Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3830
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SriRamanaAtmic/AtmicQuoterv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SriRamanaAtmic/AtmicQuoterv1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SriRamanaAtmic/AtmicQuoterv1") sentences = [ "Represent this sentence for searching relevant passages: In Aksharamanamalai, which verse contains the prayer asking Arunachala to rule over the devotee graciously through his Holy Feet?", "[Aksharamanamalai] Verse 4: Arunachala! For whose sake dids’t Thou claim me? Thou, who ruled over me happily earlier were to abandon me thereafter with disdain the whole world will heap calumny on Thee.", "[Aksharamanamalai] Verse 63: Holy Feet and be pleased to rule over me graciously.", "[Aksharamanamalai] Verse 105: May Thou live for all eternity protecting forever the poor and hapless devotees like me all over, bestowing on all the infinite and rapturous bliss in Siva.", "[The Legend of King Ballala] In this holy place was the seat of the kingdom of King Ballala... Arunachala came in the form of a Child and played in this King’s lap... the Lord said that as the King had no progeny He would Himself perform his annual ceremonies (sraddha) for all eternity | Topics: arunachala_kshetra" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
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