Process text to extract entities and details
Extract text from documents
Extract text from PDF and answer questions
Upload and analyze documents for text extraction and Q&A
Extract named entities from medical text
AI powered Document Processing app
Spirit.AI
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Extract text from documents or images
Upload and query documents for information extraction
Search documents for specific information using keywords
Find relevant passages in documents using semantic search
Find relevant text chunks from documents based on queries
Spacy-en Core Web Sm is a specialized AI tool designed to process text and extract entities and details from scanned documents. It is developed by spaCy, a modern NLP library focused on industrial-strength natural language understanding.
• Entity Recognition: Extract named entities such as people, organizations, and locations from text. • Advanced Language Processing: Analyze and understand complex textual data with high accuracy. • Optimized for Web Use: Streamlined for web applications, ensuring efficient and quick processing. • Customizable: Tunable to specific use cases, allowing users to adapt the model for unique requirements.
pip install spacy and python -m spacy download en_core_web_sm.nlp = spacy.load("en_core_web_sm") in your Python code.doc = nlp(text).for ent in doc.ents).What is Spacy-en Core Web Sm used for?
Spacy-en Core Web Sm is primarily used for extracting entities and details from text, making it ideal for applications like information retrieval, document scanning, and data extraction.
Is Spacy-en Core Web Sm free to use?
Yes, Spacy-en Core Web Sm is free to use under the MIT License, making it accessible for both personal and commercial projects.
How does Spacy-en Core Web Sm differ from other spaCy models?
Spacy-en Core Web Sm is optimized for small and medium-sized applications, balancing performance and efficiency. It is less resource-intensive than larger models like en_core_web_md or en_core_web_lg but still provides robust NLP capabilities.