Hugging Face Overview
Model Overview
This model, 0k9d0h1/reranker3b-sft, is a 3.1 billion parameter language model designed with a substantial context length of 32768 tokens. While specific details regarding its architecture, training data, and fine-tuning process are marked as "More Information Needed" in its model card, its name suggests a specialization in reranking tasks.
Key Capabilities
- Reranking: The model's designation as a "reranker" implies its primary function is to re-order a list of items (e.g., search results, document passages) based on their relevance to a given query or context. This is crucial for improving the precision and effectiveness of information retrieval systems.
- Large Context Window: With a 32768 token context length, the model can process and understand long documents or complex queries, which is highly beneficial for nuanced reranking decisions.
Good For
- Improving Search Relevance: Ideal for applications requiring a refined ordering of search results to present the most pertinent information first.
- Document Retrieval Systems: Can be integrated into systems that need to rank retrieved documents or passages for question answering, summarization, or other downstream tasks.
- Information Filtering: Useful in scenarios where a large pool of information needs to be filtered and prioritized based on specific criteria.