README.md · spacy/zh_core_web_lg at 68251edd0c5fbdfbd50f9f48c14d5ea4d9655e0b

metadata

tags:
  - spacy
  - token-classification
language:
  - zh
license: mit
model-index:
  - name: zh_core_web_lg
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.7335270192
          - name: NER Recall
            type: recall
            value: 0.6936263736
          - name: NER F Score
            type: f_score
            value: 0.7130189212
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9027089042
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.7072780475
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.6564620448
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.7530260108

Details: https://spacy.io/models/zh#zh_core_web_lg

Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.

Feature	Description
Name	`zh_core_web_lg`
Version	`3.4.0`
spaCy	`>=3.4.0,<3.5.0`
Default Pipeline	`tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner`
Components	`tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner`
Vectors	500000 keys, 500000 unique vectors (300 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) Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia) (Explosion)
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`	97.88
`TOKEN_P`	94.58
`TOKEN_R`	91.36
`TOKEN_F`	92.94
`TAG_ACC`	90.27
`SENTS_P`	77.74
`SENTS_R`	73.01
`SENTS_F`	75.30
`DEP_UAS`	70.73
`DEP_LAS`	65.65
`ENTS_P`	73.35
`ENTS_R`	69.36
`ENTS_F`	71.30