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

Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab_size=21128)). Components: transformer, tagger, parser, ner, attribute_ruler.

| Feature | Description |
| --- | --- |
| **Name** | `zh_core_web_trf` |
| **Version** | `3.7.2` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
| **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 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 />[bert-base-chinese](https://huggingface.co/bert-base-chinese) (Hugging Face) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |

### Label Scheme

<details>

<summary>View label scheme (99 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` |
| **`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` | 91.75 |
| `SENTS_P` | 70.92 |
| `SENTS_R` | 67.57 |
| `SENTS_F` | 69.21 |
| `DEP_UAS` | 75.72 |
| `DEP_LAS` | 71.45 |
| `ENTS_P` | 76.09 |
| `ENTS_R` | 72.18 |
| `ENTS_F` | 74.08 |
{"language": ["zh"], "license": "mit", "tags": ["spacy", "token-classification"]}
spacy token-classification zh license:mit model-index region:us
TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us 

### Details: URL


Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\_size=21128)). Components: transformer, tagger, parser, ner, attribute\_ruler.



### Label Scheme



View label scheme (99 labels for 3 components)


### Accuracy




### Details: URL


Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\_size=21128)). Components: transformer, tagger, parser, ner, attribute\_ruler. ### Label Scheme



View label scheme (99 labels for 3 components) ### Accuracy
TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us 
 ### Details: URL


Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\_size=21128)). Components: transformer, tagger, parser, ner, attribute\_ruler. ### Label Scheme



View label scheme (99 labels for 3 components) ### Accuracy
passage: TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us 
### Details: URL


Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\_size=21128)). Components: transformer, tagger, parser, ner, attribute\_ruler.### Label Scheme



View label scheme (99 labels for 3 components)### Accuracy