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:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+---
+
+# Dataset Card Creation Guide
+
+## 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:** [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125)
+- **Repository:** [Github repository](https://github.com/CogComp/MCTACO)
+- **Paper:** ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065)
+- **Leaderboard:** [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco)
+
+### Dataset Summary
+
+MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible.
+
+### Supported Tasks and Leaderboards
+
+The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no").
+
+Performance is measured using two metrics:
+
+- Exact Match -- the average number of questions for which all the candidate answers are predicted correctly.
+- F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall.
+
+### Languages
+
+The text in the dataset is in English. The associated BCP-47 code is `en`.
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks like this:
+
+```
+{
+  "sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.",
+  "question": "How often did Abdalonymus die?",
+  "answer": "every two years",
+  "label": "no",
+  "category": "Frequency",
+}
+```
+
+### Data Fields
+
+All fields are strings:
+- `sentence`: a sentence (or context) on which the question is based
+- `question`: a question querying some temporal commonsense knowledge
+- `answer`: a potential answer to the question (all lowercased)
+- `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise
+- `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time")
+
+### Data Splits
+
+The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers.
+
+From the original repository:
+
+*Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.*
+
+## Dataset Creation
+
+### Curation Rationale
+
+MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems.
+
+### Source Data
+
+From the original paper:
+
+*The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).*
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+From the original paper:
+
+*To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.*
+
+#### Annotation process
+
+The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling.
+
+#### Who are the annotators?
+
+Paid crowdsourcers.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Unknwon
+
+### Citation Information
+
+```
+@inproceedings{ZKNR19,
+    author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
+    title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
+    booktitle = {EMNLP},
+    year = {2019},
+}
+```

dataset_infos.json

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+{"plain_text": {"description": "MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer\npairs that require temporal commonsense comprehension. A system receives a sentence\nproviding context information, a question designed to require temporal commonsense\nknowledge, and multiple candidate answers. More than one candidate answer can be plausible.\n\nThe task is framed as binary classification: givent he context, the question,\nand the candidate answer, the task is to determine whether the candidate\nanswer is plausible (\"yes\") or not (\"no\").", "citation": "@inproceedings{ZKNR19,\n    author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},\n    title = {\u201cGoing on a vacation\u201d takes longer than \u201cGoing for a walk\u201d: A Study of Temporal Commonsense Understanding },\n    booktitle = {EMNLP},\n    year = {2019},\n}\n", "homepage": "https://cogcomp.seas.upenn.edu/page/resource_view/125", "license": "Unknown", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["no", "yes"], "names_file": null, "id": null, "_type": "ClassLabel"}, "category": {"num_classes": 5, "names": ["Event Duration", "Event Ordering", "Frequency", "Typical Time", "Stationarity"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "mc_taco", "config_name": "plain_text", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1785553, "num_examples": 9442, "dataset_name": "mc_taco"}, "validation": {"name": "validation", "num_bytes": 713023, "num_examples": 3783, "dataset_name": "mc_taco"}}, "download_checksums": {"https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/dev_3783.tsv": {"num_bytes": 679912, "checksum": "8de54f6d3e0a6466e4ba2c5179c7f9ac3442eeba8683c46fd712f5f54751d6dd"}, "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/test_9442.tsv": {"num_bytes": 1705225, "checksum": "47e12f88559eb0735eeca2af2d0a3ed48efb3bb2742ff31de9fcfc9a76094354"}}, "download_size": 2385137, "post_processing_size": null, "dataset_size": 2498576, "size_in_bytes": 4883713}}

dummy/plain_text/1.1.0/dummy_data.zip

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+oid sha256:95b49c8911ceffd65aebac490daa338e902e1f8def741fcd132fc71535b9d650
+size 711

mc_taco.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.
+"""MC-TACO Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{ZKNR19,
+    author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
+    title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
+    booktitle = {EMNLP},
+    year = {2019},
+}
+"""
+
+_DESCRIPTION = """\
+MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
+pairs that require temporal commonsense comprehension. A system receives a sentence
+providing context information, a question designed to require temporal commonsense
+knowledge, and multiple candidate answers. More than one candidate answer can be plausible.
+
+The task is framed as binary classification: givent he context, the question,
+and the candidate answer, the task is to determine whether the candidate
+answer is plausible ("yes") or not ("no")."""
+
+_LICENSE = "Unknown"
+
+_URLs = {
+    "dev": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/dev_3783.tsv",
+    "test": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/test_9442.tsv",
+}
+
+
+class McTaco(datasets.GeneratorBasedBuilder):
+    """MC-TACO Dataset: temporal commonsense knowledge."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            description="Plain text",
+            version=VERSION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["no", "yes"]),
+                    "category": datasets.ClassLabel(
+                        names=["Event Duration", "Event Ordering", "Frequency", "Typical Time", "Stationarity"]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://cogcomp.seas.upenn.edu/page/resource_view/125",
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["dev"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                delimiter="\t",
+            )
+            for id_, row in enumerate(csv_reader):
+                yield id_, {
+                    "sentence": row[0],
+                    "question": row[1],
+                    "answer": row[2],
+                    "label": row[3],
+                    "category": row[4],
+                }