---
language:
  - it
  - en
license: mit
library_name: FlagEmbedding
base_model: BAAI/bge-m3
tags:
  - embeddings
  - retrieval
  - reranking
  - bge
  - bge-m3
  - fine-tuned
pipeline_tag: sentence-similarity
---

# bge-m3-bank-it

A fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) for domain-specific retrieval and reranking.

Produces **dense, sparse (lexical), and ColBERT** embeddings simultaneously.

## Benchmark Results

Evaluation on held-out test set (20% split, queries never seen during training):

### Dense Retrieval

| 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% |

### Multi-Mode Reranking

| Metric | Base (bge-m3) | Fine-tuned | Delta |
|---|---|---|---|
| Accuracy | 37.5% | **94.0%** | ↑ 56.5% |
| MRR | 57.3% | **96.6%** | ↑ 39.4% |

## Usage

```python
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],
)
```

## Fine-Tune Your Own

This model was fine-tuned using [bge-auto-tune](https://pypi.org/project/bge-auto-tune/):

```bash
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
```