Instructions to use HgThinker/vietnamese-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HgThinker/vietnamese-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HgThinker/vietnamese-sbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use HgThinker/vietnamese-sbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HgThinker/vietnamese-sbert") model = AutoModel.from_pretrained("HgThinker/vietnamese-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bf303175eada57716eca2dae2617fcd61e9276f5d562bc4a510ce85ea3551c9
- Size of remote file:
- 540 MB
- SHA256:
- 0bc91deab57649fc0ff01ab68a7a9d8b5826fc2a18525ed271c591e4e53e5e2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.