Details: https://spacy.io/models/zh#zh_core_web_sm
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
Feature | Description |
---|---|
Name | zh_core_web_sm |
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 | 0 keys, 0 unique vectors (0 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) |
License | MIT |
Author | Explosion |
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 |
89.33 |
SENTS_P |
77.85 |
SENTS_R |
72.62 |
SENTS_F |
75.14 |
DEP_UAS |
69.60 |
DEP_LAS |
64.08 |
ENTS_P |
72.03 |
ENTS_R |
64.93 |
ENTS_F |
68.30 |
- Downloads last month
- 195
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Space using spacy/zh_core_web_sm 1
Evaluation results
- NER Precisionself-reported0.720
- NER Recallself-reported0.649
- NER F Scoreself-reported0.683
- TAG (XPOS) Accuracyself-reported0.893
- Unlabeled Attachment Score (UAS)self-reported0.696
- Labeled Attachment Score (LAS)self-reported0.641
- Sentences F-Scoreself-reported0.751