Details: https://spacy.io/models/zh#zh_core_web_trf
Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab_size=21128)). Components: transformer, tagger, parser, ner, attribute_ruler.
Feature | Description |
---|---|
Name | zh_core_web_trf |
Version | 3.7.2 |
spaCy | >=3.7.0,<3.8.0 |
Default Pipeline | transformer , tagger , parser , attribute_ruler , ner |
Components | transformer , tagger , parser , 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) bert-base-chinese (Hugging Face) |
License | MIT |
Author | Explosion |
Label Scheme
View label scheme (99 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 |
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 |
91.75 |
SENTS_P |
70.92 |
SENTS_R |
67.57 |
SENTS_F |
69.21 |
DEP_UAS |
75.72 |
DEP_LAS |
71.45 |
ENTS_P |
76.09 |
ENTS_R |
72.18 |
ENTS_F |
74.08 |
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Evaluation results
- NER Precisionself-reported0.761
- NER Recallself-reported0.722
- NER F Scoreself-reported0.741
- TAG (XPOS) Accuracyself-reported0.918
- Unlabeled Attachment Score (UAS)self-reported0.757
- Labeled Attachment Score (LAS)self-reported0.715
- Sentences F-Scoreself-reported0.692