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a-ivanovitch/bge-m3-bank-it
bge-m3-bank-it is a sentence similarity model from a-ivanovitch. Use it when you need a score for how close two texts are. It is set up for FlagEmbedding. The card lists the license as mit.
A fine-tuned version of BAAI/bge-m3 for domain-specific retrieval and reranking.
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From the Hugging Face model README
A fine-tuned version of BAAI/bge-m3 for domain-specific retrieval and reranking.
Produces dense, sparse (lexical), and ColBERT embeddings simultaneously.
Evaluation on held-out test set (20% split, queries never seen during training):
| Metric | Base (bge-m3) | Fine-tuned | Delta |
|---|---|---|---|
| Recall@1 | 29.5% | 85.0% | ↑ 55.5% |
| Recall@5 | 63.0% | 96.5% | ↑ 33.5% |
| Recall@10 | 76.0% | 99.0% | ↑ 23.0% |
| MRR | 45.0% | 90.4% | ↑ 45.4% |
| NDCG@10 | 51.8% | 92.5% | ↑ 40.6% |
| Metric | Base (bge-m3) | Fine-tuned | Delta |
|---|---|---|---|
| Accuracy | 37.5% | 94.0% | ↑ 56.5% |
| MRR | 57.3% | 96.6% | ↑ 39.4% |
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel("Sophia-AI/bge-m3-bank-it", device="cuda", use_fp16=True)
# Embeddings
output = model.encode(["Your query here"], return_dense=True, return_sparse=True)
# Reranking
scores = model.compute_score(
[["query", "document"]],
weights_for_different_modes=[0.30, 0.65, 0.05],
)
This model was fine-tuned using bge-auto-tune:
pip install bge-auto-tune
bge-auto-tune generate --collection your_collection --min-pairs 2000
bge-auto-tune finetune --dataset bge_m3_training.jsonl --epochs 4
bge-auto-tune test --model ./bge-m3-finetuned
bge-auto-tune publish --repo your-user/your-model-name