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A Transformer-based English NLP pipeline model providing high-performance named entity recognition, part-of-speech tagging, and dependency parsing.
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Release Time : 3/2/2022
Model Overview
This model is an English Transformer pipeline in the spaCy library, built on the RoBERTa-base architecture, supporting various natural language processing tasks including named entity recognition, part-of-speech tagging, dependency parsing, and sentence segmentation.
Model Features
High-performance Transformer Architecture
Based on the RoBERTa-base Transformer architecture, providing more accurate semantic understanding and context capturing capabilities.
Multi-task Processing
A single model supports multiple NLP tasks including named entity recognition, part-of-speech tagging, and dependency parsing.
High Accuracy
Achieves 90.19% F1-score in named entity recognition tasks and 98.13% accuracy in part-of-speech tagging.
Model Capabilities
Named Entity Recognition
Part-of-Speech Tagging
Dependency Parsing
Sentence Segmentation
Text Token Classification
Use Cases
Text Analysis
Information Extraction
Extract named entities such as person names, locations, and organizations from text
Accurately identifies over 90% of named entities
Syntax Analysis
Analyze the grammatical structure and part-of-speech of sentences
Part-of-speech tagging accuracy exceeds 98%
Content Processing
Document Preprocessing
Prepare text data for machine learning tasks
Provides high-quality tokenized text
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