jina-embeddings-v2-small-en-off-topic

Off-Topic Classification Model

This repository contains a fine-tuned Jina Embeddings model designed to perform binary classification. The model predicts whether a user prompt is off-topic based on the intended purpose defined in the system prompt.

Model Highlights

• Base Model: jina-embeddings-v2-small-en
• Maximum Context Length: 1024 tokens
• Task: Binary classification (on-topic/off-topic)

Performance

We evaluated our fine-tuned models on synthetic data modelling system and user prompt pairs reflecting real world enterprise use cases of LLMs. The dataset is available here.

Further evaluation results on additional synthetic and external datasets (e.g.,JailbreakBench, HarmBench, TrustLLM) are available in our technical report.

Usage

1. Clone this repository and install the required dependencies:

   pip install -r requirements.txt
   

2. You can run the model using two options:

   Option 1: Using inference_onnx.py with the ONNX Model.

    ```
    python inference_onnx.py '[
        ["System prompt example 1", "User prompt example 1"],
        ["System prompt example 2", "System prompt example 2]
    ]'
    ```
   

   Option 2: Using inference_safetensors.py with PyTorch and SafeTensors.

    ```
    python inference_safetensors.py '[
        ["System prompt example 1", "User prompt example 1"],
        ["System prompt example 2", "System prompt example 2]
    ]'
    ```
   

Read more about this model in our technical report.