Named Entity Recognition (NER) extracts key data from text, such as names, dates, and organizations. But standard models are typically predefined with a fixed set of types, like PERSON, ORG, DATE, etc.
If you need to extract something more specific, you usually have to train your own model on thousands of labeled examples.
GLiNER solves this with zero-shot entity extraction: you can extract any type without training.
Advantages:
• Works immediately on any text domain, without preparation
• Supports multiple entity types in a single pass
• Returns confidence for each found entity
• Easily integrates into spaCy and other NLP pipelines
Full article, Run this code
In addition, GLiNER is open-source! Install it with command
pip install gliner.••••••••••••••••••••••••••••••••••••••
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