En Core Web Md
spaCy's medium-sized English processing pipeline, optimized for CPU, including POS tagging, dependency parsing, named entity recognition, etc.
Downloads 433
Release Time : 3/2/2022
Model Overview
This is a medium-sized English natural language processing model with a complete text processing pipeline, suitable for various NLP tasks. The model is optimized for CPU usage and ideal for production environment deployment.
Model Features
CPU Optimization
Specifically optimized for CPU usage scenarios, suitable for production environment deployment
Complete Processing Pipeline
Includes a complete NLP processing pipeline from tokenization to named entity recognition
High-Quality Word Vectors
Contains 20000 unique word vectors (300-dimensional), trained on multi-source data
Multi-Task Support
Supports various NLP tasks including POS tagging, dependency parsing, named entity recognition, etc.
Model Capabilities
POS Tagging
Dependency Parsing
Named Entity Recognition
Sentence Segmentation
Lemmatization
Text Processing
Use Cases
Text Analysis
News Content Analysis
Extract named entities (people, organizations, locations, etc.) from news text
NER F-score reaches 85.22%
Syntax Analysis
Analyze sentence structure and part-of-speech relationships
Dependency parsing UAS reaches 92.05%
Information Extraction
Document Processing
Extract key information and entities from documents
Supports recognition of 18 entity types
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