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
- Xet hash:
- 672a741d9f9f81cccba4456048f03927e9dbfbe7b467e14a7a649f777865f04f
- Size of remote file:
- 1.19 GB
- SHA256:
- 5880e3ba048376161fb1e38062137c2d815bfcc08229bf1d8de013bf58eed1fe
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