Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:5655
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use SriRamanaAtmic/AtmicQuoterv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SriRamanaAtmic/AtmicQuoterv3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SriRamanaAtmic/AtmicQuoterv3") sentences = [ "Represent this sentence for searching relevant passages: moon water reflection analogy Ramana teachings on Self", "The mind is like the moon, deriving its light of consciousness from the Self, which thus resembles the Sun", "Sri Ramanasramam never charged for Bhagavan’s Darshan (seeing him) or for his teachings.", "Verse 14: Even to the distinctionless you, distinctions... appear even as the movements of water are mistaken for the dance of the still moon.", "Verse 8: Like bubbles rising on the surface of the waters of the ocean, all the worlds arise from, stay in and resolve into the Supreme Being." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
Commit History
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Retrain on combined v1+v3 data with [source tag] prefix and | Topics suffix stripped
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Replace hit_rate@4 with hit_rate@2
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Add production-shaped hit_rate@4 benchmark (hybrid retrieve -> crossencoder rerank)
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Call out mrr@10 v3-vs-base delta explicitly
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Corrected AtmicQuoterv3: removed degenerate RAG_citation entries, positive-vs-positive crossencoder reranking, self-mined negatives, combined multi-source dataset. Clear winner vs base and v2.
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AtmicQuoterv3: bge-small fine-tuned on verbatim-matched, self-negative-mined layer2 data
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initial commit
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