|
--- |
|
tags: |
|
- spacy |
|
- token-classification |
|
language: |
|
- zh |
|
license: mit |
|
model-index: |
|
- name: zh_core_web_md |
|
results: |
|
- task: |
|
name: NER |
|
type: token-classification |
|
metrics: |
|
- name: NER Precision |
|
type: precision |
|
value: 0.7188227082 |
|
- name: NER Recall |
|
type: recall |
|
value: 0.679010989 |
|
- name: NER F Score |
|
type: f_score |
|
value: 0.6983499096 |
|
- task: |
|
name: TAG |
|
type: token-classification |
|
metrics: |
|
- name: TAG (XPOS) Accuracy |
|
type: accuracy |
|
value: 0.9003849582 |
|
- task: |
|
name: UNLABELED_DEPENDENCIES |
|
type: token-classification |
|
metrics: |
|
- name: Unlabeled Attachment Score (UAS) |
|
type: f_score |
|
value: 0.7049895344 |
|
- task: |
|
name: LABELED_DEPENDENCIES |
|
type: token-classification |
|
metrics: |
|
- name: Labeled Attachment Score (LAS) |
|
type: f_score |
|
value: 0.652241773 |
|
- task: |
|
name: SENTS |
|
type: token-classification |
|
metrics: |
|
- name: Sentences F-Score |
|
type: f_score |
|
value: 0.7572294372 |
|
--- |
|
### Details: https://spacy.io/models/zh#zh_core_web_md |
|
|
|
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler. |
|
|
|
| Feature | Description | |
|
| --- | --- | |
|
| **Name** | `zh_core_web_md` | |
|
| **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, 20000 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` | 90.04 | |
|
| `SENTS_P` | 78.89 | |
|
| `SENTS_R` | 72.80 | |
|
| `SENTS_F` | 75.72 | |
|
| `DEP_UAS` | 70.50 | |
|
| `DEP_LAS` | 65.22 | |
|
| `ENTS_P` | 71.88 | |
|
| `ENTS_R` | 67.90 | |
|
| `ENTS_F` | 69.83 | |
