SocioCausaNet
Multi-task BERT model for joint causal extraction from text
SocioCausaNet is a fine-tuned BERT-based multi-task model that jointly extracts causal relationships from text. It performs three tasks simultaneously: classifying whether sentences contain causal claims, identifying cause and effect spans via BIO tagging, and linking cause-effect pairs with typed relations. The model handles complex patterns including one-to-many and many-to-many cause-effect structures.
- Hugging Face model
- Hugging Face dataset — annotated causal sentence and cause–effect span dataset
- GitHub repository
- Streamlit web app (PDF causal relation miner)
The model is used in production by the MetaCheck tool on ScienceVerse for evaluating randomization and causal claims in scientific reports. Training data includes expert-annotated sentences and the model supports multiple prediction strategies with adjustable confidence thresholds.