Duplicate from mwsc

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README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Modified Winograd Schema Challenge (MWSC)
+size_categories:
+- n<1K
+source_datasets:
+- extended|winograd_wsc
+task_categories:
+- multiple-choice
+task_ids:
+- multiple-choice-coreference-resolution
+paperswithcode_id: null
+dataset_info:
+  features:
+  - name: sentence
+    dtype: string
+  - name: question
+    dtype: string
+  - name: options
+    sequence: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 11022
+    num_examples: 80
+  - name: test
+    num_bytes: 15220
+    num_examples: 100
+  - name: validation
+    num_bytes: 13109
+    num_examples: 82
+  download_size: 19197
+  dataset_size: 39351
+---
+
+# Dataset Card for The modified Winograd Schema Challenge (MWSC)
+
+## 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:** [http://decanlp.com](http://decanlp.com)
+- **Repository:** https://github.com/salesforce/decaNLP
+- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
+- **Point of Contact:** [Bryan McCann](mailto:bmccann@salesforce.com), [Nitish Shirish Keskar](mailto:nkeskar@salesforce.com)
+- **Size of downloaded dataset files:** 19.20 kB
+- **Size of the generated dataset:** 39.35 kB
+- **Total amount of disk used:** 58.55 kB
+
+### Dataset Summary
+
+Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
+This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
+
+### 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:** 0.02 MB
+- **Size of the generated dataset:** 0.04 MB
+- **Total amount of disk used:** 0.06 MB
+
+An example looks as follows:
+```
+{
+    "sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
+    "question": "Who feared violence?",
+    "options": [ "councilmen", "demonstrators" ],
+    "answer": "councilmen"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `sentence`: a `string` feature.
+- `question`: a `string` feature.
+- `options`: a `list` of `string` features.
+- `answer`: a `string` feature.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|   80|        82| 100|
+
+## 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
+
+Our code for running decaNLP has been open sourced under BSD-3-Clause. 
+
+We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
+
+From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
+> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+If you use this in your work, please cite:
+```
+@article{McCann2018decaNLP,
+  title={The Natural Language Decathlon: Multitask Learning as Question Answering},
+  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:1806.08730},
+  year={2018}
+}
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.\nThis modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.\n", "citation": "@article{McCann2018decaNLP,\n  title={The Natural Language Decathlon: Multitask Learning as Question Answering},\n  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},\n  journal={arXiv preprint arXiv:1806.08730},\n  year={2018}\n}\n", "homepage": "http://decanlp.com", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "mwsc", "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": 11022, "num_examples": 80, "dataset_name": "mwsc"}, "test": {"name": "test", "num_bytes": 15220, "num_examples": 100, "dataset_name": "mwsc"}, "validation": {"name": "validation", "num_bytes": 13109, "num_examples": 82, "dataset_name": "mwsc"}}, "download_checksums": {"https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt": {"num_bytes": 19197, "checksum": "31da9bee05796bbe0f6c957f54d1eb82eb5c644a8ee59f2ff1fa890eff3885dd"}}, "download_size": 19197, "dataset_size": 39351, "size_in_bytes": 58548}}

mwsc.py

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+"""A modification of the Winograd Schema Challenge to ensure answers are a single context word"""
+
+import os
+import re
+
+import datasets
+
+
+_CITATION = """\
+@article{McCann2018decaNLP,
+  title={The Natural Language Decathlon: Multitask Learning as Question Answering},
+  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:1806.08730},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """\
+Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
+This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
+"""
+
+_DATA_URL = "https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt"
+# Alternate: https://s3.amazonaws.com/research.metamind.io/decaNLP/data/schema.txt
+
+
+class MWSC(datasets.GeneratorBasedBuilder):
+    """MWSC: modified Winograd Schema Challenge"""
+
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "options": datasets.features.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                }
+            ),
+            # 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="http://decanlp.com",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        schemas_file = dl_manager.download_and_extract(_DATA_URL)
+
+        if os.path.isdir(schemas_file):
+            # During testing the download manager mock gives us a directory
+            schemas_file = os.path.join(schemas_file, "schema.txt")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": schemas_file, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": schemas_file, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": schemas_file, "split": "dev"},
+            ),
+        ]
+
+    def _get_both_schema(self, context):
+        """Split [option1/option2] into 2 sentences.
+        From https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L815-L827"""
+        pattern = r"\[.*\]"
+        variations = [x[1:-1].split("/") for x in re.findall(pattern, context)]
+        splits = re.split(pattern, context)
+        results = []
+        for which_schema in range(2):
+            vs = [v[which_schema] for v in variations]
+            context = ""
+            for idx in range(len(splits)):
+                context += splits[idx]
+                if idx < len(vs):
+                    context += vs[idx]
+            results.append(context)
+        return results
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        schemas = []
+        with open(filepath, encoding="utf-8") as schema_file:
+            schema = []
+            for line in schema_file:
+                if len(line.split()) == 0:
+                    schemas.append(schema)
+                    schema = []
+                    continue
+                else:
+                    schema.append(line.strip())
+
+        # Train/test/dev split from decaNLP code
+        splits = {}
+        traindev = schemas[:-50]
+        splits["test"] = schemas[-50:]
+        splits["train"] = traindev[:40]
+        splits["dev"] = traindev[40:]
+
+        idx = 0
+        for schema in splits[split]:
+            sentence, question, answers = schema
+            sentence = self._get_both_schema(sentence)
+            question = self._get_both_schema(question)
+            answers = answers.split("/")
+            for i in range(2):
+                yield idx, {"sentence": sentence[i], "question": question[i], "options": answers, "answer": answers[i]}
+                idx += 1