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
Commit ·
9d43188
1
Parent(s): de97e7a
Update SAURON for cosine
Browse files
eval/triplet_evaluation_val_loss_results.csv
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epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean
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epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean
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pytorch_model.bin
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