| | --- |
| | 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: 0.7355275444 |
| | - name: NER Recall |
| | type: recall |
| | value: 0.6925274725 |
| | - name: NER F Score |
| | type: f_score |
| | value: 0.7133801223 |
| | - task: |
| | name: TAG |
| | type: token-classification |
| | metrics: |
| | - name: TAG (XPOS) Accuracy |
| | type: accuracy |
| | value: 0.9033086963 |
| | - task: |
| | name: UNLABELED_DEPENDENCIES |
| | type: token-classification |
| | metrics: |
| | - name: Unlabeled Attachment Score (UAS) |
| | type: f_score |
| | value: 0.7085620979 |
| | - task: |
| | name: LABELED_DEPENDENCIES |
| | type: token-classification |
| | metrics: |
| | - name: Labeled Attachment Score (LAS) |
| | type: f_score |
| | value: 0.6571012366 |
| | - task: |
| | name: SENTS |
| | type: token-classification |
| | metrics: |
| | - name: Sentences F-Score |
| | type: f_score |
| | value: 0.7524359748 |
| | --- |
| | ### 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** | `3.7.0` | |
| | | **spaCy** | `>=3.7.0,<3.8.0` | |
| | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` | |
| | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` | |
| | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | |
| | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (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)<br />[CoreNLP Universal Dependencies Converter](https://nlp.stanford.edu/software/stanford-dependencies.html) (Stanford NLP Group)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | |
| | | **License** | `MIT` | |
| | | **Author** | [Explosion](https://explosion.ai) | |
| |
|
| | ### Label Scheme |
| |
|
| | <details> |
| |
|
| | <summary>View label scheme (100 labels for 3 components)</summary> |
| |
|
| | | 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` | |
| |
|
| | </details> |
| |
|
| | ### Accuracy |
| |
|
| | | Type | Score | |
| | | --- | --- | |
| | | `TOKEN_ACC` | 95.85 | |
| | | `TOKEN_P` | 94.58 | |
| | | `TOKEN_R` | 91.36 | |
| | | `TOKEN_F` | 92.94 | |
| | | `TAG_ACC` | 90.33 | |
| | | `SENTS_P` | 78.05 | |
| | | `SENTS_R` | 72.63 | |
| | | `SENTS_F` | 75.24 | |
| | | `DEP_UAS` | 70.86 | |
| | | `DEP_LAS` | 65.71 | |
| | | `ENTS_P` | 73.55 | |
| | | `ENTS_R` | 69.25 | |
| | | `ENTS_F` | 71.34 | |
