Instructions to use navteca/multi-qa-mpnet-base-cos-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use navteca/multi-qa-mpnet-base-cos-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("navteca/multi-qa-mpnet-base-cos-v1") 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
Download pytorch_model.bin from navteca/multi-qa-mpnet-base-cos-v1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/navteca/multi-qa-mpnet-base-cos-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://navteca/multi-qa-mpnet-base-cos-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/navteca/multi-qa-mpnet-base-cos-v1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a6c9db3a20b8ad8a70cbb12c314afea466096b137ec5ba1634b2951c6e78c6d1
- Size of remote file:
- 438 MB
- SHA256:
- 5aba022a15fe19a16a4a78271c9289705066308dbf65765618cc7f4856bcd582
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.