nahid-hub/BLUGE-bengali-linguistic-acceptability-paired

BLUGE-BCLAP: Bangla Linguistic Acceptability (Paired)

BLUGE-BCLAP is a meticulously constructed Bangla Corpus of Linguistic Acceptability (Paired), one of the 7 tasks in BLUGE (Bengali Language UnderstandinG Evaluation), a balanced benchmark for evaluating Bengali natural language understanding. See the full BLUGE collection for all 7 tasks, and the B-CORE pretraining corpus and BnLM model suite released alongside it.

Dataset Description

This binary classification task evaluates a model's understanding of grammatical correctness in Bangla by assessing the grammatical acceptability of sentences. It is built from the BanglaGEC corpus, which spans 10 types of Bengali grammatical errors — including verb inflection, speech, homonyms, misplaced punctuation, subject change, missing subjects, and three types of verb deletion (list, VAUX, and VM).

Dataset Structure

Balanced 80 / 10 / 10 split:

Fields:

• —sentence — the Bangla sentence (ungrammatical or corrected)
• —label — acceptability (`0`: unacceptable, `1`: acceptable)
• —ErrorType — the grammatical error category (e.g. verb inflection, misplaced punctuation, missing subject)

Usage

from datasets import load_dataset

# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-linguistic-acceptability-paired")

# Access a specific split
train_ds = ds["train"]
val_ds   = ds["validation"]
test_ds  = ds["test"]

print(train_ds[0])

Load a single split directly:

from datasets import load_dataset

test_ds = load_dataset("nahid-hub/BLUGE-bengali-linguistic-acceptability-paired", split="test")

Or read the Parquet files directly with pandas:

import pandas as pd

train_df = pd.read_parquet(
    "hf://datasets/nahid-hub/BLUGE-bengali-linguistic-acceptability-paired/train.parquet"
)

Related Dataset

BCLAP is complemented by **BLUGE-BCLAU**, which offers the same acceptability judgment task in a non-paired format using mutually exclusive samples.

License

Released under CC BY 4.0.

Citation

If you use this dataset, please cite:

@ARTICLE{BnLM-BLUGE-B-CORE,
  author={Hossain, Nahid and Faisal Kabir, Md.},
  journal={IEEE Access}, 
  title={Efficient Monolingual Pretraining in Low-Resource Settings Through Morphology-Aware Tokenization, Principled Corpus Denoising, and Benchmark-Driven Evaluation}, 
  year={2026},
  volume={14},
  number={},
  pages={91979-92003},
  keywords={Modeling;Multilingual;Training;Cleaning;Vocabulary;Labeling;Tokenization;Computational linguistics;Pipelines;Conferences;B-CORE;BLUGE;BnLM;corpus;evaluation benchmark;pretrained models},
  doi={10.1109/ACCESS.2026.3701520}
}