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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: Quoref
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids: []
-paperswithcode_id: quoref
-tags:
-- coreference-resolution
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: context
-    dtype: string
-  - name: title
-    dtype: string
-  - name: url
-    dtype: string
-  - name: answers
-    sequence:
-    - name: answer_start
-      dtype: int32
-    - name: text
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 44377729
-    num_examples: 19399
-  - name: validation
-    num_bytes: 5442031
-    num_examples: 2418
-  download_size: 5078438
-  dataset_size: 49819760
----
-
-# Dataset Card for "quoref"
-
-## 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/quoref
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://aclanthology.org/D19-1606/)
-- **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:** 4.84 MB
-- **Size of the generated dataset:** 47.51 MB
-- **Total amount of disk used:** 52.36 MB
-
-### Dataset Summary
-
-Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
-span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
-coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
-
-### 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:** 4.84 MB
-- **Size of the generated dataset:** 47.51 MB
-- **Total amount of disk used:** 52.36 MB
-
-An example of 'validation' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "answers": {
-        "answer_start": [1633],
-        "text": ["Frankie"]
-    },
-    "context": "\"Frankie Bono, a mentally disturbed hitman from Cleveland, comes back to his hometown in New York City during Christmas week to ...",
-    "id": "bfc3b34d6b7e73c0bd82a009db12e9ce196b53e6",
-    "question": "What is the first name of the person who has until New Year's Eve to perform a hit?",
-    "title": "Blast of Silence",
-    "url": "https://en.wikipedia.org/wiki/Blast_of_Silence"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `id`: a `string` feature.
-- `question`: a `string` feature.
-- `context`: a `string` feature.
-- `title`: a `string` feature.
-- `url`: a `string` feature.
-- `answers`: a dictionary feature containing:
-  - `answer_start`: a `int32` feature.
-  - `text`: a `string` feature.
-
-### Data Splits
-
-| name  |train|validation|
-|-------|----:|---------:|
-|default|19399|      2418|
-
-## 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
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@article{allenai:quoref,
-      author    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},
-      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
-      journal   = {arXiv:1908.05803v2 },
-      year      = {2019},
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this \nspan-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard \ncoreferences before selecting the appropriate span(s) in the paragraphs for answering questions.\n", "citation": "@article{allenai:quoref,\n      author    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},\n      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},\n      journal   = {arXiv:1908.05803v2 },\n      year      = {2019},\n}\n", "homepage": "https://leaderboard.allenai.org/quoref/submissions/get-started", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "quoref", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 44377729, "num_examples": 19399, "dataset_name": "quoref"}, "validation": {"name": "validation", "num_bytes": 5442031, "num_examples": 2418, "dataset_name": "quoref"}}, "download_checksums": {"https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip": {"num_bytes": 5078438, "checksum": "aacde0863c04ba6e9ab46995ea844a5b0c6cea58a77ab6fd86a128e33a3ad8fb"}}, "download_size": 5078438, "dataset_size": 49819760, "size_in_bytes": 54898198}}

default/quoref-train.parquet

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default/quoref-validation.parquet

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quoref.py

@@ -1,116 +0,0 @@
-"""TODO(quoref): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(quoref): BibTeX citation
-_CITATION = """\
-@article{allenai:quoref,
-      author    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},
-      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
-      journal   = {arXiv:1908.05803v2 },
-      year      = {2019},
-}
-"""
-
-# TODO(quoref):
-_DESCRIPTION = """\
-Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
-span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
-coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
-"""
-
-_URL = "https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip"
-
-
-class Quoref(datasets.GeneratorBasedBuilder):
-    """TODO(quoref): Short description of my dataset."""
-
-    # TODO(quoref): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(quoref): 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"),
-                    "context": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "url": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "answer_start": datasets.Value("int32"),
-                            "text": 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://leaderboard.allenai.org/quoref/submissions/get-started",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(quoref): Downloads the data and defines the splits
-        # 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, "quoref-train-dev-v0.1")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "quoref-train-v0.1.json")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "quoref-dev-v0.1.json")},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(quoref): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for article in data["data"]:
-                title = article.get("title", "").strip()
-                url = article.get("url", "").strip()
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                            "url": url,
-                        }