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Create README.md
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README.md
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---
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license: mit
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language:
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- bn
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base_model:
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- sagorsarker/bangla-bert-base
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pipeline_tag: text-classification
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datasets:
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- ciol-research/Dhoroni
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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library_name: transformers
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tags:
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- Environment
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- Climate Change
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- News and Media
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- Bangla
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- Bengali
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---
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## *Dhoroni*: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing
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- **Authors**: Azmine Toushik Wasi, Wahid Faisal, Taj Ahmad, Abdur Rahman, Mst Rafia Islam
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- **Dataset DOI (Zenodo)**: https://doi.org/10.5281/zenodo.13695110
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- **Task in this model**: News Authenticity Identification
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- **Abstract**: Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage. Despite Bangladesh's status as one of the most vulnerable nations to climate impacts, research gaps persist in Bengali-language studies related to climate change and NLP. To address this disparity, we introduce ধরণী (*Dhoroni*), a novel Bengali (Bangla) climate change and environmental news dataset, comprising a 2300 annotated Bangla news articles, offering multiple perspectives such as political influence, scientific/statistical data, authenticity, stance detection, and stakeholder involvement. Furthermore, we present an in-depth exploratory analysis of *Dhoroni* and introduce *BanglaBERT-Dhoroni* family, a novel baseline family for climate stance detection in Bangla, fine-tuned on our dataset. This research contributes significantly to enhancing accessibility and analysis of climate discourse in Bengali (Bangla), addressing crucial communication and research gaps in climate-impacted regions like Bangladesh with 180 million people.
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### Highlights
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- We introduce Dhoroni, a novel benchmark dataset with 2,300 Bengali news articles.
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- The dataset is annotated by three annotators across ten different perspectives.
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- Detailed exploratory analysis and reasoning for each perspective are provided.
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- We present ten baseline models under BanglaBERT-Dhoroni family for different tasks.
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- Benchmarking scores show stable performance in different task
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