Sentence Similarity
sentence-transformers
ONNX
Transformers
Vietnamese
roberta
feature-extraction
phobert
vietnamese
sentence-embedding
text-embeddings-inference
Instructions to use laituanmanh32/vietnamese-embedding-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use laituanmanh32/vietnamese-embedding-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("laituanmanh32/vietnamese-embedding-onnx") 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 laituanmanh32/vietnamese-embedding-onnx with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("laituanmanh32/vietnamese-embedding-onnx") model = AutoModel.from_pretrained("laituanmanh32/vietnamese-embedding-onnx") - Notebooks
- Google Colab
- Kaggle
| { | |
| "name": "vietnamese-embedding-onnx", | |
| "version": "1.0.0", | |
| "main": "test_model.js", | |
| "type": "module", | |
| "scripts": { | |
| "test": "echo \"Error: no test specified\" && exit 1" | |
| }, | |
| "author": "", | |
| "license": "ISC", | |
| "description": "", | |
| "dependencies": { | |
| "@xenova/transformers": "^2.17.2" | |
| } | |
| } | |