jina-embeddings-v2-small-en-off-topic
| Property | Value |
|---|---|
| License | govtech-singapore |
| Research Paper | Technical Report |
| Base Model | jinaai/jina-embeddings-v2-small-en |
| Maximum Context Length | 1024 tokens |
What is jina-embeddings-v2-small-en-off-topic?
This is a specialized fine-tuned model based on Jina Embeddings, designed for binary classification of user prompts. It determines whether inputs are on-topic or off-topic relative to a given system prompt, achieving remarkable performance metrics with a 0.99 ROC-AUC score.
Implementation Details
The model implements a bi-encoder classification architecture, fine-tuned on synthetic data representing real-world enterprise LLM use cases. It demonstrates exceptional performance metrics, including 0.97 F1 score, 0.99 precision, and 0.95 recall.
- Built on the jina-embeddings-v2-small-en architecture
- Supports both ONNX and SafeTensors implementations
- Extensively evaluated on synthetic and external datasets
Core Capabilities
- Binary classification of prompt relevance
- High-accuracy off-topic detection
- Enterprise-ready implementation
- Flexible deployment options via ONNX or PyTorch
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional performance in off-topic detection, achieving near-perfect metrics across ROC-AUC, precision, and F1 score. It's specifically designed for enterprise use cases and offers flexible deployment options.
Q: What are the recommended use cases?
This model is ideal for enterprise applications requiring robust content relevance checking, prompt filtering, and maintaining conversation coherence in AI systems. It's particularly useful for ensuring user inputs align with intended system purposes.
