StoryEmb: Narrative-Focused Story Embeddings

StoryEmb is a 7 billion parameter embedding model, derived from the intfloat/e5-mistral-7b-instruct architecture, and specifically trained to create narrative-focused representations of fictional stories. This model is the result of research by Hatzel and Biemann, detailed in their 2024 paper "Story Embeddings — Narrative-Focused Representations of Fictional Stories" presented at ACL.

Key Capabilities

• Narrative-Focused Embeddings: Generates embeddings that capture the narrative structure and content of fictional stories.
• Augmented Data Training: The published variant is trained on augmented data for enhanced performance.
• Compatibility: Can be used similarly to the base intfloat/e5-mistral-7b-instruct model.

Primary Use Case

• Story Retrieval: Optimized for retrieving stories with similar narrative structures. Users should apply the task prefix "Retrieve stories with a similar narrative to the given story: " for optimal results.

Additional Information

• Adapter weights are available in the adapter-weights directory for those interested in continued fine-tuning.
• The model's development is thoroughly documented in the associated research paper.