Extract named entities from text
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Upload and query documents for information extraction
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Search information in uploaded PDFs
Extract text from documents or images
Next-generation reasoning model that runs locally in-browser
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Extract handwritten text from images
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Search... using text for relevant documents
Process text to extract entities and details
Dslim Bert Base NER is a pre-trained BERT-based model designed for Named Entity Recognition (NER) tasks. It is optimized to extract named entities such as names, locations, organizations, and other specific entities from unstructured text. Built on the BERT architecture, this model leverages advanced language understanding to deliver high accuracy in entity extraction.
• Pre-trained BERT model: Natively supports high-performance entity recognition
• State-of-the-art accuracy: Fine-tuned for optimal entity extraction results
• Multi-language support: Works with multiple languages, expanding its applicability
• Efficient processing: Optimized for quick and reliable entity extraction
• Customizable: Can be fine-tuned for domain-specific tasks
What is Named Entity Recognition (NER)?
Named Entity Recognition is a process of identifying and categorizing named entities in unstructured text.
Can I use Dslim Bert Base NER for languages other than English?
Yes, Dslim Bert Base NER supports multiple languages, though performance may vary across languages.
Can I customize this model for my specific use case?
Yes, you can fine-tune Dslim Bert Base NER on your dataset for better performance in domain-specific tasks.