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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_sm |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.7203462148 |
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- name: NER Recall |
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type: recall |
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value: 0.6493406593 |
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- name: NER F Score |
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type: f_score |
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value: 0.6830029475 |
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- task: |
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name: TAG |
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type: token-classification |
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metrics: |
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- name: TAG (XPOS) Accuracy |
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type: accuracy |
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value: 0.8933253054 |
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- task: |
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name: UNLABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Unlabeled Attachment Score (UAS) |
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type: f_score |
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value: 0.6960047338 |
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- task: |
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name: LABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Labeled Attachment Score (LAS) |
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type: f_score |
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value: 0.640776699 |
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- task: |
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name: SENTS |
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type: token-classification |
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metrics: |
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- name: Sentences F-Score |
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type: f_score |
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value: 0.7514211886 |
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--- |
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### Details: https://spacy.io/models/zh#zh_core_web_sm |
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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_sm` | |
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| **Version** | `3.7.0` | |
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| **spaCy** | `>=3.7.0,<3.8.0` | |
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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** | 0 keys, 0 unique vectors (0 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) | |
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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 (100 labels for 3 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`, `_SP` | |
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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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| **`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` | 95.85 | |
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| `TOKEN_P` | 94.58 | |
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| `TOKEN_R` | 91.36 | |
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| `TOKEN_F` | 92.94 | |
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| `TAG_ACC` | 89.33 | |
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| `SENTS_P` | 77.85 | |
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| `SENTS_R` | 72.62 | |
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| `SENTS_F` | 75.14 | |
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| `DEP_UAS` | 69.60 | |
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| `DEP_LAS` | 64.08 | |
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| `ENTS_P` | 72.03 | |
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| `ENTS_R` | 64.93 | |
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| `ENTS_F` | 68.30 | |
