Instructions to use HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B") 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:
- 9a11ed0909f8069d0112dc706cd9dc82bf51a304f6034166660869ebdc056dec
- Size of remote file:
- 11.4 MB
- SHA256:
- 5f45684bb3bd50e1eb753e6bc438efc14329c293af236ecd331667b46657a3cc
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