A finance-domain dense retriever: BAAI/bge-small-en-v1.5 (33M params) fully fine-tuned on SEC 10-K question–answer pairs for retrieval-augmented generation over financial documents.

Training

Method: full fine-tuning (all parameters)
Objective: MultipleNegativesRankingLoss (contrastive, in-batch negatives)
Data: virattt/financial-qa-10K — 6,000 (question, context) pairs, 2 epochs
Hardware: CPU only (AMD Ryzen 9 7950X)

Evaluation

Held-out SEC 10-K QA retrieval: 300 queries over a ~4,000-passage corpus (each query's gold context among distractors).

Metric	Base bge-small	Fine-tuned
Recall@1	0.670	0.820
Recall@5	0.850	0.930
nDCG@10	0.780	0.891
MRR@10	0.745	0.870

Caveat: single-dataset evaluation (financial-qa-10K held-out split); not a BeIR-wide benchmark.

Usage

python

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("vivekkopthsd/finance-retriever-bge-small")
emb = model.encode(["What was the company's revenue growth driver in fiscal 2023?"])