Sentence Similarity
sentence-transformers
Safetensors
Vietnamese
xlm-roberta
Embedding
text-embeddings-inference
Instructions to use thaile1809/Vietnamese_Embedding_fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thaile1809/Vietnamese_Embedding_fast with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thaile1809/Vietnamese_Embedding_fast") 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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from thaile1809/Vietnamese_Embedding_fast: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/thaile1809/Vietnamese_Embedding_fast/resolve/main/tokenizer.json
- Command line
-
hf download hf://thaile1809/Vietnamese_Embedding_fast/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thaile1809/Vietnamese_Embedding_fast/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- e6290e11ac2546a60a645b3487ba9d6032dd37076c0a574a943de1358fb00403
- Size of remote file:
- 17.1 MB
- SHA256:
- 919c4e6bc5d2c10fd3de8130c9086580c9a0e92b1c8e71b051c44b0c74f91dca
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