Sentence Similarity
sentence-transformers
ONNX
English
Chinese
qwen3
feature-extraction
text-embeddings
embeddings
retrieval
mteb
onnxruntime
cpu
fp32
custom_code
text-embeddings-inference
Instructions to use magiccodingman/Jasper-Token-Compression-600M-ONNX-FP32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use magiccodingman/Jasper-Token-Compression-600M-ONNX-FP32 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magiccodingman/Jasper-Token-Compression-600M-ONNX-FP32", trust_remote_code=True) 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
- Xet hash:
- fde87e4333c7f0304c62c786b8fa9ebc8c0ecc0fc3454565e7e6b9cc5c4ca154
- Size of remote file:
- 1.78 MB
- SHA256:
- 5f50bfa3ad46f1587bf9d189ae23a8bf6ddd44445dbc12c0e3924193a3905a88
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