Albert Tiny Chinese Pos
A lightweight ALBERT model developed by Academia Sinica's CKIP team, supporting Traditional Chinese NLP tasks
Downloads 2,781
Release Time : 3/2/2022
Model Overview
This model is a lightweight Chinese model based on ALBERT architecture, primarily used for NLP tasks like Chinese word segmentation, part-of-speech tagging, and named entity recognition.
Model Features
Lightweight Design
Adopts ALBERT's lightweight architecture, suitable for resource-constrained environments
Traditional Chinese Support
Specially optimized for Traditional Chinese text processing
Multifunctional NLP Tool
Provides various NLP functions including 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 Analysis
Performing word segmentation and part-of-speech tagging on Traditional Chinese texts
Accurately identifies word boundaries and parts of speech
Named Entity Recognition
Identifying entities like person names and locations from Chinese texts
Can recognize common Chinese entities
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