Zero-Shot NER with GliNER and spaCy

2024-03-20 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

GliNER, a new BERT/Transformer-based named entity recognition (NER) model, offers zero-shot learning capabilities, enabling identification of entities without prior training data. The "gliner-spaCy" library integrates GliNER into spaCy pipelines, allowing users to leverage its power with minimal code. Installation is straightforward via "pip install gliner-spaCy". This integration facilitates the identification of both generic entities like "person" and "organization," and highly domain-specific entities, such as "concentration_camp" for "Auschwitz," by simply defining custom labels. While not perfect, GliNER is positioned as an effective initial tool for cultivating large quantities of training data, particularly for complex, untagged datasets like archival oral testimonies, where it is being tested by their.story for NER, summarization, and categorization.

Key takeaway

For NLP Engineers or researchers needing to extract entities from text without existing training data, "gliner-spaCy" offers a rapid solution. You can quickly define and identify both common and highly specialized entities, significantly accelerating initial data labeling efforts. This approach is particularly valuable for bootstrapping annotation projects on complex, untagged datasets, allowing you to cultivate high-quality training data more efficiently.

Key insights

GliNER enables zero-shot named entity recognition for both generic and domain-specific labels without training data.

Principles

• Zero-shot models generalize to unseen data.
• Custom labels enhance domain specificity.
• Consistency aids data cultivation.

Method

Install "gliner-spaCy" via pip. Import "GlinerSpaCy" and add it as a component to a spaCy pipeline, configuring desired labels in the "config" dictionary. Process text through the pipeline.

In practice

• Identify generic entities (person, organization).
• Extract domain-specific terms (e.g., "concentration_camp").
• Cultivate training data for complex datasets.

Topics

• Zero-Shot Learning
• Named Entity Recognition
• spaCy Pipelines
• GliNER
• BERT Models
• Data Annotation

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.