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QCRI/AZERG-T4-Mistral
AZERG-T4-Mistral is a machine learning model from QCRI. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 specialized for Task 4: Relationship Type Identification. It has been trained on the QCRI/AZERG-Dataset to classify the specific type of STIX re…
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From the Hugging Face model README
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 specialized for Task 4: Relationship Type Identification. It has been trained on the QCRI/AZERG-Dataset to classify the specific type of STIX relationship (e.g., uses, targets, indicates) between two related entities.
This is a specialist model designed for high performance on entity detection within the AZERG framework.
Use this model to extract potential STIX entities from a given security text passage.
Instruction:
You are a helpful threat intelligence analyst. Your task is to identify the label of the relationship between the source entity and the target entity in the provided text passage. To help you, we provide all the possible relationship labels between the source and target entities. Answer in the following format: <label>Your chosen label</label>
Input:
- Source Entity: [SOURCE ENTITY]
- Target Entity: [TARGET ENTITY]
- Possible Relationship Labels: [STIX RELATIONSHIP LABELS]
- Text Passage: [INPUT TEXT]
Response:
If you use this model, please cite our paper:
@article{lekssays2025azerg,
title={From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction},
author={Lekssays, Ahmed and Sencar, Husrev Taha and Yu, Ting},
journal={arXiv preprint arXiv:2507.16576},
year={2025}
}