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Nl Core News Md

Developed by spacy
CPU-optimized Dutch processing pipeline including tokenization, POS tagging, dependency parsing, named entity recognition, etc.
Downloads 15
Release Time : 3/2/2022

Model Overview

This is a medium-sized Dutch language processing model developed based on the spaCy framework, suitable for various Dutch text processing tasks. The model includes a complete natural language processing pipeline capable of performing tasks such as tokenization, POS tagging, dependency parsing, and named entity recognition.

Model Features

CPU Optimization
Specially optimized for CPU usage, suitable for deployment in environments without GPUs.
Comprehensive NLP Pipeline
Includes a complete set of natural language processing components, from tokenization to named entity recognition.
Pretrained Vectors
Includes 20,000 300-dimensional pretrained word vectors to enhance semantic understanding.

Model Capabilities

Dutch Tokenization
POS Tagging
Dependency Parsing
Named Entity Recognition
Lemmatization
Sentence Segmentation
Morphological Analysis

Use Cases

Text Analysis
Dutch Text Preprocessing
Provides preprocessing functions for Dutch text analysis tasks.
Accuracy up to 96.39% (UPOS POS Tagging)
Information Extraction
Extracts named entities (people, places, organizations, etc.) from Dutch text.
F1-score 75.28%
Linguistic Research
Dutch Grammar Analysis
Analyzes the grammatical structure and dependencies of Dutch sentences.
Dependency Parsing UAS 86.82%, LAS 82.13%
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