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Developed by spacy
CPU-optimized German processing pipeline including tokenization, part-of-speech tagging, morphological analysis, dependency parsing, lemmatization, named entity recognition, etc.
Downloads 22
Release Time : 3/2/2022

Model Overview

Medium-sized German processing model provided by spaCy, trained on TIGER corpus and WikiNER data, suitable for general German text processing tasks

Model Features

CPU Optimization
Specifically optimized for CPU usage scenarios, suitable for resource-constrained environments
Comprehensive Processing Pipeline
Includes complete NLP processing components from tokenization to named entity recognition
High-Quality Word Vectors
Contains 500,000 keys and 20,000 unique vectors (300 dimensions), trained on fastText

Model Capabilities

Tokenization
Part-of-speech tagging
Morphological analysis
Dependency parsing
Lemmatization
Named entity recognition
Sentence segmentation

Use Cases

Text Processing
German Text Analysis
Performs grammatical and structural analysis of German texts
Accurately identifies parts of speech, dependency relations, and named entities
Information Extraction
Extracts structured information from German texts
Identifies entities such as person names, locations, and organizations in text
Language Learning
German Grammar Analysis
Helps learners understand German sentence structures
Provides detailed part-of-speech tagging and dependency relation analysis
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