Instructions to use XRandomForest/mymodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use XRandomForest/mymodel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("XRandomForest/mymodel") 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:
- dc16141488b4c68ca8cfc51540feddac6ecef5688099bd1c185833f0442777ea
- Size of remote file:
- 19.3 kB
- SHA256:
- 60759cdc623f90f683832a2626b67d62799a31bfffb1a9b82703d5b0076da69e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.