Albert Base Chinese Ws
A Traditional Chinese natural language processing model developed by the Academia Sinica CKIP team, based on the ALBERT architecture, supporting tasks such as word segmentation and part-of-speech tagging.
Downloads 1,498
Release Time : 3/2/2022
Model Overview
This model is a foundational natural language processing model for Traditional Chinese, optimized with the ALBERT architecture, primarily used for basic NLP tasks such as Chinese text segmentation and part-of-speech tagging.
Model Features
Traditional Chinese Support
A natural language processing model specifically optimized for Traditional Chinese text.
ALBERT Architecture
Utilizes the lightweight ALBERT architecture, reducing parameter size while maintaining performance.
Multi-task Processing
Supports various Chinese NLP tasks such as word segmentation, part-of-speech tagging, and named entity recognition.
Model Capabilities
Chinese word segmentation
Part-of-speech tagging
Named entity recognition
Use Cases
Text Processing
Chinese Text Preprocessing
Performs word segmentation and part-of-speech tagging on Traditional Chinese text to prepare for subsequent NLP tasks.
Information Extraction
Identifies named entities (e.g., person names, locations, organization names) from Chinese text.
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