Update README.md

README.md

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----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+language:
+- en
+base_model:
+- facebook/esm2_t33_650M_UR50D
+tags:
+- Antifungal
+- Protein
+- Bioinformatics
+- Machine Learning
+---
+
+# AntiFP2: Fine-tuned ESM2 Antifungal Protein Classifier
+
+This repository contains a fine-tuned ESM2 model for classifying antifungal proteins from amino acid sequences. The model is trained to predict binary labels indicating whether a protein is antifungal or not.
+
+## Model Description
+
+- **Base Model:** ESM2-t36-3B-UR50D (Fine-tuned)
+- **Fine-tuning Task:** Binary antifungal protein classification.
+- **Architecture:** ESM2 backbone with a linear classification head.
+- **Input:** Protein amino acid sequences.
+- **Output:** Binary labels (0 = non-antifungal, 1 = antifungal).
+
+## Repository Contents
+
+- `pytorch_model.bin`: Trained model weights.
+- `alphabet.bin`: ESM2 alphabet (tokenizer).
+- `config.json`: Model configuration.
+- `README.md`: This file.
+
+## Usage
+
+### Installation
+
+Install required Python packages:
+
+```bash
+pip install torch esm biopython huggingface_hub
+````
+
+### Loading the Model from Hugging Face
+
+```python
+import torch
+import torch.nn as nn
+import esm
+from huggingface_hub import hf_hub_download
+import json
+
+# Define the classifier architecture (must match training)
+class ProteinClassifier(nn.Module):
+    def __init__(self, esm_model, embedding_dim, num_classes):
+        super(AntiFP2Classifier, self).__init__()
+        self.esm_model = esm_model
+        self.fc = nn.Linear(embedding_dim, num_classes)
+    def forward(self, tokens):
+        with torch.no_grad():
+            results = self.esm_model(tokens, repr_layers=[36])
+        embeddings = results["representations"][36].mean(1)
+        output = self.fc(embeddings)
+        return output
+
+# Download model files from Hugging Face Hub
+repo_id = "your-username/antifp2"
+model_weights_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
+alphabet_path = hf_hub_download(repo_id=repo_id, filename="alphabet.bin")
+config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+
+# Load ESM2 backbone model
+esm_model, alphabet = esm.pretrained.esm2_t36_3B_UR50D()
+
+# Load configuration
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+# Initialize classifier
+classifier = ProteinClassifier(esm_model, embedding_dim=config['embedding_dim'], num_classes=config['num_classes'])
+
+# Load weights
+classifier.load_state_dict(torch.load(model_weights_path))
+classifier.eval()
+
+# Load alphabet tokenizer
+alphabet = torch.load(alphabet_path)
+batch_converter = alphabet.get_batch_converter()
+
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+classifier = classifier.to(device)
+```
+
+## Input Format
+
+Input sequences must be provided as amino acid strings using standard single-letter codes.
+
+## Output
+
+The model outputs logits for two classes, which can be converted to probabilities using softmax. The predicted label is antifungal (1) if the probability exceeds a threshold (e.g., 0.5).
+
+---