nahid-hub/BLUGE-bengali-news-classification

BLUGE-NCC: Bangla News Classification

BLUGE-NCC is a meticulously curated and balanced Bangla News Category Classification 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 Description

This task classifies Bangla news articles into seven categories:

Label	Category
0	Business
1	Technology
2	Crime
3	Entertainment
4	International Affairs
5	Sports
6	Lifestyle

Dataset Structure

Balanced 80 / 10 / 10 split:

Split	Samples
Train	49,840
Validation	6,230
Test	6,230
Total	62,300

Fields:

• text — the Bangla news article content
• label — news category (0–6, see table above)

Usage

from datasets import load_dataset

# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-news-classification")

# 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-news-classification", 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-news-classification/train.parquet"
)

License

Released under CC BY 4.0 for the dataset compilation, labels, and splits. You are free to share and adapt this dataset for research and most other purposes, provided you give appropriate credit — see the note above regarding underlying article copyright.

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}
}