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README.md
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from gliner import GLiNER
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### Load model directly from Hugging Face
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model = GLiNER.from_pretrained("selfconstruct3d/AITSecNER", load_tokenizer=True)
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text = """
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Upon opening Emotet maldocs
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"""
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labels = [
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entities = model.predict_entities(text, labels, threshold=0.5)
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for entity in entities:
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print(entity[
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#### Output:
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#### Emotet => MALWARE
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#### Microsoft => ORG
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# AITSecNER - Entity Recognition for Cybersecurity
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This repository demonstrates how to use the **AITSecNER** model hosted on Hugging Face, based on the powerful GLiNER library, to extract cybersecurity-related entities from text.
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## Installation
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Install GLiNER via pip:
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```bash
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pip install gliner
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```
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## Usage
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### Import and Load Model
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Load the pretrained AITSecNER model directly from Hugging Face:
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```python
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from gliner import GLiNER
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model = GLiNER.from_pretrained("selfconstruct3d/AITSecNER", load_tokenizer=True)
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```
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### Predict Entities
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Define the input text and entity labels you wish to extract:
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```python
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# Example input text
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text = """
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Upon opening Emotet maldocs, victims are greeted with fake Microsoft 365 prompt that states
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“THIS DOCUMENT IS PROTECTED,” and instructs victims on how to enable macros.
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"""
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# Entity labels
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labels = [
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'CLICommand/CodeSnippet', 'CON', 'DATE', 'GROUP', 'LOC',
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'MALWARE', 'ORG', 'SECTOR', 'TACTIC', 'TECHNIQUE', 'TOOL'
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]
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# Predict entities
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entities = model.predict_entities(text, labels, threshold=0.5)
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# Display results
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for entity in entities:
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print(f"{entity['text']} => {entity['label']}")
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```
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### Sample Output
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```bash
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Emotet => MALWARE
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Microsoft => ORG
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```
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## About
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**AITSecNER** leverages GLiNER to quickly and accurately extract cybersecurity-specific entities, making it highly suitable for tasks such as:
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- Cyber threat intelligence analysis
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- Incident response documentation
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- Automated cybersecurity reporting
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