Update README.md

README.md

@@ -4,4 +4,50 @@ metrics:
 - r_squared
 base_model:
 - InstaDeepAI/nucleotide-transformer-v2-500m-multi-species
----
+---
+
+# PanDrugTransformer Model Card
+
+## Model Overview
+
+**PanDrugTransformer** is a sequence-to-value regression model designed to predict `RT_transformed` from nucleotide sequences and drug context.
+
+- **Architecture:** Custom transformer with cross-attention between nucleotide sequence and drug embedding, plus a regression head.
+- **Base Model:** [`InstaDeepAI/nucleotide-transformer-v2-500m-multi-species`](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species)
+- **Purpose:** Predict readthrough rates for given nucleotide sequences and drug conditions.
+
+## Training Procedure
+
+- **Hyperparameter Optimization:** Optuna was used to tune model parameters.
+- **Final Training:** Best hyperparameters were selected for full training on processed splits.
+- **Evaluation Metrics:** R² (coefficient of determination) on validation/test sets.
+
+## Data
+
+- **Splits:** Model trained and evaluated on processed train/validation/test splits.
+- **Features:** Each sample includes a nucleotide sequence and a drug name column (embedded for cross-attention).
+
+## Usage Instructions
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("Dichopsis/TransStop")
+tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-500m-multi-species")
+
+# Example input
+sequence = "CGTTGGTAGCCAATT" # (6nt-STOP-6nt)
+drug_name = "Clitocine"  # Format as required by model
+
+inputs = tokenizer(sequence, return_tensors="pt")
+# Add drug name embedding as required by model's API
+outputs = model(**inputs, drug_name=drug_name)
+prediction = outputs.logits.item()  # Regression output
+```
+
+## Notes for Hugging Face Users
+
+- **Drug Embedding:** Drug name is embedded and integrated via cross-attention.
+- **Regression Head:** Model outputs a continuous value.
+- **Compatibility:** Requires a 15nt nucleotide sequence (6nt-STOP-6nt) and drug name input.
+- **Evaluation:** R² reported for validation/test splits.