AdarshSingh7647/Std-SFT-Passage-9B
AdarshSingh7647/Std-SFT-Passage-9B is a 9 billion parameter GLM-Z1-9B passage reranker model, developed by AdarshSingh7647, with a context length of 32768 tokens. It is trained using the Std-SFT method, which optimizes the loss on the teacher's reasoning and the final answer. This model is specifically designed for pointwise reranking tasks, determining passage relevance to a query by generating a 'true' or 'false' response based on its internal reasoning.
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Overview
AdarshSingh7647/Std-SFT-Passage-9B is a 9 billion parameter GLM-Z1-9B model specifically fine-tuned for passage reranking. Developed by AdarshSingh7647, this model utilizes the Std-SFT (Standard Supervised Fine-Tuning) training methodology, where the optimization loss is applied to both the teacher's reasoning process and the final answer. It operates as a pointwise reranker, similar to models like Rank1, by generating internal reasoning (<think>) followed by a true or false relevance judgment.
Key Capabilities
- Passage Reranking: Determines the relevance of a given passage to a specific query.
- Reasoning-based Output: Generates an explicit reasoning step before providing a relevance judgment.
- Pointwise Scoring: Assigns a relevance score based on the probability of generating 'true' as the final token.
- GLM-Z1-9B Architecture: Built upon the GLM-Z1-9B base model, offering a robust foundation for reranking tasks.
Good for
- Information Retrieval Systems: Enhancing search result quality by reordering passages based on relevance.
- Question Answering Systems: Filtering and ranking candidate passages to improve answer extraction accuracy.
- Semantic Search Applications: Providing more precise relevance judgments for complex queries.
- Research and Development: Exploring and comparing different reranking strategies, particularly those involving explicit reasoning.