Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+- extractive-qa
+---
+
+
+# Dataset Card for [covid_qa_castorini]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** https://covidqa.ai
+- **Repository:** https://github.com/castorini/pygaggle
+- **Paper:** https://arxiv.org/abs/2004.11339
+- **Point of Contact:** [Castorini research group @UWaterloo](https://github.com/castorini/)
+
+### Dataset Summary
+
+CovidQA is a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered 
+from Kaggle’s COVID-19 Open Research Dataset Challenge.
+The dataset comprises 156 question-article pairs with 27 questions (topics) and 85 unique articles.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+**What do the instances that comprise the dataset represent?**
+Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
+
+**How many instances are there in total?**
+
+**What data does each instance consist of?**
+Each instance is a query (natural language question and keyword-based), a set of answers, and a document id with its title associated with each answer.
+
+[More Information Needed]
+
+### Data Fields
+
+The data was annotated in SQuAD style fashion, where each row contains:
+
+* **question_query**: Natural language question query
+* **keyword_query**: Keyword-based query
+* **category_name**: Category in which the queries are part of
+* **answers**: List of answers
+  * **id**: The document ID the answer is found on
+  * **title**: Title of the document of the answer
+  * **exact_answer**: Text (string) of the exact answer
+
+### Data Splits
+
+**data/kaggle-lit-review-0.2.json**: 156 question-article pairs with 27 questions (topics) and 85 unique articles from
+CORD-19.
+
+[More Information Needed]
+
+## Dataset Creation
+
+The dataset aims to help for guiding research until more substantial evaluation resources become available. Being a smaller dataset,
+it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19.
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+Five of the co-authors participated in this annotation effort, applying the aforementioned approach, with one lead 
+annotator responsible for approving topics and answering technical questions from the other annotators. Two annotators are
+undergraduate students majoring in computer science, one is a science alumna, another is a computer science professor, 
+and the lead annotator is a graduate student in computer science—all affiliated with the University of Waterloo.
+
+#### Annotation process
+
+#### Who are the annotators?
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset was intended as a stopgap measure for guiding research until more substantial evaluation resources become available.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+While this dataset, comprising 124 question–article pairs as of the present version 0.1 release, does not have sufficient
+examples for supervised machine learning, it can be helpful for evaluating the zero-shot or transfer capabilities
+of existing models on topics specifically related to COVID-19.
+
+## Additional Information
+
+The listed authors in the homepage are maintaining/supporting the dataset. 
+
+### Dataset Curators
+
+[More Information Needed]
+
+The covidqa dataset is licensed under 
+the [Apache License 2.0](https://github.com/castorini/pygaggle/blob/master/LICENSE)
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@article{tang2020rapidly,
+  title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
+  author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
+  journal={arXiv preprint arXiv:2004.11339},
+  year={2020}
+}
+```

covid_qa_castorini.py

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+# 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.
+"""CovidQA, a question answering dataset specifically designed for COVID-19."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import logging
+
+import datasets
+
+
+_CITATION = """\
+@article{tang2020rapidly,
+  title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
+  author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
+  journal={arXiv preprint arXiv:2004.11339},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from \
+knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.
+"""
+
+_HOMEPAGE = "http://covidqa.ai"
+
+_LICENSE = "Apache License 2.0"
+
+_URL = "https://raw.githubusercontent.com/castorini/pygaggle/master/data/"
+_URLs = {"covid_qa_castorini": _URL + "kaggle-lit-review-0.2.json"}
+
+
+class CovidQaCastorini(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.2.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="covid_qa_castorini",
+            version=VERSION,
+            description="CovidQA, a question answering dataset specifically designed for COVID-19",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "category_name": datasets.Value("string"),
+                "question_query": datasets.Value("string"),
+                "keyword_query": datasets.Value("string"),
+                "answers": datasets.features.Sequence(
+                    {
+                        "id": datasets.Value("string"),
+                        "title": datasets.Value("string"),
+                        "exact_answer": datasets.Value("string"),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url = _URLs[self.config.name]
+        downloaded_filepath = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_filepath},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            covid_qa = json.load(f)
+            for article in covid_qa["categories"]:
+                category_name = article["name"]
+                for idx, paragraph in enumerate(article["sub_categories"]):
+                    question_query = paragraph["nq_name"]
+                    keyword_query = paragraph["kq_name"]
+
+                    ids = [answer["id"] for answer in paragraph["answers"]]
+                    titles = [answer["title"] for answer in paragraph["answers"]]
+                    exact_answers = [answer["exact_answer"] for answer in paragraph["answers"]]
+
+                    yield idx, {
+                        "category_name": category_name,
+                        "question_query": question_query,
+                        "keyword_query": keyword_query,
+                        "answers": {
+                            "id": ids,
+                            "title": titles,
+                            "exact_answer": exact_answers,
+                        },
+                    }

dataset_infos.json

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+{"covid_qa_deepset": {"description": "COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.\n", "citation": "@inproceedings{moller2020covid,\n  title={COVID-QA: A Question Answering Dataset for COVID-19},\n  author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},\n  booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},\n  year={2020}\n}\n", "homepage": "https://github.com/deepset-ai/COVID-QA", "license": "Apache License 2.0", "features": {"document_id": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "is_impossible": {"dtype": "bool", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_deepset", "config_name": "covid_qa_deepset", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65151262, "num_examples": 2019, "dataset_name": "covid_qa_deepset"}}, "download_checksums": {"https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/COVID-QA.json": {"num_bytes": 4418117, "checksum": "291abf17f4bc2bd343838fd8ef5debb6278bbbb61b262db1f1bd58048fff76b9"}}, "download_size": 4418117, "post_processing_size": null, "dataset_size": 65151262, "size_in_bytes": 69569379}, "covidqa": {"description": "CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.\n", "citation": "@article{tang2020rapidly,\n  title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},\n  author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},\n  journal={arXiv preprint arXiv:2004.11339},\n  year={2020}\n}\n", "homepage": "http://covidqa.ai", "license": "Apache License 2.0", "features": {"category_name": {"dtype": "string", "id": null, "_type": "Value"}, "question_query": {"dtype": "string", "id": null, "_type": "Value"}, "keyword_query": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "exact_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_castorini", "config_name": "covidqa", "version": {"version_str": "0.2.0", "description": null, "major": 0, "minor": 2, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33757, "num_examples": 27, "dataset_name": "covid_qa_castorini"}}, "download_checksums": {"https://raw.githubusercontent.com/castorini/pygaggle/master/data/kaggle-lit-review-0.2.json": {"num_bytes": 51438, "checksum": "b998dee956c4592a63828c628d1a369e6a81b8527e384a9d3448f417008080fb"}}, "download_size": 51438, "post_processing_size": null, "dataset_size": 33757, "size_in_bytes": 85195}, "covid_qa_castorini": {"description": "CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.\n", "citation": "@article{tang2020rapidly,\n  title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},\n  author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},\n  journal={arXiv preprint arXiv:2004.11339},\n  year={2020}\n}\n", "homepage": "http://covidqa.ai", "license": "Apache License 2.0", "features": {"category_name": {"dtype": "string", "id": null, "_type": "Value"}, "question_query": {"dtype": "string", "id": null, "_type": "Value"}, "keyword_query": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "exact_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_castorini", "config_name": "covid_qa_castorini", "version": {"version_str": "0.2.0", "description": null, "major": 0, "minor": 2, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33757, "num_examples": 27, "dataset_name": "covid_qa_castorini"}}, "download_checksums": {"https://raw.githubusercontent.com/castorini/pygaggle/master/data/kaggle-lit-review-0.2.json": {"num_bytes": 51438, "checksum": "b998dee956c4592a63828c628d1a369e6a81b8527e384a9d3448f417008080fb"}}, "download_size": 51438, "post_processing_size": null, "dataset_size": 33757, "size_in_bytes": 85195}}

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