Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3
feature-extraction
embedding
text-embedding
retrieval
text-embeddings-inference
Instructions to use Octen/Octen-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Octen/Octen-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Octen/Octen-Embedding-0.6B") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Octen/Octen-Embedding-0.6B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Octen/Octen-Embedding-0.6B/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://Octen/Octen-Embedding-0.6B@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Octen/Octen-Embedding-0.6B/resolve/refs%2Fpr%2F1/tokenizer.json
11.4 MB
- Xet hash:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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