Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
roberta
feature-extraction
style
representation
text-embeddings-inference
Instructions to use TimKoornstra/SAURON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TimKoornstra/SAURON with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TimKoornstra/SAURON") 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] - Transformers
How to use TimKoornstra/SAURON with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TimKoornstra/SAURON") model = AutoModel.from_pretrained("TimKoornstra/SAURON") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36ecb6ce393980e75923576c1ab263fd2323b77bd4c62d58beb49447f6ca7388
- Size of remote file:
- 499 MB
- SHA256:
- fb393031f91a1f7b21e8253d240bb0d7f247223b813a29eefb8423ae8415e1d9
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