nahid-hub/BLUGE-bengali-wikipedia-section-title-prediction

BLUGE-WSTP: Bangla Wikipedia Section-Title Prediction

BLUGE-WSTP is a meticulously filtered and balanced Bangla Wikipedia Section-Title Prediction dataset, 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 Structure

Balanced 80 / 10 / 10 split:

Split	Samples
Train	39,604
Validation	4,951
Test	4,951
Total	49,506
Fields:

• sectionText — the Bangla Wikipedia section content
• titleA — candidate section title A
• titleB — candidate section title B
• titleC — candidate section title C
• titleD — candidate section title D
• correctTitle — the correct answer, given as one of "titleA", "titleB", "titleC", or "titleD"
• url — source Wikipedia article URL

Usage

from datasets import load_dataset
# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-wikipedia-section-title-prediction")
# 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-wikipedia-section-title-prediction", 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-wikipedia-section-title-prediction/train.parquet"
)

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}
}