zh_core_web_lg Open-source Model - Free Deployment to Achieve NLP Functions such as Chinese Word Segmentation and Part-of-speech Tagging

Zh Core Web Lg

Developed by spacy

CPU-optimized Chinese natural language processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and more

Sequence Labeling ChineseOpen Source License:MIT #Chinese NLP Full Pipeline #OntoNotes High Precision #Dependency Parsing

Downloads 61

Release Time : 3/2/2022

Model Overview

This is a Chinese processing model based on the spaCy framework, primarily used for Chinese text annotation and classification tasks, including named entity recognition, part-of-speech tagging, dependency parsing, etc. The model is optimized for CPU usage and is suitable for various Chinese text processing applications.

Model Features

CPU Optimized

Specifically optimized for CPU usage, suitable for running in environments without GPUs

Comprehensive Chinese Processing

Provides a complete Chinese text processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, and more

High-Quality Vectors

Includes 500,000 unique vectors (300 dimensions), providing strong semantic representation capabilities

Multi-Task Processing

Supports multiple NLP tasks simultaneously, such as named entity recognition, part-of-speech tagging, and dependency parsing

Model Capabilities

Chinese Tokenization

Part-of-Speech Tagging

Named Entity Recognition

Dependency Parsing

Sentence Segmentation

Use Cases

Text Analysis

Chinese Text Preprocessing

Performs tokenization, part-of-speech tagging, and other preprocessing on Chinese text

Accuracy 90.33%

Information Extraction

Extracts named entities such as person names, locations, and organizations from text

F1 Score 71.34%

Language Understanding

Syntactic Analysis

Analyzes dependency relationships in Chinese sentences

Labeled Attachment Score (LAS) 65.71%

🚀 zh_core_web_lg

A Chinese language processing pipeline optimized for CPU, featuring components like tok2vec, tagger, parser, senter, ner, and attribute_ruler.

🚀 Quick Start

For more details, please visit: https://spacy.io/models/zh#zh_core_web_lg

✨ Features

The Chinese pipeline is optimized for CPU and includes components such as tok2vec, tagger, parser, senter, ner, and attribute_ruler.

Property	Details
Name	`zh_core_web_lg`
Version	`3.7.0`
spaCy	`>=3.7.0,<3.8.0`
Default Pipeline	`tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner`
Components	`tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner`
Vectors	500000 keys, 500000 unique vectors (300 dimensions)
Sources	OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) CoreNLP Universal Dependencies Converter (Stanford NLP Group) Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia) (Explosion)
License	`MIT`
Author	Explosion

📚 Documentation

Label Scheme

View label scheme (100 labels for 3 components)

Component	Labels
`tagger`	`AD`, `AS`, `BA`, `CC`, `CD`, `CS`, `DEC`, `DEG`, `DER`, `DEV`, `DT`, `ETC`, `FW`, `IJ`, `INF`, `JJ`, `LB`, `LC`, `M`, `MSP`, `NN`, `NR`, `NT`, `OD`, `ON`, `P`, `PN`, `PU`, `SB`, `SP`, `URL`, `VA`, `VC`, `VE`, `VV`, `X`, `_SP`
`parser`	`ROOT`, `acl`, `advcl:loc`, `advmod`, `advmod:dvp`, `advmod:loc`, `advmod:rcomp`, `amod`, `amod:ordmod`, `appos`, `aux:asp`, `aux:ba`, `aux:modal`, `aux:prtmod`, `auxpass`, `case`, `cc`, `ccomp`, `compound:nn`, `compound:vc`, `conj`, `cop`, `dep`, `det`, `discourse`, `dobj`, `etc`, `mark`, `mark:clf`, `name`, `neg`, `nmod`, `nmod:assmod`, `nmod:poss`, `nmod:prep`, `nmod:range`, `nmod:tmod`, `nmod:topic`, `nsubj`, `nsubj:xsubj`, `nsubjpass`, `nummod`, `parataxis:prnmod`, `punct`, `xcomp`
`ner`	`CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`

Accuracy

Type	Score
`TOKEN_ACC`	95.85
`TOKEN_P`	94.58
`TOKEN_R`	91.36
`TOKEN_F`	92.94
`TAG_ACC`	90.33
`SENTS_P`	78.05
`SENTS_R`	72.63
`SENTS_F`	75.24
`DEP_UAS`	70.86
`DEP_LAS`	65.71
`ENTS_P`	73.55
`ENTS_R`	69.25
`ENTS_F`	71.34

📄 License

This project is licensed under the MIT License.

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