Instructions to use KoichiYasuoka/chinese-bert-wwm-ext-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/chinese-bert-wwm-ext-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/chinese-bert-wwm-ext-upos", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
chinese-bert-wwm-ext-upos
Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-bert-wwm-ext. Every word is tagged by UPOS (Universal Part-Of-Speech).
How to Use
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos")
or
import esupar
nlp=esupar.load("KoichiYasuoka/chinese-bert-wwm-ext-upos")
See Also
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
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Model tree for KoichiYasuoka/chinese-bert-wwm-ext-upos
Base model
hfl/chinese-bert-wwm-ext