Feature Extraction
Transformers
PyTorch
Vietnamese
viconbert
bert
wsd
vietnamese
semantic_similarity
custom_code
Instructions to use tkhangg0910/viconbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkhangg0910/viconbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tkhangg0910/viconbert-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tkhangg0910/viconbert-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add library_name, update paper link and enhance model table
#1
by nielsr HF Staff - opened
This PR improves the model card for ViConBERT by:
- Adding
library_name: transformersto the metadata, enabling the automated "How to use" widget on the model page. This is supported by thetransformersimports and usage in the provided example code (AutoModel.from_pretrained,AutoTokenizer.from_pretrained). - Correcting the paper link in the content to
https://huggingface.co/papers/2511.12249for accuracy. - Adding a direct link to the GitHub repository (
https://github.com/tkhangg0910/ViConBERT) for easier access to the source code. - Enhancing the "ViConBERT models" table to include the "Backbone" column with links to the base models, aligning with the original GitHub README and providing more comprehensive information.
tkhangg0910 changed pull request status to merged