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Codesota · Tasks · Reading ComprehensionHome/Tasks/Natural Language Processing/Reading Comprehension

Reading Comprehension.

Understanding and answering questions about passages.

1
Datasets
2
Results
accuracy
Canonical metric
§ 02 · Canonical benchmark

The reference dataset.

RACE

Canonical multiple-choice reading comprehension benchmark built from English exams for Chinese middle and high school students. ~28K passages and ~100K questions. Evaluated as accuracy over RACE-M (middle) + RACE-H (high) combined.

Primary metric: accuracy
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§ 03 · Top 10

Leading models.

Leading models on RACE.

#ModelaccuracyYearSource
ALBERT ensemble89.42019paper ↗
2RoBERTa83.22019paper ↗

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§ 04 · All datasets

Tracked datasets.

1 dataset tracked for this task.

RACE
CANONICAL
2 results · accuracy
Top: ALBERT ensemble 89.4
§ 05 · Related tasks

Other tasks in Natural Language Processing.

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