
-{
-    "answerKey": "F",
-    "choices": {
-        "label": ["A", "B", "C", "D", "E", "F", "G", "H"],
-        "text": ["sand", "occurs over a wide range", "forests", "Global warming", "rapid changes occur", "local weather conditions", "measure of motion", "city life"]
-    },
-    "combinedfact": "Climate is generally described in terms of local weather conditions",
-    "fact1": "Climate is generally described in terms of temperature and moisture.",
-    "fact2": "Fire behavior is driven by local weather conditions such as winds, temperature and moisture.",
-    "formatted_question": "Climate is generally described in terms of what? (A) sand (B) occurs over a wide range (C) forests (D) Global warming (E) rapid changes occur (F) local weather conditions (G) measure of motion (H) city life",
-    "id": "3NGI5ARFTT4HNGVWXAMLNBMFA0U1PG",
-    "question": "Climate is generally described in terms of what?"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `id`: a `string` feature.
-- `question`: a `string` feature.
-- `choices`: a dictionary feature containing:
-  - `text`: a `string` feature.
-  - `label`: a `string` feature.
-- `answerKey`: a `string` feature.
-- `fact1`: a `string` feature.
-- `fact2`: a `string` feature.
-- `combinedfact`: a `string` feature.
-- `formatted_question`: a `string` feature.
-
-### Data Splits
-
-| name  |train|validation|test|
-|-------|----:|---------:|---:|
-|default| 8134|       926| 920|
-
-## 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 dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
-
-### Citation Information
-
-```
-@article{allenai:qasc,
-      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
-      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
-      journal   = {arXiv:1910.11473v2},
-      year      = {2020},
-}
-```
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

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-{"default": {"description": "\nQASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice \nquestions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.\n", "citation": "@article{allenai:qasc,\n      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},\n      title     = {QASC: A Dataset for Question Answering via Sentence Composition},\n      journal   = {arXiv:1910.11473v2},\n      year      = {2020},\n}\n", "homepage": "https://allenai.org/data/qasc", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}, "fact1": {"dtype": "string", "id": null, "_type": "Value"}, "fact2": {"dtype": "string", "id": null, "_type": "Value"}, "combinedfact": {"dtype": "string", "id": null, "_type": "Value"}, "formatted_question": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "qasc", "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": 393683, "num_examples": 920, "dataset_name": "qasc"}, "train": {"name": "train", "num_bytes": 4919377, "num_examples": 8134, "dataset_name": "qasc"}, "validation": {"name": "validation", "num_bytes": 562352, "num_examples": 926, "dataset_name": "qasc"}}, "download_checksums": {"http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz": {"num_bytes": 1616514, "checksum": "a7b3f2244f768974c609fd621346c931a72715609f171cb5544fc1da2a2ad55c"}}, "download_size": 1616514, "dataset_size": 5875412, "size_in_bytes": 7491926}}

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qasc.py

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-"""TODO(qasc): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO(qasc): BibTeX citation
-_CITATION = """\
-@article{allenai:qasc,
-      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
-      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
-      journal   = {arXiv:1910.11473v2},
-      year      = {2020},
-}
-"""
-
-# TODO(qasc):
-_DESCRIPTION = """
-QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
-questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
-"""
-_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"
-
-
-class Qasc(datasets.GeneratorBasedBuilder):
-    """TODO(qasc): Short description of my dataset."""
-
-    # TODO(qasc): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(qasc): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "choices": datasets.features.Sequence(
-                        {"text": datasets.Value("string"), "label": datasets.Value("string")}
-                    ),
-                    "answerKey": datasets.Value("string"),
-                    "fact1": datasets.Value("string"),
-                    "fact2": datasets.Value("string"),
-                    "combinedfact": datasets.Value("string"),
-                    "formatted_question": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://allenai.org/data/qasc",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(qasc): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        archive = dl_manager.download(_URl)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "train.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "test.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "dev.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        # TODO(qasc): Yields (key, example) tuples from the dataset
-        for path, f in files:
-            if path == filepath:
-                for row in f:
-                    data = json.loads(row.decode("utf-8"))
-                    answerkey = data.get("answerKey", "")
-                    id_ = data["id"]
-                    question = data["question"]["stem"]
-                    choices = data["question"]["choices"]
-                    text_choices = [choice["text"] for choice in choices]
-                    label_choices = [choice["label"] for choice in choices]
-                    fact1 = data.get("fact1", "")
-                    fact2 = data.get("fact2", "")
-                    combined_fact = data.get("combinedfact", "")
-                    formatted_question = data.get("formatted_question", "")
-                    yield id_, {
-                        "id": id_,
-                        "answerKey": answerkey,
-                        "question": question,
-                        "choices": {"text": text_choices, "label": label_choices},
-                        "fact1": fact1,
-                        "fact2": fact2,
-                        "combinedfact": combined_fact,
-                        "formatted_question": formatted_question,
-                    }
-                break