neuralchainai/embeddinggemma-300m-insuranceqa

A fully fine-tuned (not LoRA) EmbeddingGemma retriever for insurance question answering. Base model: google/embeddinggemma-300m. Fine-tuned on deccan-ai/insuranceQA-v2 with MultipleNegativesRankingLoss + MatryoshkaLoss.

Before / after (test split, dim 768)

Retrieval is scored against a fixed global unique-answer bank (all splits deduped).

Metric	Base	Fine-tuned	Δ
recall@1	0.2753	0.3181	+0.0428
recall@5	0.5031	0.5978	+0.0946
recall@10	0.6082	0.7108	+0.1026
recall@100	0.8774	0.9550	+0.0776
mrr@10	0.4747	0.5487	+0.0740
ndcg@10	0.4709	0.5532	+0.0824

Usage

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("neuralchainai/embeddinggemma-300m-insuranceqa")
q = model.encode_query(["How much does term life insurance cost?"])
d = model.encode_document(["Term life premiums depend on age, health, and coverage."])

Prompts (asymmetric)

• query: task: search result | query:
• document: title: none | text:

Matryoshka dimensions

[768, 512, 256, 128]

Limitations

Evaluated against a fixed global answer bank; a test question's gold answer often also appears among training answers (InsuranceQA reuses answers), so the headline numbers reflect a shared train/prod bank. See the repo's unseen-answer subset for a generalization-only view.

License

Derivative of Google's EmbeddingGemma, distributed under the Gemma Terms of Use. Use is subject to those terms.