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Duplicate from winogrande
Browse filesCo-authored-by: Parquet-converter (BOT) <[email protected]>
- .gitattributes +27 -0
- README.md +365 -0
- dataset_infos.json +1 -0
- winogrande.py +145 -0
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*.rar filter=lfs diff=lfs merge=lfs -text
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README.md
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+
---
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language:
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- en
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paperswithcode_id: winogrande
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pretty_name: WinoGrande
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dataset_info:
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- config_name: winogrande_xs
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features:
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- name: sentence
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dtype: string
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- name: option1
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dtype: string
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- name: option2
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dtype: string
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- name: answer
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dtype: string
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splits:
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- name: train
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num_bytes: 164199
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num_examples: 1267
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download_size: 3395492
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dataset_size: 412552
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- config_name: winogrande_s
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features:
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- name: sentence
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dtype: string
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dtype: string
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num_examples: 1267
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download_size: 3395492
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dataset_size: 474156
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- config_name: winogrande_m
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features:
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- name: sentence
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dtype: string
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dtype: string
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|
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|
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num_examples: 1267
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download_size: 3395492
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dataset_size: 720849
|
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- config_name: winogrande_l
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features:
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- name: sentence
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dtype: string
|
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- name: option1
|
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dtype: string
|
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- name: option2
|
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dtype: string
|
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- name: answer
|
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dtype: string
|
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splits:
|
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- name: train
|
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|
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- name: test
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|
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num_examples: 1267
|
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download_size: 3395492
|
94 |
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dataset_size: 1711424
|
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- config_name: winogrande_xl
|
96 |
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features:
|
97 |
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- name: sentence
|
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dtype: string
|
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|
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dtype: string
|
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dtype: string
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splits:
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|
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114 |
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num_examples: 1267
|
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download_size: 3395492
|
116 |
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dataset_size: 5577680
|
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- config_name: winogrande_debiased
|
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features:
|
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- name: sentence
|
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|
121 |
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|
122 |
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|
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|
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|
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|
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download_size: 3395492
|
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dataset_size: 1595268
|
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+
---
|
140 |
+
|
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+
# Dataset Card for "winogrande"
|
142 |
+
|
143 |
+
## Table of Contents
|
144 |
+
- [Dataset Description](#dataset-description)
|
145 |
+
- [Dataset Summary](#dataset-summary)
|
146 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
147 |
+
- [Languages](#languages)
|
148 |
+
- [Dataset Structure](#dataset-structure)
|
149 |
+
- [Data Instances](#data-instances)
|
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+
- [Data Fields](#data-fields)
|
151 |
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- [Data Splits](#data-splits)
|
152 |
+
- [Dataset Creation](#dataset-creation)
|
153 |
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- [Curation Rationale](#curation-rationale)
|
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- [Source Data](#source-data)
|
155 |
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- [Annotations](#annotations)
|
156 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
157 |
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- [Considerations for Using the Data](#considerations-for-using-the-data)
|
158 |
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- [Social Impact of Dataset](#social-impact-of-dataset)
|
159 |
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- [Discussion of Biases](#discussion-of-biases)
|
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- [Other Known Limitations](#other-known-limitations)
|
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- [Additional Information](#additional-information)
|
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- [Dataset Curators](#dataset-curators)
|
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- [Licensing Information](#licensing-information)
|
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- [Citation Information](#citation-information)
|
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+
- [Contributions](#contributions)
|
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+
|
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+
## Dataset Description
|
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+
|
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- **Homepage:** [https://leaderboard.allenai.org/winogrande/submissions/get-started](https://leaderboard.allenai.org/winogrande/submissions/get-started)
|
170 |
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- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
171 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
172 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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+
- **Size of downloaded dataset files:** 20.37 MB
|
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- **Size of the generated dataset:** 10.50 MB
|
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+
- **Total amount of disk used:** 30.87 MB
|
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+
|
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### Dataset Summary
|
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+
|
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WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
|
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2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
|
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fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
|
182 |
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commonsense reasoning.
|
183 |
+
|
184 |
+
### Supported Tasks and Leaderboards
|
185 |
+
|
186 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
187 |
+
|
188 |
+
### Languages
|
189 |
+
|
190 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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+
|
192 |
+
## Dataset Structure
|
193 |
+
|
194 |
+
### Data Instances
|
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+
|
196 |
+
#### winogrande_debiased
|
197 |
+
|
198 |
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- **Size of downloaded dataset files:** 3.40 MB
|
199 |
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- **Size of the generated dataset:** 1.59 MB
|
200 |
+
- **Total amount of disk used:** 4.99 MB
|
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+
|
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An example of 'train' looks as follows.
|
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```
|
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+
|
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```
|
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+
|
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#### winogrande_l
|
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|
209 |
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- **Size of downloaded dataset files:** 3.40 MB
|
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- **Size of the generated dataset:** 1.71 MB
|
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- **Total amount of disk used:** 5.11 MB
|
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+
|
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An example of 'validation' looks as follows.
|
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```
|
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+
|
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+
```
|
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+
|
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#### winogrande_m
|
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|
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- **Size of downloaded dataset files:** 3.40 MB
|
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- **Size of the generated dataset:** 0.72 MB
|
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- **Total amount of disk used:** 4.12 MB
|
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+
|
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An example of 'validation' looks as follows.
|
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```
|
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+
|
227 |
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```
|
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+
|
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#### winogrande_s
|
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|
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- **Size of downloaded dataset files:** 3.40 MB
|
232 |
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- **Size of the generated dataset:** 0.47 MB
|
233 |
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- **Total amount of disk used:** 3.87 MB
|
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+
|
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An example of 'validation' looks as follows.
|
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```
|
237 |
+
|
238 |
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```
|
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+
|
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#### winogrande_xl
|
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|
242 |
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- **Size of downloaded dataset files:** 3.40 MB
|
243 |
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- **Size of the generated dataset:** 5.58 MB
|
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- **Total amount of disk used:** 8.98 MB
|
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+
|
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An example of 'train' looks as follows.
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```
|
248 |
+
|
249 |
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```
|
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+
|
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### Data Fields
|
252 |
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|
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The data fields are the same among all splits.
|
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+
|
255 |
+
#### winogrande_debiased
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- `sentence`: a `string` feature.
|
257 |
+
- `option1`: a `string` feature.
|
258 |
+
- `option2`: a `string` feature.
|
259 |
+
- `answer`: a `string` feature.
|
260 |
+
|
261 |
+
#### winogrande_l
|
262 |
+
- `sentence`: a `string` feature.
|
263 |
+
- `option1`: a `string` feature.
|
264 |
+
- `option2`: a `string` feature.
|
265 |
+
- `answer`: a `string` feature.
|
266 |
+
|
267 |
+
#### winogrande_m
|
268 |
+
- `sentence`: a `string` feature.
|
269 |
+
- `option1`: a `string` feature.
|
270 |
+
- `option2`: a `string` feature.
|
271 |
+
- `answer`: a `string` feature.
|
272 |
+
|
273 |
+
#### winogrande_s
|
274 |
+
- `sentence`: a `string` feature.
|
275 |
+
- `option1`: a `string` feature.
|
276 |
+
- `option2`: a `string` feature.
|
277 |
+
- `answer`: a `string` feature.
|
278 |
+
|
279 |
+
#### winogrande_xl
|
280 |
+
- `sentence`: a `string` feature.
|
281 |
+
- `option1`: a `string` feature.
|
282 |
+
- `option2`: a `string` feature.
|
283 |
+
- `answer`: a `string` feature.
|
284 |
+
|
285 |
+
### Data Splits
|
286 |
+
|
287 |
+
| name |train|validation|test|
|
288 |
+
|-------------------|----:|---------:|---:|
|
289 |
+
|winogrande_debiased| 9248| 1267|1767|
|
290 |
+
|winogrande_l |10234| 1267|1767|
|
291 |
+
|winogrande_m | 2558| 1267|1767|
|
292 |
+
|winogrande_s | 640| 1267|1767|
|
293 |
+
|winogrande_xl |40398| 1267|1767|
|
294 |
+
|winogrande_xs | 160| 1267|1767|
|
295 |
+
|
296 |
+
## Dataset Creation
|
297 |
+
|
298 |
+
### Curation Rationale
|
299 |
+
|
300 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
301 |
+
|
302 |
+
### Source Data
|
303 |
+
|
304 |
+
#### Initial Data Collection and Normalization
|
305 |
+
|
306 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
307 |
+
|
308 |
+
#### Who are the source language producers?
|
309 |
+
|
310 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
311 |
+
|
312 |
+
### Annotations
|
313 |
+
|
314 |
+
#### Annotation process
|
315 |
+
|
316 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
317 |
+
|
318 |
+
#### Who are the annotators?
|
319 |
+
|
320 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
321 |
+
|
322 |
+
### Personal and Sensitive Information
|
323 |
+
|
324 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
325 |
+
|
326 |
+
## Considerations for Using the Data
|
327 |
+
|
328 |
+
### Social Impact of Dataset
|
329 |
+
|
330 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
331 |
+
|
332 |
+
### Discussion of Biases
|
333 |
+
|
334 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
335 |
+
|
336 |
+
### Other Known Limitations
|
337 |
+
|
338 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
339 |
+
|
340 |
+
## Additional Information
|
341 |
+
|
342 |
+
### Dataset Curators
|
343 |
+
|
344 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
345 |
+
|
346 |
+
### Licensing Information
|
347 |
+
|
348 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
349 |
+
|
350 |
+
### Citation Information
|
351 |
+
|
352 |
+
```
|
353 |
+
@InProceedings{ai2:winogrande,
|
354 |
+
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
|
355 |
+
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
|
356 |
+
},
|
357 |
+
year={2019}
|
358 |
+
}
|
359 |
+
|
360 |
+
```
|
361 |
+
|
362 |
+
|
363 |
+
### Contributions
|
364 |
+
|
365 |
+
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
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"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
ADDED
@@ -0,0 +1,145 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
1 |
+
"""TODO(winogrande): Add a description here."""
|
2 |
+
|
3 |
+
|
4 |
+
import json
|
5 |
+
import os
|
6 |
+
|
7 |
+
import datasets
|
8 |
+
|
9 |
+
|
10 |
+
# TODO(winogrande): BibTeX citation
|
11 |
+
_CITATION = """\
|
12 |
+
@InProceedings{ai2:winogrande,
|
13 |
+
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
|
14 |
+
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
|
15 |
+
},
|
16 |
+
year={2019}
|
17 |
+
}
|
18 |
+
"""
|
19 |
+
|
20 |
+
# TODO(winogrande):
|
21 |
+
_DESCRIPTION = """\
|
22 |
+
WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
|
23 |
+
2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
|
24 |
+
fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
|
25 |
+
commonsense reasoning.
|
26 |
+
"""
|
27 |
+
|
28 |
+
_URL = "https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip"
|
29 |
+
_FORMATS = ["xs", "s", "m", "l", "xl", "debiased"]
|
30 |
+
|
31 |
+
|
32 |
+
class WinograndeConfig(datasets.BuilderConfig):
|
33 |
+
|
34 |
+
"""BuilderConfig for Discofuse"""
|
35 |
+
|
36 |
+
def __init__(self, data_size, **kwargs):
|
37 |
+
"""
|
38 |
+
|
39 |
+
Args:
|
40 |
+
data_size: the format of the training set we want to use (xs, s, m, l, xl, debiased)
|
41 |
+
**kwargs: keyword arguments forwarded to super.
|
42 |
+
"""
|
43 |
+
super(WinograndeConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
|
44 |
+
self.data_size = data_size
|
45 |
+
|
46 |
+
|
47 |
+
class Winogrande(datasets.GeneratorBasedBuilder):
|
48 |
+
"""TODO(winogrande): Short description of my dataset."""
|
49 |
+
|
50 |
+
# TODO(winogrande): Set up version.
|
51 |
+
VERSION = datasets.Version("1.1.0")
|
52 |
+
BUILDER_CONFIGS = [
|
53 |
+
WinograndeConfig(name="winogrande_" + data_size, description="AI2 dataset", data_size=data_size)
|
54 |
+
for data_size in _FORMATS
|
55 |
+
]
|
56 |
+
|
57 |
+
def _info(self):
|
58 |
+
# TODO(winogrande): Specifies the datasets.DatasetInfo object
|
59 |
+
return datasets.DatasetInfo(
|
60 |
+
# This is the description that will appear on the datasets page.
|
61 |
+
description=_DESCRIPTION,
|
62 |
+
# datasets.features.FeatureConnectors
|
63 |
+
features=datasets.Features(
|
64 |
+
{
|
65 |
+
"sentence": datasets.Value("string"),
|
66 |
+
"option1": datasets.Value("string"),
|
67 |
+
"option2": datasets.Value("string"),
|
68 |
+
"answer": datasets.Value("string")
|
69 |
+
# These are the features of your dataset like images, labels ...
|
70 |
+
}
|
71 |
+
),
|
72 |
+
# If there's a common (input, target) tuple from the features,
|
73 |
+
# specify them here. They'll be used if as_supervised=True in
|
74 |
+
# builder.as_dataset.
|
75 |
+
supervised_keys=None,
|
76 |
+
# Homepage of the dataset for documentation
|
77 |
+
homepage="https://leaderboard.allenai.org/winogrande/submissions/get-started",
|
78 |
+
citation=_CITATION,
|
79 |
+
)
|
80 |
+
|
81 |
+
def _split_generators(self, dl_manager):
|
82 |
+
"""Returns SplitGenerators."""
|
83 |
+
# TODO(winogrande): Downloads the data and defines the splits
|
84 |
+
# dl_manager is a datasets.download.DownloadManager that can be used to
|
85 |
+
# download and extract URLs
|
86 |
+
dl_dir = dl_manager.download_and_extract(_URL)
|
87 |
+
data_dir = os.path.join(dl_dir, "winogrande_1.1")
|
88 |
+
return [
|
89 |
+
datasets.SplitGenerator(
|
90 |
+
name=datasets.Split.TRAIN,
|
91 |
+
# These kwargs will be passed to _generate_examples
|
92 |
+
gen_kwargs={
|
93 |
+
"filepath": os.path.join(data_dir, f"train_{self.config.data_size}.jsonl"),
|
94 |
+
# 'labelpath': os.path.join(data_dir, 'train_{}-labels.lst'.format(self.config.data_size)),
|
95 |
+
"split": "train",
|
96 |
+
},
|
97 |
+
),
|
98 |
+
datasets.SplitGenerator(
|
99 |
+
name=datasets.Split.TEST,
|
100 |
+
# These kwargs will be passed to _generate_examples
|
101 |
+
gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"},
|
102 |
+
),
|
103 |
+
datasets.SplitGenerator(
|
104 |
+
name=datasets.Split.VALIDATION,
|
105 |
+
# These kwargs will be passed to _generate_examples
|
106 |
+
gen_kwargs={
|
107 |
+
"filepath": os.path.join(data_dir, "dev.jsonl"),
|
108 |
+
# 'labelpath': os.path.join(data_dir, 'dev-labels.lst'),
|
109 |
+
"split": "dev",
|
110 |
+
},
|
111 |
+
),
|
112 |
+
]
|
113 |
+
|
114 |
+
def _generate_examples(self, filepath, split):
|
115 |
+
"""Yields examples."""
|
116 |
+
# TODO(winogrande): Yields (key, example) tuples from the dataset
|
117 |
+
with open(filepath, encoding="utf-8") as f:
|
118 |
+
for id_, row in enumerate(f):
|
119 |
+
data = json.loads(row)
|
120 |
+
if split == "test":
|
121 |
+
yield id_, {
|
122 |
+
"sentence": data["sentence"],
|
123 |
+
"option1": data["option1"],
|
124 |
+
"option2": data["option2"],
|
125 |
+
"answer": "",
|
126 |
+
}
|
127 |
+
else:
|
128 |
+
yield id_, {
|
129 |
+
"sentence": data["sentence"],
|
130 |
+
"option1": data["option1"],
|
131 |
+
"option2": data["option2"],
|
132 |
+
"answer": data["answer"],
|
133 |
+
}
|
134 |
+
|
135 |
+
|
136 |
+
# def _generate_test_example(filepath, split, labelpath=None):
|
137 |
+
# with open(filepath, encoding="utf-8") as f:
|
138 |
+
# for id_, row in enumerate(f):
|
139 |
+
# data = json.loads(row)
|
140 |
+
# yield id_,{
|
141 |
+
# 'sentence': data['sentence'],
|
142 |
+
# 'option1': data['option1'],
|
143 |
+
# 'option2': data['option2'],
|
144 |
+
# 'answer': None
|
145 |
+
# }
|