Process text to extract entities and details
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Next-generation reasoning model that runs locally in-browser
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.