Feature Extraction
ONNX
sentence-transformers
English
embeddings
scientific-papers
distillation
int8
wasm
research-library
Instructions to use PeytonT/1m-paper-embedding-model-lite-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PeytonT/1m-paper-embedding-model-lite-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PeytonT/1m-paper-embedding-model-lite-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:
- c5481234a6dd5d8e5e9822cb6765005f093782e961f18ecb0ddc5ef468317149
- Size of remote file:
- 45.5 MB
- SHA256:
- 29b1bd3f06aba867d8094882d954ba5a0e077af0ae4036976bbe9d61ec6f3f26
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