Sentence Similarity
sentence-transformers
Safetensors
Transformers
Vietnamese
Vietnamese
feature-extraction
phobert
vietnamese
sentence-embedding
custom_code
Instructions to use dangvantuan/vietnamese-document-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dangvantuan/vietnamese-document-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/vietnamese-document-embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use dangvantuan/vietnamese-document-embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dangvantuan/vietnamese-document-embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Multilingual powerhouse — testing for mobile deployment
#5
by 3morixd - opened
This model covers Hebrew, Italian, Spanish, German, Turkish — exactly the kind of multilingual capability we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone. Models like this are the foundation.
We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to see which maintain quality across languages when quantized to 4-bit. Results vary wildly — some lose 30% quality in non-English after quantization.
Would love to see multilingual eval at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE