Sentence Similarity
Adapters
Safetensors
sentence-transformers
English
Chinese
qwen3
mteb
retriever
text-embeddings-inference
custom_code
Instructions to use infgrad/Jasper-Token-Compression-600M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use infgrad/Jasper-Token-Compression-600M with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("infgrad/Jasper-Token-Compression-600M", set_active=True) - sentence-transformers
How to use infgrad/Jasper-Token-Compression-600M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("infgrad/Jasper-Token-Compression-600M", 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:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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