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--- |
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library_name: "pytorch" |
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tags: |
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- protein |
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- biosequence |
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- cnn |
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- embedding |
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license: apache-2.0 |
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--- |
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# CNNED_Protein |
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CNN-based embedding model for protein/bio sequences (triplet/contrastive training ready). |
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## Model Summary |
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- **Input**: one-hot encoded sequence of shape `(B, A, L)` |
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- **Encoder**: 1D CNN + AvgPooling stacks |
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- **Output**: L2-normalized embedding `(B, D)` via projection head |
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- **Training**: Designed for triplet/contrastive loss (anchor, positive, negative) |
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### Config |
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- `alphabet_size`: 27 |
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- `target_size`: 128 |
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- `channel`: 256 |
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- `depth`: 3 |
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- `kernel_size`: 7 |
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- `l2norm`: True |
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## Usage |
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```python |
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import json, torch |
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from safetensors.torch import load_file |
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# Load config |
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cfg = json.load(open("config.json","r")) |
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from model import CNNED_Protein |
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model = CNNED_Protein(**cfg).eval() |
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# Load weights |
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try: |
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sd = load_file("model.safetensors") |
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except Exception: |
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sd = torch.load("model.pt", map_location="cpu") |
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model.load_state_dict(sd, strict=True) |
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model.eval() |
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# Dummy inference |
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# x: (B, A, L) one-hot tensor |
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x = torch.randn(2, cfg['alphabet_size'], 512) |
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y, z = model.encode(x) |
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print(y.shape) # (2, target_size) |
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``` |
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