Fine-tuned sentence-embedding model for financial text retrieval. Upgrades semantic search / RAG retrieval over financial sentences (news, invoices, contracts) by learning sentiment-aware similarity.

Model

Base: BAAI/bge-small-en-v1.5 (~33M params)
Method: contrastive fine-tuning with MultipleNegativesRankingLoss
Training pairs: 4,356 same-label sentence pairs from FinancialPhraseBank (Malo et al. 2014; mirrored on Kaggle)
Hyperparameters: 3 epochs, batch 32, lr 2e-05, max_seq 128
Hardware: Tesla T4 (Kaggle GPU)
Word-embedding layer frozen during training for stability

Evaluation (leave-one-out label retrieval, held-out 483 sentences)

Metric	Base	Fine-tuned	Δ
Recall@1	0.6729	0.8282	+0.1553
MRR	0.7995	0.8848	+0.0853

Pooled-200 subsample: Recall@1 0.6150 → 0.8100, MRR 0.7684 → 0.8784 (base → fine-tuned).

A retrieval is a hit when the top-1 neighbor of a sentence shares its financial-sentiment label (positive/neutral/negative).

Usage

python

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("vivekkopthsd/financial-embedding-bge-small")
emb = model.encode(["Earnings per share amounted to a loss of EUR 0.38."])

Limitations

Evaluated on a label-retrieval proxy, not human-judged search relevance.
English only; vocabulary limited to financial-news language.
Full eval script: Kaggle notebook financial-embedding-finetune-bge-small (vivekkopthsd).