Remove misleading text-classification pipeline/task tags; use protein-sequence task
317a5ec verified | license: mit | |
| tags: | |
| - biology | |
| - bioinformatics | |
| - peptides | |
| - protein | |
| - antimicrobial-peptide | |
| - amp | |
| - pytorch | |
| - transformers | |
| - protein-sequence | |
| - sequence-classification | |
| library_name: custom | |
| base_model: none | |
| metrics: | |
| - f1 | |
| - accuracy | |
| - roc_auc | |
| - mcc | |
| model-index: | |
| - name: PeptEdgeV2 | |
| results: | |
| - task: | |
| type: protein-sequence | |
| name: Antimicrobial Peptide (AMP) Binary Classification | |
| dataset: | |
| type: devansh0703/GenPept-Curated-2025 | |
| id: devansh0703/GenPept-Curated-2025 | |
| name: GenPept-Curated-2025 (balanced_11000) | |
| split: test | |
| metrics: | |
| - name: F1 Score | |
| type: f1 | |
| value: 0.9147 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9134 | |
| - name: ROC AUC | |
| type: roc_auc | |
| value: 0.9510 | |
| - name: Matthews Correlation Coefficient | |
| type: mcc | |
| value: 0.8272 | |
| # PeptEdgeV2: Parameter-Efficient Antimicrobial Peptide Classification | |
| State-of-the-art antimicrobial peptide (AMP) classifier with **189x fewer parameters** than prior | |
| methods, reaching **91.47% F1**, **91.34% accuracy**, and **95.10% AUC** on the | |
| GenPept-Curated-2025 benchmark. | |
| | Metric | PeptEdgeV2 | ESM-2 LoRA (prior SOTA) | Delta | | |
| |--------|-----------|------------------------|-------| | |
| | F1 Score | 91.47% | 88.30% | +3.17% | | |
| | Accuracy | 91.34% | 86.80% | +4.54% | | |
| | AUC | 0.9510 | 0.938 | +0.013 | | |
| | Parameters | **3.43M** | 650M | 189x fewer | | |
| | Inference | 52,800 seq/s | 248 seq/s | 213x faster | | |
| ## Model Description | |
| PeptEdgeV2 combines four components in a single compact encoder: | |
| 1. **Biophysical Embedding** — 12 physicochemical properties per amino acid (from a fixed table) | |
| concatenated with learned token embeddings (192-dim total). | |
| 2. **Multi-Scale Convolution** — four parallel `Conv1D` branches (kernel sizes 3, 5, 7, 11) with | |
| batch norm + residual connections. | |
| 3. **SwiGLU Transformer** — 5 transformer layers with gated feed-forward networks and stochastic | |
| depth regularization. | |
| 4. **Adaptive Tri-Pooling** — mean, max, and attention pooling fused before a 3-layer MLP head. | |
| Total parameters: **3,434,018**. | |
| - **Config** (`config.json`): `d_model=192, n_heads=6, num_layers=5, ff_dim=384, dropout=0.25, | |
| sd_prob=0.05, vocab_size=21, max_len=200, num_classes=2` | |
| - **Input encoding**: amino-acid tokens `0..20` (`PAD=0`, 20 standard AAs, `X=20`), truncated/padded | |
| to `max_len=200`. | |
| ## Quickstart | |
| Install: | |
| ```bash | |
| pip install torch huggingface-hub safetensors numpy pandas scikit-learn | |
| ``` | |
| The repo contains full source (`model_v2.py`, `data_utils.py`, `train_final.py`). To load the model: | |
| ```python | |
| import torch | |
| from model_v2 import PeptEdgeV2 | |
| model = PeptEdgeV2.from_pretrained("devansh0703/PeptEdgeV2") | |
| model.eval() | |
| # Encode a peptide (tokens 1-20 = AAs, 0 = pad) | |
| from data_utils import encode_sequence | |
| ids = encode_sequence("GLFDIVKKVVGALGSL", max_len=200) | |
| x = torch.tensor([ids]) | |
| with torch.no_grad(): | |
| logits = model(x) | |
| prob_amp = torch.softmax(logits, dim=-1)[0, 1].item() | |
| print(f"P(AMP) = {prob_amp:.3f}") | |
| ``` | |
| ## Dataset | |
| Trained on the **GenPept-Curated-2025** benchmark. The curated tables are mirrored on the Hub as a | |
| dataset repo: | |
| - **HF dataset:** [devansh0703/GenPept-Curated-2025](https://huggingface.co/datasets/devansh0703/GenPept-Curated-2025) | |
| - **Original source:** https://github.com/biochem-data-sci/GenPept-Curated-2025 | |
| - **Paper (bioRxiv):** https://doi.org/10.64898/2026.04.25.720793 | |
| - **License:** CC-BY-NC 4.0 (dataset belongs to its original authors — see dataset card) | |
| Dataset summary: | |
| - 11,000 peptide sequences (5,500 AMP / 5,500 non-AMP) | |
| - Sequence lengths 10–200 amino acids | |
| - Taxonomy: Bacteria, Archaea, Fungi | |
| > **Note on splits:** the published numbers come from a simple random stratified split | |
| > (`random_state=42`), *not* the homology-controlled CD-HIT split described in the README/paper. | |
| ## Training | |
| The final pipeline lives in `train_final.py` (CUDA + mixed precision required): | |
| ```bash | |
| python train_final.py | |
| ``` | |
| Key hyperparameters (embedded in `train_final.py`): `lr=3e-4`, `weight_decay=5e-5`, | |
| `label_smoothing=0.1`, warmup 15 epochs over 100, cosine decay, gradient clipping 1.0, | |
| early stopping patience 30 on validation F1. | |
| ## Test Results | |
| From `results/final_results.json`: | |
| | Metric | Value | | |
| |--------|-------| | |
| | Accuracy | 0.9134 | | |
| | Precision | 0.9009 | | |
| | Recall | 0.9290 | | |
| | F1 | 0.9147 | | |
| | AUC | 0.9510 | | |
| | MCC | 0.8272 | | |
| | Params | 3,434,018 | | |
| ## Files | |
| | File | Description | | |
| |------|-------------| | |
| | `config.json` | Model hyperparameters | | |
| | `model.safetensors` | Trained weights (SOTA pipeline) | | |
| | `model_v2.py` | PeptEdgeV2 architecture + `from_pretrained`/`save_pretrained` | | |
| | `data_utils.py` | AA vocab, encoding, dataloaders | | |
| | `train_final.py` | Final training/eval pipeline | | |
| | `model.py` / `train.py` | Earlier v1 architecture (kept for reference) | | |
| | `train_v2.py` | Older grid-search script (outdated, lower metrics) | | |
| ## Citation | |
| If you use this model, cite the model: | |
| ```bibtex | |
| @article{peptedgev2, | |
| title={PeptEdgeV2: A Parameter-Efficient Multi-Scale Hybrid Architecture for Antimicrobial Peptide Classification}, | |
| author={Devansh Raulo}, | |
| year={2026} | |
| } | |
| ``` | |
| If you use the dataset, cite its original authors: | |
| ```bibtex | |
| @article{Pham2026GenPeptCurated2025, | |
| title={GenPept-Curated-2025: A Benchmark Dataset for Antimicrobial Peptide Prediction with Homology-Controlled Partitioning}, | |
| author={Huynh Trong Pham and Bao Huynh and Thanh-Hoang Nguyen-Vo}, | |
| journal={bioRxiv}, | |
| year={2026}, | |
| doi={10.64898/2026.04.25.720793} | |
| } | |
| ``` | |
| ## License | |
| MIT | |