shanaka95/embeddinggemma-300m-rag-stage1

embeddinggemma-300m-rag-stage1

Fine-tuned version of google/embeddinggemma-300m on the EnterpriseRAG-Bench question-document pairs for first-place retrieval.

Training

Evaluation (89 questions, 5k corpus subset, 2048 ctx)

Compared against google/embeddinggemma-300m baseline.

Eval setup: 89 of 470 EnterpriseRAG-Bench questions whose first expecteddocid falls in the first 5k corpus docs, scored against those 5k. Re-encoded corpus with the fine-tuned model (NEVER re-use base-model corpus embeddings — different spaces).

Usage

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("shanaka95/embeddinggemma-300m-rag-stage1")
model.max_seq_length = 2048  # EmbeddingGemma native

query_emb = model.encode("How does X work?", prompt_name="query")
doc_emb   = model.encode("X is a method that ...", prompt_name="document")
score = (query_emb @ doc_emb.T)  # cosine (already normalized)

Training data

• —Queries: shanaka95/enterprise-rag-questions
• —Documents: onyx-dot-app/EnterpriseRAG-Bench — documents split
• —Eval: same questions split, filtered to those with at least one expected_doc_id in the docs corpus (470 questions), using the first expecteddocid as gold.

Notes

• —Use prompt names query and document (matches model.prompts).
• —For best retrieval results, re-encode the corpus with this model (do not reuse base-model embeddings).
• —Stage 2 (hard negative mining) is in progress for further improvements.