File size: 1,151 Bytes
eda7ec8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
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)
```
|