---
library_name: "pytorch"
tags:
  - protein
  - biosequence
  - cnn
  - embedding
license: apache-2.0
---

# CNNED_Protein

CNN-based embedding model for protein/bio sequences (triplet/contrastive training ready).

## Model Summary
- **Input**: one-hot encoded sequence of shape `(B, A, L)`
- **Encoder**: 1D CNN + AvgPooling stacks
- **Output**: L2-normalized embedding `(B, D)` via projection head
- **Training**: Designed for triplet/contrastive loss (anchor, positive, negative)

### Config
- `alphabet_size`: 27
- `target_size`: 128
- `channel`: 256
- `depth`: 3
- `kernel_size`: 7
- `l2norm`: True

## Usage

```python
import json, torch
from safetensors.torch import load_file

# Load config
cfg = json.load(open("config.json","r"))
from model import CNNED_Protein
model = CNNED_Protein(**cfg).eval()

# Load weights
try:
    sd = load_file("model.safetensors")
except Exception:
    sd = torch.load("model.pt", map_location="cpu")
model.load_state_dict(sd, strict=True)
model.eval()

# Dummy inference
# x: (B, A, L) one-hot tensor
x = torch.randn(2, cfg['alphabet_size'], 512)
y, z = model.encode(x)
print(y.shape)  # (2, target_size)
```