SriRamanaAtmic/AtmicEmbeddingv3

AtmicEmbedding v3 (English query→passage)

Contrastive continue-training of AtmicEmbeddingv2 on a much larger combined corpus: 452 expert Q&A pairs + 1,356 Claude-generated paraphrase/scenario questions (same answers)

• 1,368 pre-mined theoretical-dialogue triplets (question/positive/negative_1-3, deduped where slots repeated — 3,176 total training rows, 1,666 unique passages). Every row gets 3 negatives; where a source doesn't already supply them, negative_1 is drawn from a 100-chunk pool of genuinely mistaken interpretations of Ramana's teaching (extracted from a scholarly critique, then rewritten to strip named-interpreter attribution), and all negative selection uses hybrid dense+BM25 scoring (Reciprocal Rank Fusion). Use query: / passage: prefixes + mean pooling + L2 normalize.

Benchmarks

Three-way comparison — base intfloat/multilingual-e5-large (zero domain fine-tuning) vs. v2 vs. v3 — on two test sets.

Own held-out test data (275 rows, 581-passage corpus):

Metric	base e5-large	v2	v3
Triplet accuracy	0.611	0.532	0.884
Recall@1	0.251	0.233	0.415
Recall@5	0.491	0.466	0.651
MRR@10	0.361	0.348	0.521
NDCG@10	0.399	0.392	0.566

v3 beats both base and v2 by a wide margin here — the larger, more diverse training set (theoretical dialogue data plus paraphrase/scenario questions) generalizes well beyond either the un-fine-tuned base model or v2's narrower specialization.

Full 161_pass dataset (644 triplets — v2's OWN original training data, used as a retention / catastrophic-forgetting check):

Metric	base e5-large	v2	v3
Triplet accuracy	0.775	0.926	0.812
Recall@1	0.592	0.778	0.571
Recall@5	0.800	0.957	0.789
MRR@10	0.682	0.852	0.675
NDCG@10	0.717	0.883	0.717

Honest tradeoff, stated plainly: v3's retention on 161_pass is the weakest of any AtmicEmbedding release — it has round-tripped to roughly base-model performance on this specific corpus (R@1 0.571 vs base's 0.592), losing most of the specialization v1→v2 training added there. This is a real cost of training on ~1.75x more data (3,176 vs the prior round's 1,808 rows) at the same epoch/learning-rate/freeze-layer budget, which pulls the model further from v2's weights. Notably, v2 itself underperforms base on the newer, more diverse own-test-data set (0.532 vs 0.611 accuracy) — v2 appears overfit to 161_pass's narrower style, and v3 trades that narrow strength for broader generalization. If 161_pass-specific retrieval quality matters for your use case, evaluate v3 there directly before switching from v2.