Datasets:

samanjoy2
/

BnMMLU

pretty_name: BnMMLU
language:
  - bn
tags:
  - bengali
  - benchmark
  - evaluation
  - multiple-choice
  - multilingual
size_categories:
  - 100K<n<1M
configs:
  - config_name: full
    data_files:
      - split: train
        path: data/full/bnmmlu-full.parquet
  - config_name: hard
    data_files:
      - split: train
        path: data/hard/bnmmlu-hard.parquet

BnMMLU

BnMMLU is a Bengali multiple-choice benchmark for measuring multitask language understanding across 41 subjects.

This repository exposes two dataset configs:

full: the complete benchmark with 134,382 questions.
hard: a 15,074-question subset containing the most challenging items, including a failure_ratio field.

The source data in this workspace is stored as CSV. For the Hugging Face dataset repo, the upload script converts the files to Parquet so that options is preserved as a real list instead of a stringified Python list.

Dataset Schema

`full`

Column	Type	Description
`Unique_Serial`	`int64`	Unique sequential identifier.
`subject_name`	`string`	Subject or domain label.
`question`	`string`	Bengali question text.
`correct_answer`	`string`	Correct option key: `a`, `b`, `c`, or `d`.
`options`	`list[string]`	Four answer options in order.
`question_char_count`	`int64`	Character count of the question text.

`hard`

The hard config contains all columns from full plus:

Column	Type	Description
`failure_ratio`	`float64`	Proportion of evaluated models that answered incorrectly.

Loading

from datasets import load_dataset

full = load_dataset("samanjoy2/BnMMLU", "full")
hard = load_dataset("samanjoy2/BnMMLU", "hard")

Citation

@misc{joy2026bnmmlumeasuringmassivemultitask,
  title={BnMMLU: Measuring Massive Multitask Language Understanding in Bengali},
  author={Saman Sarker Joy and Swakkhar Shatabda},
  year={2026},
  eprint={2505.18951},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.18951},
}