You can deploy this model and start earning money today!

Ella01/
zh_core_web_trf

Views

No views yet

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

Ella01/zh_core_web_trf

Label Scheme

Accuracy

Ella01/
zh_core_web_trf