Zero-shot Named Entity Recognition

Zero-shot Named Entity Recognition

Identify and extract any custom key entities within the text

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Zero-shot NER model reliably identifies and extracts custom entities from text, leveraging sophisticated generalization techniques. KEY FEATURES: - Unlimited entity type labels - 100K token capacity - Multilingual support - Supports zero-shot and few-shot learning - Provides logical reasoning for classification outputs - 64K model size API access: https://rapidapi.com/knowledgator-knowledgator-default/api/zero-shot-ner/
Adaptive Named Entity Recognition (NER) gallery image
Adaptive Named Entity Recognition (NER) gallery image
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Launch tags:Developer Tools•Artificial Intelligence•Data & Analytics
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Famulor AI
Famulor AI
One agent, all channels: phone, web & WhatsApp AI
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Knowledgator Engineering
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What use cases do you envision for text classification in your industry? To spark some ideas, here are some top case studies we've already tackled: - analyzing social media for trends and key influencers, - conducting market analysis by extracting industry-specific data, - enhancing competitive intelligence through strategic information gathering, - streamlining retail operations by identifying product trends and customer preferences, - improving customer support with quick identification of issues and solutions, - accelerating scientific research by extracting relevant data from publications, clinical trials, and medical records. Test in our AI Playground to experience NER capabilities, no registration is needed. Excited to see how you'll leverage NER in your industry! https://playground.knowledgator....