Instructions to use KoichiYasuoka/bert-base-vietnamese-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/bert-base-vietnamese-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/bert-base-vietnamese-upos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-base-vietnamese-upos
Model Description
This is a BERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing, derived from vibert-base-cased. Every word is tagged by UPOS(Universal Part-Of-Speech).
How to Use
from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos")
pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple")
nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]
print(nlp("Hai cái đầu thì tốt hơn một."))
or
import esupar
nlp=esupar.load("KoichiYasuoka/bert-base-vietnamese-upos")
print(nlp("Hai cái đầu thì tốt hơn một."))
See Also
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
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Model tree for KoichiYasuoka/bert-base-vietnamese-upos
Base model
FPTAI/vibert-base-cased