metadata
tags:
- spacy
- token-classification
language:
- zh
license: mit
model-index:
- name: zh_core_web_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6807715613
- name: NER Recall
type: recall
value: 0.7407692308
- name: NER F Score
type: f_score
value: 0.7095042627
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9237103231
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.7641386414
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.724427042
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.6377692376
Details: https://spacy.io/models/zh#zh_core_web_trf
Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagger, parser, ner, attribute_ruler.
Feature | Description |
---|---|
Name | zh_core_web_trf |
Version | 3.4.0 |
spaCy | >=3.4.0,<3.5.0 |
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 |
97.88 |
TOKEN_P |
94.58 |
TOKEN_R |
91.36 |
TOKEN_F |
92.94 |
TAG_ACC |
92.37 |
SENTS_P |
68.85 |
SENTS_R |
59.40 |
SENTS_F |
63.78 |
DEP_UAS |
76.41 |
DEP_LAS |
72.44 |
ENTS_P |
68.08 |
ENTS_R |
74.08 |
ENTS_F |
70.95 |