Datasets:
Tasks:
Multiple Choice
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
100K - 1M
ArXiv:
License:
Update files from the datasets library (from 1.7.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.7.0
README.md
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# Dataset Card for "race"
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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dataset is collected from English examinations in China, which are designed for middle school and high school students.
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The dataset can be served as the training and test sets for machine comprehension.
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### Supported Tasks
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- `question`: a `string` feature.
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- `options`: a `list` of `string` features.
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### Data Splits
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| name |train|validation|test|
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|------|----:|---------:|---:|
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### Source Data
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Personal and Sensitive Information
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paperswithcode_id: race
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# Dataset Card for "race"
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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dataset is collected from English examinations in China, which are designed for middle school and high school students.
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The dataset can be served as the training and test sets for machine comprehension.
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### Supported Tasks and Leaderboards
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- `question`: a `string` feature.
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- `options`: a `list` of `string` features.
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### Data Splits
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| name |train|validation|test|
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|------|----:|---------:|---:|
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the source language producers?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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#### Annotation process
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the annotators?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Personal and Sensitive Information
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race.py
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"""Yields examples."""
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current_path = Path(train_test_or_eval)
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files_in_dir = [str(f.absolute()) for f in sorted(current_path.glob("**/*.txt"))]
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for file in sorted(files_in_dir):
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with open(file, encoding="utf-8") as f:
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data = json.load(f)
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questions = data["questions"]
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question = questions[i]
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answer = answers[i]
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option = options[i]
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yield i, {
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"example_id": data["id"],
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"article": data["article"],
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"question": question,
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"""Yields examples."""
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current_path = Path(train_test_or_eval)
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files_in_dir = [str(f.absolute()) for f in sorted(current_path.glob("**/*.txt"))]
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for file_idx, file in enumerate(sorted(files_in_dir)):
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with open(file, encoding="utf-8") as f:
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data = json.load(f)
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questions = data["questions"]
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question = questions[i]
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answer = answers[i]
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option = options[i]
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yield f"{file_idx}_{i}", {
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"example_id": data["id"],
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"article": data["article"],
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"question": question,
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