README.md

---
language:
  - bn
pretty_name: "BLUGE-NCC: Bangla News Classification"
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - topic-classification
tags:
  - bengali
  - bangla
  - bluge
  - ncc
  - news-classification
  - topic-classification
  - bengali-nlp
  - bnlp
  - low-resource
  - text-classification
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train.parquet"
      - split: validation
        path: "validation.parquet"
      - split: test
        path: "test.parquet"
---

# 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** (**B**engali **L**anguage **U**nderstandin**G** **E**valuation), a balanced benchmark for evaluating Bengali natural language understanding. See the full [BLUGE collection](https://huggingface.co/collections/nahid-hub/bluge) for all 7 tasks, and the [B-CORE](https://huggingface.co/datasets/nahid-hub/B-CORE-bengali-corpus) 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

```python
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:

```python
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:

```python
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:

```bibtex
@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}
}
```