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.gitattributes

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README.md

@@ -1,334 +0,0 @@
----
-annotations_creators:
-- crowdsourced
-language_creators:
-- crowdsourced
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids:
-- multiple-choice-qa
-paperswithcode_id: codah
-pretty_name: COmmonsense Dataset Adversarially-authored by Humans
-dataset_info:
-- config_name: codah
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 571208
-    num_examples: 2776
-  download_size: 485130
-  dataset_size: 571208
-- config_name: fold_0
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 344912
-    num_examples: 1665
-  - name: validation
-    num_bytes: 114211
-    num_examples: 556
-  - name: test
-    num_bytes: 112109
-    num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
-- config_name: fold_1
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 340990
-    num_examples: 1665
-  - name: validation
-    num_bytes: 114211
-    num_examples: 556
-  - name: test
-    num_bytes: 116031
-    num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
-- config_name: fold_2
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 342293
-    num_examples: 1665
-  - name: validation
-    num_bytes: 114211
-    num_examples: 556
-  - name: test
-    num_bytes: 114728
-    num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
-- config_name: fold_3
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 342844
-    num_examples: 1665
-  - name: validation
-    num_bytes: 114211
-    num_examples: 556
-  - name: test
-    num_bytes: 114177
-    num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
-- config_name: fold_4
-  features:
-  - name: id
-    dtype: int32
-  - name: question_category
-    dtype:
-      class_label:
-        names:
-          0: Idioms
-          1: Reference
-          2: Polysemy
-          3: Negation
-          4: Quantitative
-          5: Others
-  - name: question_propmt
-    dtype: string
-  - name: candidate_answers
-    sequence: string
-  - name: correct_answer_idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 342844
-    num_examples: 1665
-  - name: validation
-    num_bytes: 114177
-    num_examples: 555
-  - name: test
-    num_bytes: 114211
-    num_examples: 556
-  download_size: 485130
-  dataset_size: 571232
----
-
-# Dataset Card for COmmonsense Dataset Adversarially-authored by Humans
-
-## 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:** [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]
-
-### Contributions
-
-Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

codah.py

@@ -1,141 +0,0 @@
-# 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)"""
-
-
-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

codah/codah-train.parquet

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dataset_infos.json

@@ -1 +0,0 @@
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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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