Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3
feature-extraction
embedding
text-embedding
retrieval
text-embeddings-inference
Instructions to use Octen/Octen-Embedding-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Octen/Octen-Embedding-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Octen/Octen-Embedding-4B") 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] - Notebooks
- Google Colab
- Kaggle
Add octen.ai links to Octen-Embedding model README
Browse files
README.md
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# Octen-Embedding-4B
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Octen-Embedding-4B is a text embedding model
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## Key Highlights
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- **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency
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- **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments
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## Experimental Results
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# Octen-Embedding-4B
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Octen-Embedding-4B is a text embedding model developed by [Octen](https://octen.ai/) for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) and supports multiple languages, providing high-quality embeddings for various applications.
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## Key Highlights
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- **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency
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- **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments
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For API access, deployment solutions, and technical documentation, visit [octen.ai](https://octen.ai/).
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## Experimental Results
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