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finance-retriever-bge-small
vivekkopthsd/finance-retriever-bge-small
finance-retriever-bge-small is a sentence similarity model from vivekkopthsd. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as apache-2.0.
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.
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At a glance
- Task
- Sentence Similarity
- Library
- sentence-transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Aug 4, 2026
- Updated
- Aug 4, 2026
- SHA
- d04923d0
Base models
Datasets
Model card
From the Hugging Face model README
finance-retriever-bge-small
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
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?"])
- Task
- Sentence Similarity
- Library
- sentence-transformers
- Type
- bert
- License
- apache-2.0
- Created
- Aug 4, 2026
- Updated
- Aug 4, 2026