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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- zh |
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license: MIT |
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model-index: |
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- name: zh_core_web_md |
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results: |
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- tasks: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7220589964 |
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- name: Recall |
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type: recall |
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value: 0.6751648352 |
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- name: F Score |
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type: f_score |
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value: 0.6978249759 |
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- tasks: |
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name: POS |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9004973002 |
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- tasks: |
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name: SENTER |
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type: token-classification |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7859447831 |
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- name: Recall |
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type: recall |
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value: 0.7298152156 |
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- name: F Score |
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type: f_score |
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value: 0.7568407423 |
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- tasks: |
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name: UNLABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7076909586 |
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- tasks: |
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name: LABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7076909586 |
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--- |
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### Details: https://spacy.io/models/zh#zh_core_web_md |
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Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `zh_core_web_md` | |
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| **Version** | `3.1.0` | |
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| **spaCy** | `>=3.1.0,<3.2.0` | |
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| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` | |
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| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` | |
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| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) | |
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| **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) | |
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| **License** | `MIT` | |
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| **Author** | [Explosion](https://explosion.ai) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (101 labels for 4 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`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` | |
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| **`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` | |
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| **`senter`** | `I`, `S` | |
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| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `TOKEN_ACC` | 97.88 | |
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| `TAG_ACC` | 90.05 | |
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| `DEP_UAS` | 70.77 | |
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| `DEP_LAS` | 65.52 | |
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| `ENTS_P` | 72.21 | |
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| `ENTS_R` | 67.52 | |
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| `ENTS_F` | 69.78 | |
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| `SENTS_P` | 78.59 | |
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| `SENTS_R` | 72.98 | |
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| `SENTS_F` | 75.68 | |
