Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:111470
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/model-b-structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/model-b-structured with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/model-b-structured") sentences = [ "when was the first elephant brought to america", "Old Bet The first elephant brought to the United States was in 1796, aboard the America which set sail from Calcutta for New York on December 3, 1795.[4] However, it is not certain that this was Old Bet.[2] The first references to Old Bet start in 1804 in Boston as part of a menagerie.[1] In 1808, while residing in Somers, New York, Hachaliah Bailey purchased the menagerie elephant for $1,000 and named it \"Old Bet\".[5][6]", "Cronus Rhea secretly gave birth to Zeus in Crete, and handed Cronus a stone wrapped in swaddling clothes, also known as the Omphalos Stone, which he promptly swallowed, thinking that it was his son.", "Renal artery One or two accessory renal arteries are frequently found, especially on the left side since they usually arise from the aorta, and may come off above (more common) or below the main artery. Instead of entering the kidney at the hilus, they usually pierce the upper or lower part of the organ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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