Iker/ClickbaitFighter-7B
Iker/ClickbaitFighter-7B is a 7 billion parameter instruction-tuned causal language model developed by Iker, based on openchat/openchat-3.5-0106. Fine-tuned on the NoticIA dataset, it specializes in Spanish clickbait article summarization, generating single-sentence summaries that reveal the truth behind sensational headlines. This model is optimized for debunking clickbait content in Spanish news articles.
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Overview
Iker/ClickbaitFighter-7B is a 7 billion parameter language model specifically fine-tuned for Spanish clickbait article summarization. Developed by Iker, this model is built upon the openchat/openchat-3.5-0106 base model and leverages the NoticIA Dataset for its specialized training.
Key Capabilities
- Clickbait Debunking: Analyzes sensational Spanish headlines and their corresponding article bodies to extract the core truth.
- Concise Summarization: Generates single-sentence summaries that directly address the misleading nature of clickbait.
- Contextual Citation: Can cite original text, especially direct quotes, to support its summaries.
- Spanish Language Focus: Optimized for processing and generating content exclusively in Spanish.
Performance
This 7B parameter model achieves a ROUGE score of 49.81 on the NoticIA dataset, demonstrating strong performance in its specialized task. It is part of a series of ClickbaitFighter models, offering a balance between size and summarization accuracy.
Good For
- Developers building applications that require automatic debunking of Spanish clickbait.
- Researchers interested in natural language processing for misinformation detection in Spanish.
- Content analysis tools focused on identifying and summarizing the factual content of sensational news.