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README.md

@@ -1,365 +0,0 @@
----
-language:
-- en
-paperswithcode_id: winogrande
-pretty_name: WinoGrande
-dataset_info:
-- config_name: winogrande_xs
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 20704
-    num_examples: 160
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 412552
-- config_name: winogrande_s
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 82308
-    num_examples: 640
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 474156
-- config_name: winogrande_m
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 329001
-    num_examples: 2558
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 720849
-- config_name: winogrande_l
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1319576
-    num_examples: 10234
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 1711424
-- config_name: winogrande_xl
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 5185832
-    num_examples: 40398
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 5577680
-- config_name: winogrande_debiased
-  features:
-  - name: sentence
-    dtype: string
-  - name: option1
-    dtype: string
-  - name: option2
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1203420
-    num_examples: 9248
-  - name: test
-    num_bytes: 227649
-    num_examples: 1767
-  - name: validation
-    num_bytes: 164199
-    num_examples: 1267
-  download_size: 3395492
-  dataset_size: 1595268
----
-
-# Dataset Card for "winogrande"
-
-## 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:** [https://leaderboard.allenai.org/winogrande/submissions/get-started](https://leaderboard.allenai.org/winogrande/submissions/get-started)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 20.37 MB
-- **Size of the generated dataset:** 10.50 MB
-- **Total amount of disk used:** 30.87 MB
-
-### Dataset Summary
-
-WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
- 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
-fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
-commonsense reasoning.
-
-### 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
-
-#### winogrande_debiased
-
-- **Size of downloaded dataset files:** 3.40 MB
-- **Size of the generated dataset:** 1.59 MB
-- **Total amount of disk used:** 4.99 MB
-
-An example of 'train' looks as follows.
-```
-
-```
-
-#### winogrande_l
-
-- **Size of downloaded dataset files:** 3.40 MB
-- **Size of the generated dataset:** 1.71 MB
-- **Total amount of disk used:** 5.11 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### winogrande_m
-
-- **Size of downloaded dataset files:** 3.40 MB
-- **Size of the generated dataset:** 0.72 MB
-- **Total amount of disk used:** 4.12 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### winogrande_s
-
-- **Size of downloaded dataset files:** 3.40 MB
-- **Size of the generated dataset:** 0.47 MB
-- **Total amount of disk used:** 3.87 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### winogrande_xl
-
-- **Size of downloaded dataset files:** 3.40 MB
-- **Size of the generated dataset:** 5.58 MB
-- **Total amount of disk used:** 8.98 MB
-
-An example of 'train' looks as follows.
-```
-
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### winogrande_debiased
-- `sentence`: a `string` feature.
-- `option1`: a `string` feature.
-- `option2`: a `string` feature.
-- `answer`: a `string` feature.
-
-#### winogrande_l
-- `sentence`: a `string` feature.
-- `option1`: a `string` feature.
-- `option2`: a `string` feature.
-- `answer`: a `string` feature.
-
-#### winogrande_m
-- `sentence`: a `string` feature.
-- `option1`: a `string` feature.
-- `option2`: a `string` feature.
-- `answer`: a `string` feature.
-
-#### winogrande_s
-- `sentence`: a `string` feature.
-- `option1`: a `string` feature.
-- `option2`: a `string` feature.
-- `answer`: a `string` feature.
-
-#### winogrande_xl
-- `sentence`: a `string` feature.
-- `option1`: a `string` feature.
-- `option2`: a `string` feature.
-- `answer`: a `string` feature.
-
-### Data Splits
-
-|       name        |train|validation|test|
-|-------------------|----:|---------:|---:|
-|winogrande_debiased| 9248|      1267|1767|
-|winogrande_l       |10234|      1267|1767|
-|winogrande_m       | 2558|      1267|1767|
-|winogrande_s       |  640|      1267|1767|
-|winogrande_xl      |40398|      1267|1767|
-|winogrande_xs      |  160|      1267|1767|
-
-## 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
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@InProceedings{ai2:winogrande,
-title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
-authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
-},
-year={2019}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@TevenLeScao](https://github.com/TevenLeScao), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"winogrande_xs": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_xs", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 20704, "num_examples": 160, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 412552, "size_in_bytes": 3808044}, "winogrande_s": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_s", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 82308, "num_examples": 640, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 474156, "size_in_bytes": 3869648}, "winogrande_m": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_m", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 329001, "num_examples": 2558, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 720849, "size_in_bytes": 4116341}, "winogrande_l": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_l", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1319576, "num_examples": 10234, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 1711424, "size_in_bytes": 5106916}, "winogrande_xl": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_xl", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5185832, "num_examples": 40398, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 5577680, "size_in_bytes": 8973172}, "winogrande_debiased": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_debiased", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1203420, "num_examples": 9248, "dataset_name": "winogrande"}, "test": {"name": "test", "num_bytes": 227649, "num_examples": 1767, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164199, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 3395492, "checksum": "3619ab104d8be2977b25c90ff420cb42d491707dcc75362a1e5d22bc082b7318"}}, "download_size": 3395492, "post_processing_size": null, "dataset_size": 1595268, "size_in_bytes": 4990760}}

winogrande.py

@@ -1,145 +0,0 @@
-"""TODO(winogrande): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(winogrande): BibTeX citation
-_CITATION = """\
-@InProceedings{ai2:winogrande,
-title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
-authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
-},
-year={2019}
-}
-"""
-
-# TODO(winogrande):
-_DESCRIPTION = """\
-WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
- 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
-fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
-commonsense reasoning.
-"""
-
-_URL = "https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip"
-_FORMATS = ["xs", "s", "m", "l", "xl", "debiased"]
-
-
-class WinograndeConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Discofuse"""
-
-    def __init__(self, data_size, **kwargs):
-        """
-
-        Args:
-            data_size: the format of the training set we want to use (xs, s, m, l, xl, debiased)
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(WinograndeConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
-        self.data_size = data_size
-
-
-class Winogrande(datasets.GeneratorBasedBuilder):
-    """TODO(winogrande): Short description of my dataset."""
-
-    # TODO(winogrande): Set up version.
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        WinograndeConfig(name="winogrande_" + data_size, description="AI2 dataset", data_size=data_size)
-        for data_size in _FORMATS
-    ]
-
-    def _info(self):
-        # TODO(winogrande): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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="https://leaderboard.allenai.org/winogrande/submissions/get-started",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(winogrande): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "winogrande_1.1")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, f"train_{self.config.data_size}.jsonl"),
-                    # 'labelpath': os.path.join(data_dir, 'train_{}-labels.lst'.format(self.config.data_size)),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    # 'labelpath': os.path.join(data_dir, 'dev-labels.lst'),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        # TODO(winogrande): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                if split == "test":
-                    yield id_, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "option2": data["option2"],
-                        "answer": "",
-                    }
-                else:
-                    yield id_, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "option2": data["option2"],
-                        "answer": data["answer"],
-                    }
-
-
-# def _generate_test_example(filepath, split, labelpath=None):
-#       with open(filepath, encoding="utf-8") as f:
-#           for id_, row in enumerate(f):
-#               data = json.loads(row)
-#               yield id_,{
-#                   'sentence': data['sentence'],
-#                   'option1': data['option1'],
-#                   'option2': data['option2'],
-#                   'answer': None
-#               }

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