CuongCao/oe-bge-m3-LoRA-ft-v2
0
BGE-M3 LoRA — Finetuned on OE Support Knowledge Base
This is a LoRA-finetuned version of BAAI/bge-m3 trained on an internal Order Express (OE) support knowledge base to improve retrieval accuracy for customer support queries.
Training Summary
Training Process
Usage
With sentence-transformers (merged model — recommended for inference)
import os
from huggingface_hub import snapshot_download
from langchain_huggingface import HuggingFaceEmbeddings
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
print("Downloading merged model files...")
local_model_dir = snapshot_download(
repo_id="CuongCao/oe-bge-m3-LoRA-ft-v2",
allow_patterns=["merged/*", "merged/**/*"]
)
merged_path = os.path.join(local_model_dir, "merged")
print("Loading model into LangChain wrapper...")
model = HuggingFaceEmbeddings(
model_name=merged_path,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
query = "How do I reset my password?"
documents = ["Password reset procedure", "Account settings guide", ...]
query_emb = model.encode(query)
doc_embs = model.encode(documents)
# Cosine similarity
scores = query_emb @ doc_embs.TRepo Structure
CuongCao/oe-bge-m3-LoRA-ft-v2/
├── lora-adapters/ # LoRA adapter weights only (~12 MB)
│ ├── adapter_config.json
│ └── adapter_model.safetensors
└── merged/ # Full merged model (~2.2 GB, ready for inference)
├── config.json
├── model.safetensors
└── ...Citation
If you use this model, please cite the original BGE-M3 paper:
@article{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
journal={arXiv preprint arXiv:2402.03216},
year={2024}
}