Instructions to use NghiaNguyen1529/octen-embedding-0.6b-directml-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NghiaNguyen1529/octen-embedding-0.6b-directml-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NghiaNguyen1529/octen-embedding-0.6b-directml-onnx") 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
- Xet hash:
- 5a30014f27df88a94ce1306dddd1ab4270fa53049d2d7d4c8a7c9f502911241d
- Size of remote file:
- 1.19 GB
- SHA256:
- 61a4510194226ae1b645843e6a65d655cc862ff1078b534495e69e10c79e178e
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