Update files from the datasets library (from 1.2.0)

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

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
+- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
+- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

codah.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.
+"""The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{chen2019codah,
+  title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
+  author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
+  booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
+  pages={63--69},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense \
+question-answering in the sentence completion style of SWAG. As opposed to other automatically \
+generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback \
+from a pre-trained model and use this information to design challenging commonsense questions. \
+Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
+"""
+
+_URL = "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/"
+_FULL_DATA_URL = _URL + "full_data.tsv"
+
+QUESTION_CATEGORIES_MAPPING = {
+    "i": "Idioms",
+    "r": "Reference",
+    "p": "Polysemy",
+    "n": "Negation",
+    "q": "Quantitative",
+    "o": "Others",
+}
+
+
+class CodahConfig(datasets.BuilderConfig):
+    """BuilderConfig for CODAH."""
+
+    def __init__(self, fold=None, **kwargs):
+        """BuilderConfig for CODAH.
+
+        Args:
+          fold: `string`, official cross validation fold.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CodahConfig, self).__init__(**kwargs)
+        self.fold = fold
+
+
+class Codah(datasets.GeneratorBasedBuilder):
+    """The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        CodahConfig(name="codah", version=datasets.Version("1.0.0"), description="Full CODAH dataset", fold=None),
+        CodahConfig(
+            name="fold_0", version=datasets.Version("1.0.0"), description="Official CV split (fold_0)", fold="fold_0"
+        ),
+        CodahConfig(
+            name="fold_1", version=datasets.Version("1.0.0"), description="Official CV split (fold_1)", fold="fold_1"
+        ),
+        CodahConfig(
+            name="fold_2", version=datasets.Version("1.0.0"), description="Official CV split (fold_2)", fold="fold_2"
+        ),
+        CodahConfig(
+            name="fold_3", version=datasets.Version("1.0.0"), description="Official CV split (fold_3)", fold="fold_3"
+        ),
+        CodahConfig(
+            name="fold_4", version=datasets.Version("1.0.0"), description="Official CV split (fold_4)", fold="fold_4"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "question_category": datasets.features.ClassLabel(
+                        names=["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"]
+                    ),
+                    "question_propmt": datasets.Value("string"),
+                    "candidate_answers": datasets.features.Sequence(datasets.Value("string")),
+                    "correct_answer_idx": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/Websail-NU/CODAH",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "codah":
+            data_file = dl_manager.download(_FULL_DATA_URL)
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
+
+        base_url = f"{_URL}cv_split/{self.config.fold}/"
+        _urls = {
+            "train": base_url + "train.tsv",
+            "dev": base_url + "dev.tsv",
+            "test": base_url + "test.tsv",
+        }
+        downloaded_files = dl_manager.download_and_extract(_urls)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_file": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, data_file):
+        with open(data_file, encoding="utf-8") as f:
+            rows = csv.reader(f, delimiter="\t")
+            for i, row in enumerate(rows):
+                question_category = QUESTION_CATEGORIES_MAPPING[row[0]] if row[0] != "" else -1
+                example = {
+                    "id": i,
+                    "question_category": question_category,
+                    "question_propmt": row[1],
+                    "candidate_answers": row[2:-1],
+                    "correct_answer_idx": int(row[-1]),
+                }
+                yield i, example

dataset_infos.json

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+{"codah": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n  title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n  author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n  booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n  pages={63--69},\n  year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "codah", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 571208, "num_examples": 2776, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/full_data.tsv": {"num_bytes": 485130, "checksum": "96689f2abf2f09eb91af50e2d8f92bb553e157601908a1cee83fe68670b5c3ea"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571208, "size_in_bytes": 1056338}, "fold_0": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. 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