Sentence Similarity
sentence-transformers
ONNX
English
Chinese
qwen3
feature-extraction
text-embeddings
embeddings
retrieval
mteb
onnxruntime
cpu
int-8
custom_code
text-embeddings-inference
Instructions to use magiccodingman/Jasper-Token-Compression-600M-ONNX-INT8 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-INT8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magiccodingman/Jasper-Token-Compression-600M-ONNX-INT8", 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:
- 1ea77596f5382aa8ac4caf3fa82c1855dad92790150b356607e115fa5cc3430b
- Size of remote file:
- 612 MB
- SHA256:
- e2dfb2f44c38bf2a3db867a000b01a6bb58e0ea3893bea098e9e963a14452040
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