spaCy
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About
spaCy is an industrial-strength natural language processing library in Python designed to help build real products and gather real insights. It excels at large-scale information extraction tasks with its carefully memory-managed Cython implementation. spaCy offers support for 75+ languages, 84 trained pipelines, and multi-task learning with pretrained transformers like BERT. Its features include tokenization, named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, and entity linking. spaCy also provides a system for configuring training runs, making it easier to rerun experiments and track changes. The spacy-llm package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various NLP tasks, no training data required.
Platform
Task
Features
• production-ready training system
• linguistically-motivated tokenization
• state-of-the-art speed
• pretrained word vectors
• multi-task learning with pretrained transformers like bert
• 84 trained pipelines for 25 languages
• support for 75+ languages
• named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more components for
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