Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use li-ping/supervised_ft_embedding_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use li-ping/supervised_ft_embedding_v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("li-ping/supervised_ft_embedding_v4") 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:
- 65ad08cf159ca9f8815e7fee953200f9a8fb3ab714e13bd9001374dc2894f3c4
- Size of remote file:
- 1.11 GB
- SHA256:
- e701b13daca815087522e29b0d22d2f76f81be1ad4b764d9ce1cdc0c641ce327
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.