Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Vietnamese
roberta
feature-extraction
phobert
vietnamese
sentence-embedding
Instructions to use dangvantuan/vietnamese-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dangvantuan/vietnamese-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/vietnamese-embedding") 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-embedding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dangvantuan/vietnamese-embedding") model = AutoModel.from_pretrained("dangvantuan/vietnamese-embedding", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from dangvantuan/vietnamese-embedding: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/dangvantuan/vietnamese-embedding/resolve/refs%2Fpr%2F8/sentence_bert_config.json
- Command line
-
hf download hf://dangvantuan/vietnamese-embedding@refs/pr/8/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/dangvantuan/vietnamese-embedding/resolve/refs%2Fpr%2F8/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |