ko_core_news_sm Open-source Korean Processing Model - Free Support for Word Segmentation, Part-of-Speech Tagging, etc.

Ko Core News Sm

Developed by spacy

Korean processing pipeline optimized for CPU, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, etc.

Sequence Labeling Korean#Korean NLP #Multi-task Processing #Dependency Parsing

Downloads 62

Release Time : 5/2/2022

Model Overview

This is a Korean natural language processing model that provides basic NLP functions such as tokenization, part-of-speech tagging, dependency parsing, and named entity recognition. The model is optimized for CPU usage and is suitable for processing Korean text data.

Model Features

CPU Optimization

Specially optimized for CPU usage, suitable for running in environments without GPU

Comprehensive NLP Features

Provides a complete NLP processing pipeline from basic tokenization to named entity recognition

High-Accuracy Sentence Segmentation

Sentence segmentation F-score reaches 99.93%, accurately identifying sentence boundaries

Model Capabilities

Tokenization

Part-of-Speech Tagging

Dependency Parsing

Named Entity Recognition

Lemmatization

Sentence Segmentation

Use Cases

Text Processing

Korean Text Analysis

Performing grammatical analysis and structural parsing on Korean news and social media texts

Accurately identifies sentence structures and part-of-speech relationships

Information Extraction

Extracting named entities such as person names, organization names, and locations from Korean texts

NER F-score reaches 71.11%

Linguistic Research

Korean Grammar Research

Analyzing Korean syntactic structures and morphological changes

Provides detailed part-of-speech tagging and dependency relationship analysis

🚀 ko_core_news_sm

The ko_core_news_sm is a Korean language processing pipeline optimized for CPU, offering high - performance token - classification tasks.

📚 Documentation

Details: https://spacy.io/models/ko#ko_core_news_sm

This is a Korean pipeline optimized for CPU. Components include tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.

Property	Details
Name	`ko_core_news_sm`
Version	`3.7.0`
spaCy	`>=3.7.0,<3.8.0`
Default Pipeline	`tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner`
Components	`tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	UD Korean Kaist v2.8 (Choi, Jinho; Han, Na - Rae; Hwang, Jena; Chun, Jayeol) KLUE v1.1.0 (Sungjoon Park, Jihyung Moon, Sungdong Kim, Won Ik Cho, Jiyoon Han, Jangwon Park, Chisung Song, Junseong Kim, Youngsook Song, Taehwan Oh, Joohong Lee, Juhyun Oh, Sungwon Ryu, Younghoon Jeong, Inkwon Lee, Sangwoo Seo, Dongjun Lee, Hyunwoo Kim, Myeonghwa Lee, Seongbo Jang, Seungwon Do, Sunkyoung Kim, Kyungtae Lim, Jongwon Lee, Kyumin Park, Jamin Shin, Seonghyun Kim, Lucy Park, Alice Oh, Jung - Woo Ha, Kyunghyun Cho)
License	`CC BY - SA 4.0`
Author	Explosion

Label Scheme

View label scheme (2028 labels for 4 components)

Component	Labels
`tagger`	`_SP`, `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm`, `f+jco`, `f+jcs`, `f+jct`, `f+jct+jcm`, `f+jp+ef`, `f+jp+ep+ef`, `f+jp+etm`, `f+jxc`, `f+jxt`, `f+ncn`, `f+ncn+jcm`, `f+ncn+jcs`, `f+ncn+jp+ecc`, `f+ncn+jxt`, `f+ncpa+jcm`, `f+npp+jcs`, `f+nq`, `f+xsn`, `f+xsn+jco`, `f+xsn+jxt`, `ii`, `jca`, `jca+jcm`, `jca+jxc`, `jca+jxt`, `jcc`, `jcj`, `jcm`, `jco`, `jcr`, `jcr+jxc`, `jcs`, `jct`, `jct+jcm`, `jct+jxt`, `jp+ecc`, `jp+ecs`, `jp+ef`, `jp+ef+jcr`, `jp+ef+jcr+jxc`, `jp+ep+ecs`, `jp+ep+ef`, `jp+ep+etm`, `jp+ep+etn`, `jp+etm`, `jp+etn`, `jp+etn+jco`, `jp+etn+jxc`, `jxc`, `jxc+jca`, `jxc+jco`, `jxc+jcs`, `jxt`, `mad`, `mad+jxc`, `mad+jxt`, `mag`, `mag+jca`, `mag+jcm`, `mag+jcs`, `mag+jp+ef+jcr`, `mag+jxc`, `mag+jxc+jxc`, `mag+jxt`, `mag+xsn`, `maj`, `maj+jxc`, `maj+jxt`, `mma`, `mmd`, `nbn`, `nbn+jca`, `nbn+jca+jcj`, `nbn+jca+jcm`, `nbn+jca+jp+ef`, `nbn+jca+jxc`, `nbn+jca+jxt`, `nbn+jcc`, `nbn+jcj`, `nbn+jcm`, `nbn+jco`, `nbn+jcr`, `nbn+jcs`, `nbn+jct`, `nbn+jct+jcm`, `nbn+jct+jxt`, `nbn+jp+ecc`, `nbn+jp+ecs`, `nbn+jp+ecs+jca`, `nbn+jp+ecs+jcm`, `nbn+jp+ecs+jco`, `nbn+jp+ecs+jxc`, `nbn+jp+ecs+jxt`, `nbn+jp+ecx`, `nbn+jp+ef`, `nbn+jp+ef+jca`, `nbn+jp+ef+jco`, `nbn+jp+ef+jcr`, `nbn+jp+ef+jcr+jxc`, `nbn+jp+ef+jcr+jxt`, `nbn+jp+ef+jcs`, `nbn+jp+ef+jxc`, `nbn+jp+ef+jxc+jco`, `nbn+jp+ef+jxf`, `nbn+jp+ef+jxt`, `nbn+jp+ep+ecc`, `nbn+jp+ep+ecs`, `nbn+jp+ep+ecs+jxc`, `nbn+jp+ep+ef`, `nbn+jp+ep+ef+jcr`, `nbn+jp+ep+etm`, `nbn+jp+ep+etn`, `nbn+jp+ep+etn+jco`, `nbn+jp+ep+etn+jcs`, `nbn+jp+etm`, `nbn+jp+etn`, `nbn+jp+etn+jca`, `nbn+jp+etn+jca+jxt`, `nbn+jp+etn+jco`, `nbn+jp+etn+jcs`, `nbn+jp+etn+jxc`, `nbn+jp+etn+jxt`, `nbn+jxc`, `nbn+jxc+jca`, `nbn+jxc+jca+jxc`, `nbn+jxc+jca+jxt`, `nbn+jxc+jcc`, `nbn+jxc+jcm`, `nbn+jxc+jco`, `nbn+jxc+jcs`, `nbn+jxc+jp+ef`, `nbn+jxc+jxc`, `nbn+jxc+jxt`, `nbn+jxt`, `nbn+nbn`, `nbn+nbn+jp+ef`, `nbn+xsm+ecs`, `nbn+xsm+ef`, `nbn+xsm+ep+ef`, `nbn+xsm+ep+ef+jcr`, `nbn+xsm+etm`, `nbn+xsn`, `nbn+xsn+jca`, `nbn+xsn+jca+jp+ef+jcr`, `nbn+xsn+jca+jxc`, `nbn+xsn+jca+jxt`, `nbn+xsn+jcm`, `nbn+xsn+jco`, `nbn+xsn+jcs`, `nbn+xsn+jct`, `nbn+xsn+jp+ecc`, `nbn+xsn+jp+ecs`, `nbn+xsn+jp+ef`, `nbn+xsn+jp+ef+jcr`, `nbn+xsn+jp+ep+ef`, `nbn+xsn+jxc`, `nbn+xsn+jxt`, `nbn+xsv+etm`, `nbu`, `nbu+jca`, `nbu+jca+jxc`, `nbu+jca+jxt`, `nbu+jcc`, `nbu+jcc+jxc`, `nbu+jcj`, `nbu+jcm`, `nbu+jco`, `nbu+jcs`, `nbu+jct`, `nbu+jct+jxc`, `nbu+jp+ecc`, `nbu+jp+ecs`, `nbu+jp+ef`, `nbu+jp+ef+jcr`, `nbu+jp+ef+jxc`, `nbu+jp+ep+ecc`, `nbu+jp+ep+ecs`, `nbu+jp+ep+ef`, `nbu+jp+ep+ef+jcr`, `nbu+jp+ep+etm`, `nbu+jp+ep+etn+jco`, `nbu+jp+etm`, `nbu+jxc`, `nbu+jxc+jca`, `nbu+jxc+jcs`, `nbu+jxc+jp+ef`, `nbu+jxc+jp+ep+ef`, `nbu+jxc+jxt`, `nbu+jxt`, `nbu+ncn`, `nbu+ncn+jca`, `nbu+ncn+jcm`, `nbu+xsn`, `nbu+xsn+jca`, `nbu+xsn+jca+jxc`, `nbu+xsn+jca+jxt`, `nbu+xsn+jcm`, `nbu+xsn+jco`, `nbu+xsn+jcs`, `nbu+xsn+jp+ecs`, `nbu+xsn+jp+ep+ef`, `nbu+xsn+jxc`, `nbu+xsn+jxc+jxt`, `nbu+xsn+jxt`, `nbu+xsv+ecc`, `nbu+xsv+etm`, `ncn`, `ncn+f+ncpa+jco`, `ncn+jca`, `ncn+jca+jca`, `ncn+jca+jcc`, `ncn+jca+jcj`, `ncn+jca+jcm`, `ncn+jca+jcs`, `ncn+jca+jct`, `ncn+jca+jp+ecc`, `ncn+jca+jp+ecs`, `ncn+jca+jp+ef`, `ncn+jca+jp+ep+ef`, `ncn+jca+jp+etm`, `ncn+jca+jp+etn+jxt`, `ncn+jca+jxc`, `ncn+jca+jxc+jcc`, `ncn+jca+jxc+jcm`, `ncn+jca+jxc+jxc`, `ncn+jca+jxc+jxt`, `ncn+jca+jxt`, `ncn+jcc`, `ncn+jcc+jxc`, `ncn+jcj`, `ncn+jcj+jxt`, `ncn+jcm`, `ncn+jco`, `ncn+jcr`, `ncn+jcr+jxc`, `ncn+jcs`, `ncn+jcs+jxt`, `ncn+jct`, `ncn+jct+jcm`, `ncn+jct+jxc`, `ncn+jct+jxt`, `ncn+jcv`, `ncn+jp+ecc`, `ncn+jp+ecc+jct`, `ncn+jp+ecc+jxc`, `ncn+jp+ecs`, `ncn+jp+ecs+jcm`, `ncn+jp+ecs+jco`, `ncn+jp+ecs+jxc`, `ncn+jp+ecs+jxt`, `ncn+jp+ecx`, `ncn+jp+ef`, `ncn+jp+ef+jca`, `ncn+jp+ef+jcm`, `ncn+jp+ef+jco`, `ncn+jp+ef+jcr`, `ncn+jp+ef+jcr+jxc`, `ncn+jp+ef+jcr+jxt`, `ncn+jp+ef+jp+etm`, `ncn+jp+ef+jxc`, `ncn+jp+ef+jxf`, `ncn+jp+ef+jxt`, `ncn+jp+ep+ecc`, `ncn+jp+ep+ecs`, `ncn+jp+ep+ecs+jxc`, `ncn+jp+ep+ecx`, `ncn+jp+ep+ef`, `ncn+jp+ep+ef+jcr`, `ncn+jp+ep+ef+jcr+jxc`, `ncn+jp+ep+ef+jxc`, `ncn+jp+ep+ef+jxf`, `ncn+jp+ep+ef+jxt`, `ncn+jp+ep+ep+etm`, `ncn+jp+ep+etm`, `ncn+jp+ep+etn`, `ncn+jp+ep+etn+jca`, `ncn+jp+ep+etn+jca+jxc`, `ncn+jp+ep+etn+jco`, `ncn+jp+ep+etn+jcs`, `ncn+jp+ep+etn+jxt`, `ncn+jp+etm`, `ncn+jp+etn`, `ncn+jp+etn+jca`, `ncn+jp+etn+jca+jxc`, `ncn+jp+etn+jca+jxt`, `ncn+jp+etn+jco`, `ncn+jp+etn+jcs`, `ncn+jp+etn+jct`, `ncn+jp+etn+jxc`, `ncn+jp+etn+jxt`, `ncn+jxc`, `ncn+jxc+jca`, `ncn+jxc+jca+jxc`, `ncn+jxc+jca+jxt`, `ncn+jxc+jcc`, `ncn+jxc+jcm`, `ncn+jxc+jco`, `ncn+jxc+jcs`, `ncn+jxc+jct+jxt`, `ncn+jxc+jp+ef`, `ncn+jxc+jp+ef+jcr`, `ncn+jxc+jp+ep+ecs`, `ncn+jxc+jp+ep+ef`, `ncn+jxc+jp+etm`, `ncn+jxc+jxc`, `ncn+jxc+jxt`, `ncn+jxt`, `ncn+jxt+jcm`, `ncn+jxt+jxc`, `ncn+nbn`, `ncn+nbn+jca`, `ncn+nbn+jcm`, `ncn+nbn+jcs`, `ncn+nbn+jp+ecc`, `ncn+nbn+jp+ep+ef`, `ncn+nbn+jxc`, `ncn+nbn+jxt`, `ncn+nbu`, `ncn+nbu+jca`, `ncn+nbu+jcm`, `ncn+nbu+jco`, `ncn+nbu+jp+ef`, `ncn+nbu+jxc`, `ncn+nbu+ncn`, `ncn+ncn`, `ncn+ncn+jca`, `ncn+ncn+jca+jcc`, `ncn+ncn+jca+jcm`, `ncn+ncn+jca+jxc`, `ncn+ncn+jca+jxc+jcm`, `ncn+ncn+jca+jxc+jxc`, `ncn+ncn+jca+jxt`, `ncn+ncn+jcc`, `ncn+ncn+jcj`, `ncn+ncn+jcm`, `ncn+ncn+jco`, `ncn+ncn+jcr`, `ncn+ncn+jcs`, `ncn+ncn+jct`, `ncn+ncn+jct+jcm`, `ncn+ncn+jct+jxc`, `ncn+ncn+jct+jxt`, `ncn+ncn+jp+ecc`, `ncn+ncn+jp+ecs`, `ncn+ncn+jp+ef`, `ncn+ncn+jp+ef+jcm`, `ncn+ncn+jp+ef+jcr`, `ncn+ncn+jp+ef+jcs`, `ncn+ncn+jp+ep+ecc`, `ncn+ncn+jp+ep+ecs`, `ncn+ncn+jp+ep+ef`, `ncn+ncn+jp+ep+ef+jcr`, `ncn+ncn+jp+ep+ep+etm`, `ncn+ncn+jp+ep+etm`, `ncn+ncn+jp+ep+etn`, `ncn+ncn+jp+etm`, `ncn+ncn+jp+etn`, `ncn+ncn+jp+etn+jca`, `ncn+ncn+jp+etn+jco`, `ncn+ncn+jp+etn+jxc`, `ncn+ncn+jxc`, `ncn+ncn+jxc+jca`, `ncn+ncn+jxc+jcc`, `ncn+ncn+jxc+jcm`, `ncn+ncn+jxc+jco`, `ncn+ncn+jxc+jcs`, `ncn+ncn+jxc+jxc`, `ncn+ncn+jxt`, `ncn+ncn+nbn`, `ncn+ncn+ncn`, `ncn+ncn+ncn+jca`, `ncn+ncn+ncn+jca+jcm`, `ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+jcj`, `ncn+ncn+ncn+jcm`, `ncn+ncn+ncn+jco`, `ncn+ncn+ncn+jcs`, `ncn+ncn+ncn+jct+jxt`, `ncn+ncn+ncn+jp+etn+jxc`, `ncn+ncn+ncn+jxt`, `ncn+ncn+ncn+ncn+jca`, `ncn+ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+ncn+jco`, `ncn+ncn+ncn+xsn+jp+etm`, `ncn+ncn+ncpa`, `ncn+ncn+ncpa+jca`, `ncn+ncn+ncpa+jcm`, `ncn+ncn+ncpa+jco`, `ncn+ncn+ncpa+jcs`, `ncn+ncn+ncpa+jxc`, `ncn+ncn+ncpa+jxt`, `ncn+ncn+ncpa+ncn`, `ncn+ncn+ncpa+ncn+jca`, `ncn+ncn+ncpa+ncn+jcj`, `ncn+ncn+ncpa+ncn+jcm`, `ncn+ncn+ncpa+ncn+jxt`, `ncn+ncn+xsn`, `ncn+ncn+xsn+jca`, `ncn+ncn+xsn+jca+jxt`, `ncn+ncn+xsn+jcj`, `ncn+ncn+xsn+jcm`, `ncn+ncn+xsn+jco`, `ncn+ncn+xsn+jcs`, `ncn+ncn+xsn+jct`, `ncn+ncn+xsn+jp+ecs`, `ncn+ncn+xsn+jp+ep+ef`, `ncn+ncn+xsn+jp+etm`, `ncn+ncn+xsn+jxc`, `ncn+ncn+xsn+jxc+jcs`, `ncn+ncn+xsn+jxt`, `ncn+ncn+xsv+ecc`, `ncn+ncn+xsv+etm`, `ncn+ncpa`, `ncn+ncpa+jca`, `ncn+ncpa+jca+jcm`, `ncn+ncpa+jca+jxc`, `ncn+ncpa+jca+jxt`, `ncn+ncpa+jcc`, `ncn+ncpa+jcj`, `ncn+ncpa+jcm`, `ncn+ncpa+jco`, `ncn+ncpa+jcr`, `ncn+ncpa+jcs`, `ncn+ncpa+jct`, `ncn+ncpa+jct+jcm`, `ncn+ncpa+jct+jxt`, `ncn+ncpa+jp+ecc`, `ncn+ncpa+jp+ecc+jxc`, `ncn+ncpa+jp+ecs`, `ncn+ncpa+jp+ecs+jxc`, `ncn+ncpa+jp+ef`, `ncn+ncpa+jp+ef+jcr`, `ncn+ncpa+jp+ef+jcr+jxc`, `ncn+ncpa+jp+ep+ef`, `ncn+ncpa+jp+ep+etm`, `ncn+ncpa+jp+ep+etn`, `ncn+ncpa+jp+etm`, `ncn+ncpa+jxc`, `ncn+ncpa+jxc+jca+jxc`, `ncn+ncpa+jxc+jco`, `ncn+ncpa+jxc+jcs`, `ncn+ncpa+jxt`, `ncn+ncpa+nbn+jcs`, `ncn+ncpa+ncn`, `ncn+ncpa+ncn+jca`, `ncn+ncpa+ncn+jca+jcm`, `ncn+ncpa+ncn+jca+jxc`, `ncn+ncpa+ncn+jca+jxt`, `ncn+ncpa+ncn+jcj`, `ncn+ncpa+ncn+jcm`, `ncn+ncpa+ncn+jco`, `ncn+ncpa+ncn+jcs`, `ncn+ncpa+ncn+jct`, `ncn+ncpa+ncn+jct+jcm`, `ncn+ncpa+ncn+jp+ef+jcr`, `ncn+ncpa+ncn+jp+ep+etm`, `ncn+ncpa+ncn+jxc`, `ncn+ncpa+ncn+jxt`, `ncn+ncpa+ncn+xsn+jcm`, `ncn+ncpa+ncn+xsn+jxt`, `ncn+ncpa+ncpa`, `ncn+ncpa+ncpa+jca`, `ncn+ncpa+ncpa+jcj`, `ncn+ncpa+ncpa+jcm`, `ncn+ncpa+ncpa+jco`, `ncn+ncpa+ncpa+jcs`, `ncn+ncpa+ncpa+jp+ep+ef`, `ncn+ncpa+ncpa+jxt`, `ncn+ncpa+ncpa+ncn`, `ncn+ncpa+xsn`, `ncn+ncpa+xsn+jcm`, `ncn+ncpa+xsn+jco`, `ncn+ncpa+xsn+jcs`, `ncn+ncpa+xsn+jp+ecc`, `ncn+ncpa+xsn+jp+etm`, `ncn+ncpa+xsn+jxt`, `ncn+ncpa+xsv+ecc`, `ncn+ncpa+xsv+ecs`, `ncn+ncpa+xsv+ecx`, `ncn+ncpa+xsv+ecx+px+etm`, `ncn+ncpa+xsv+ef`, `ncn+ncpa+xsv+ef+jcm`, `ncn+ncpa+xsv+ef+jcr`, `ncn+ncpa+xsv+etm`, (truncated: full list in pipeline meta)
`morphologizer`	`POS=CCONJ`, `POS=ADV`, `POS=SCONJ`, `POS=DET`, `POS=NOUN`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=SPACE`, `POS=AUX`, `POS=PRON`, `POS=PROPN`, `POS=NUM`, `POS=INTJ`, `POS=PART`, `POS=X`, `POS=ADP`, `POS=SYM`
`parser`	`ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct`, `xcomp`
`ner`	`DT`, `LC`, `OG`, `PS`, `QT`, `TI`

Accuracy

Type	Score
`TOKEN_ACC`	100.00
`TOKEN_P`	100.00
`TOKEN_R`	100.00
`TOKEN_F`	100.00
`TAG_ACC`	73.06
`POS_ACC`	85.82
`SENTS_P`	99.90
`SENTS_R`	99.95
`SENTS_F`	99.93
`DEP_UAS`	73.61
`DEP_LAS`	65.59
`LEMMA_ACC`	83.57
`ENTS_P`	77.04
`ENTS_R`	66.03
`ENTS_F`	71.11

🔧 Technical Details

Model Index

Name: ko_core_news_sm
Results:
- Task: NER (Token - Classification)
  - Metrics:
    - NER Precision: 0.7704418068
    - NER Recall: 0.6603320381
    - NER F Score: 0.7111499981
- Task: TAG (Token - Classification)
  - Metrics:
    - TAG (XPOS) Accuracy: 0.7305919816
- Task: POS (Token - Classification)
  - Metrics:
    - POS (UPOS) Accuracy: 0.8582222398
- Task: LEMMA (Token - Classification)
  - Metrics:
    - Lemma Accuracy: 0.8356969086
- Task: UNLABELED_DEPENDENCIES (Token - Classification)
  - Metrics:
    - Unlabeled Attachment Score (UAS): 0.7360798556
- Task: LABELED_DEPENDENCIES (Token - Classification)
  - Metrics:
    - Labeled Attachment Score (LAS): 0.6558677391
- Task: SENTS (Token - Classification)
  - Metrics:
    - Sentences F - Score: 0.999274135

📄 License

The model is released under the CC BY - SA 4.0 license.

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