Instructions to use navteca/all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use navteca/all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("navteca/all-mpnet-base-v2") 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/all-mpnet-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/navteca/all-mpnet-base-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://navteca/all-mpnet-base-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/navteca/all-mpnet-base-v2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0ab2a3b88dc0d2c888ed5d7f6a6db1238084a98c662263267ced0eacba5b18cf
- Size of remote file:
- 438 MB
- SHA256:
- a8fd120b1a0032e70ff3d4b8ab8e46a6d01c2cb08ffe7c007a021c1788928146
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.