README.md

metadata

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

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)