README.md

metadata

license: cc-by-4.0
task_categories:
  - text-generation
  - summarization
  - image-to-text
language:
  - bn
tags:
  - bengali
  - news
  - headline-generation
  - multimodal
  - vision-language
  - nlp
size_categories:
  - 1M<n<10M

BartaLens: Bengali Multimodal News Corpus

Overview

BartaLens (বার্তা + Lens) is a large-scale multimodal Bengali news corpus pairing articles with their associated news images. It is designed for training and evaluating vision-language models on Bengali headline generation.

Key features:

• 1.2M+ articles with associated news images
• Three splits (train / validation / test) for reproducible evaluation
• Multimodal — each article paired with its news photograph
• Diverse sources from major Bangladeshi news outlets

Dataset Statistics

Split	Rows
train	1,219,442
validation	5,000
test	15,000

Loading the Dataset

from datasets import load_dataset

# Load train split
dataset = load_dataset("dipta007/BartaLens", split="train")

# Load test split
test = load_dataset("dipta007/BartaLens", split="test")

# Access an example
example = dataset[0]
image = example["image"]  # PIL Image
headline = example["headline"]
article = example["article"]

Data Fields

Field	Type	Description
id	string	Unique identifier
article	string	Full article body in Bengali
headline	string	Article headline in Bengali
source	string	News outlet name
category	string	News category in Bengali
image	Image	Associated news photograph (PIL Image)

Intended Use

• Multimodal headline generation — generate headlines from article text + image
• Vision-language model fine-tuning (e.g., Gemma, Qwen VLMs with LoRA)
• Image-guided summarization — leverage visual context for better summaries
• Bengali VLM benchmarking — evaluate multimodal understanding in Bengali
• Cross-modal retrieval — match news images to articles

Related Datasets

• Barta — text-only version (1.7M articles, full corpus without image filtering)

Citation

@article{BartaLens2025,
  title={BartaLens: A Multimodal Bengali News Corpus for Headline Generation},
  author={Shubhashis Roy Dipta},
  year={2025}
}