Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use li-ping/supervised_ft_embedding_v3 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_v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("li-ping/supervised_ft_embedding_v3") 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:
- 5c70acf8b42bd5aba38c6f6f7dd8c9b0deeacdb98da1d42f3b23f7489a2ee09d
- Size of remote file:
- 1.11 GB
- SHA256:
- c2d632ec03c8fa02f19462914c7ddce8edcd6cd6afc07723f4182261918a4838
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