---
language:
- tr
- en
- multilingual
license: apache-2.0
library_name: PyTorch
base_model:
- BAAI/bge-m3
base_model_relation: adapter
tags:
- adapter
- bge-m3
- feature-extraction
- sentence-similarity
- contrastive-learning
- food-recognition
- turkish-nlp
- culinary-ai
- nutrition
datasets:
- ozertuu/turkish-food-contrastive-triplets
pipeline_tag: feature-extraction
---

# 🍽️ BGE-M3 Food Projection Head

A lightweight **1024-dimensional Neural Domain Adapter (8.4 MB)** trained on top of **frozen `BAAI/bge-m3`** representations.

It projects general multilingual text embeddings into a specialized **culinary semantic space**, aligning Turkish and international dish names, cooking styles, and ingredients while pushing apart raw items, canned goods, and confusing modifiers.

Developed for the **EatBetter** multimodal meal recognition and nutrition engine.

---

## 💡 Key Highlights

* **Base Model:** `BAAI/bge-m3` (560M parameters) — **Kept 100% Frozen**.
* **Adapter Size:** 2.1 Million parameters (**8.4 MB** / `model.safetensors`).
* **Why Frozen Base?** Prevents *catastrophic forgetting* of multilingual grammar while adding domain-specific contrastive alignment for gastronomy.
* **Trained on:** 30,001 culinary triplets from [`ozertuu/turkish-food-contrastive-triplets`](https://huggingface.co/datasets/ozertuu/turkish-food-contrastive-triplets).

---

## 🏗️ How It Works

```
Input Text ("Soslu Kuru Fasulye")
             │
             ▼
┌──────────────────────────────────────────────┐
│  BAAI/bge-m3 Base Encoder (Frozen, 560M)     │
└──────────────────────┬───────────────────────┘
                       │ 1024-d Base Embedding (x)
                       ▼
┌──────────────────────────────────────────────┐
│  FoodProjectionHead (8.4 MB Residual Adapter)│
│  • Linear(1024, 1024) ──► LayerNorm ──► GELU │
│  • Linear(1024, 1024)                        │
│  • Output = Normalize(x + 0.5 * MLP(x))      │
└──────────────────────┬───────────────────────┘
                       │
                       ▼
   1024-d Culinary-Aligned Food Vector (z)
```

---

## 🎯 Semantic Separation Examples

| Food Query A | Food Query B | Relationship | Similarity Score |
| :--- | :--- | :--- | :--- |
| **Kuru Fasulye** | **Soslu etli kuru fasulye yemeği** | Same Dish (Gastronomic Match) | **0.94 (High)** |
| **Kuru Fasulye** | **Kuru fasulye konservesi / tohumu** | Dish vs Raw / Canned Ingredient | **0.41 (Separated)** |
| **Kuşbaşılı Pide** | **Kuşbaşı Et** | Composite Bakery vs Pure Meat | **0.38 (Separated)** |
| **Menemen** | **Domatesli Biberli Yumurta** | Traditional Recipe Equivalence | **0.91 (High)** |

---

## 🚀 Quickstart & Usage

```python
import torch
from sentence_transformers import SentenceTransformer
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

# 1. Define Lightweight Adapter
class FoodProjectionHead(torch.nn.Module):
    def __init__(self, dim=1024):
        super().__init__()
        self.net = torch.nn.Sequential(
            torch.nn.Linear(dim, dim),
            torch.nn.LayerNorm(dim),
            torch.nn.GELU(),
            torch.nn.Linear(dim, dim)
        )
    def forward(self, x):
        return torch.nn.functional.normalize(x + 0.5 * self.net(x), p=2, dim=-1)

# 2. Load Base Model & Download Adapter
device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
base_model = SentenceTransformer("BAAI/bge-m3", device=device)

adapter = FoodProjectionHead(dim=1024).to(device)
weights_path = hf_hub_download(repo_id="ozertuu/bge-m3-food-projection-head", filename="model.safetensors")
adapter.load_state_dict(load_file(weights_path))
adapter.eval()

# 3. Generate Culinary Embeddings
foods = [
    "Kuru Fasulye",
    "Soslu etli kuru fasulye yemeği",
    "Kuru fasulye konservesi",
    "Kuşbaşılı pide",
    "Kuşbaşı et"
]

with torch.inference_mode():
    base_embeddings = base_model.encode(foods, normalize_embeddings=True, convert_to_tensor=True, device=device)
    food_embeddings = adapter(base_embeddings)

# Cosine similarity
similarity_matrix = torch.matmul(food_embeddings, food_embeddings.T)
print("Similarity [Kuru Fasulye] & [Soslu Kuru Fasulye]:", round(similarity_matrix[0, 1].item(), 4))
print("Similarity [Kuru Fasulye] & [Konserve]:", round(similarity_matrix[0, 2].item(), 4))
```

---

## 📊 Training Details

* **Loss Function:** In-batch **InfoNCE Contrastive Loss** (Temperature $\tau = 0.05$).
* **Dataset:** 30,001 Anchor-Positive-HardNegative pairs (`ozertuu/turkish-food-contrastive-triplets`).
* **Optimizer:** AdamW (`lr = 1e-3`, `weight_decay = 1e-4`).
* **Batch Size:** 64.
* **Epochs:** 5.

---

## 📜 Citation

```bibtex
@misc{ozertuu2026bgem3food,
  author = {Ertugrul Ozer},
  title = {BGE-M3 Food Projection Head: Contrastive Culinary Domain Adapter},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {https://huggingface.co/ozertuu/bge-m3-food-projection-head}
}
```