Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:43
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use gaggi009/sbert-su-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gaggi009/sbert-su-docs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gaggi009/sbert-su-docs", trust_remote_code=True) 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
File size: 219 Bytes
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1.0,11,0.20000000298023224
2.0,22,0.4000000059604645
3.0,33,0.4000000059604645
4.0,44,0.4000000059604645
5.0,55,0.4000000059604645
6.0,66,0.4000000059604645
7.0,77,0.4000000059604645
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