%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
En Core Web Sm
A CPU-optimized small English language processing pipeline provided by spaCy, including core functionalities such as tokenization, part-of-speech tagging, dependency parsing, and named entity recognition
Downloads 2,707
Release Time : 3/2/2022
Model Overview
This is an English natural language processing model primarily used for basic NLP tasks such as text tokenization, part-of-speech tagging, dependency parsing, and named entity recognition. The model is optimized for CPU usage, making it suitable for lightweight application scenarios.
Model Features
CPU Optimization
Specially optimized for CPU usage scenarios, suitable for resource-limited environments
Multi-task Processing
Single pipeline simultaneously handles tokenization, part-of-speech tagging, dependency parsing, and named entity recognition
Lightweight
Small model size without word vectors, suitable for quick deployment
High Accuracy
Achieves high accuracy on standard datasets such as OntoNotes 5
Model Capabilities
Text Tokenization
Part-of-speech Tagging
Dependency Parsing
Named Entity Recognition
Sentence Segmentation
Lemmatization
Use Cases
Text Analysis
Information Extraction
Extract entity information such as person names, locations, and organizations from text
F1 score 84.56%
Syntax Analysis
Analyze sentence grammatical structure and word dependency relationships
Dependency Parsing UAS 91.75%
Content Processing
Text Preprocessing
Prepare text data for machine learning models, including tokenization and part-of-speech tagging
Tokenization accuracy 99.86%
Transformers
PyTorchFeatured Recommended AI Models
Š 2025AIbase