Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3
feature-extraction
embedding
text-embedding
retrieval
text-embeddings-inference
Instructions to use Octen/Octen-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Octen/Octen-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Octen/Octen-Embedding-8B") 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
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -36,7 +36,7 @@ Octen-Embedding-8B is a text embedding model designed for semantic search and re
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("bflhc/Octen-Embedding-8B"
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# Encode sentences
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sentences = [
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import torch.nn.functional as F
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tokenizer = AutoTokenizer.from_pretrained("bflhc/Octen-Embedding-8B")
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model = AutoModel.from_pretrained("bflhc/Octen-Embedding-8B"
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model.eval()
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def encode(texts):
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("bflhc/Octen-Embedding-8B")
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# Encode sentences
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sentences = [
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import torch.nn.functional as F
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tokenizer = AutoTokenizer.from_pretrained("bflhc/Octen-Embedding-8B")
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model = AutoModel.from_pretrained("bflhc/Octen-Embedding-8B")
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model.eval()
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def encode(texts):
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