Improve dataset card: Add paper/code links, fix arXiv badge, and include sample usage (#3)

- Improve dataset card: Add paper/code links, fix arXiv badge, and include sample usage (f51a680405012fc0a12c117078a1474e62090e54)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,8 @@
 ---
 license: apache-2.0
+task_categories:
+- text-generation
+- text-classification
 dataset_info:
   features:
   - name: smiles
@@ -38,15 +41,13 @@ configs:
     path: data/test-*
   - split: external
     path: data/external-*
-task_categories:
-- text-generation
-- text-classification
 tags:
 - chemistry
 - biology
 - medical
 ---
-[![arXiv](https://img.shields.io/badge/arXiv-1234.5678-b31b1b.svg)](https://arxiv.org/abs/2508.18579)
+
+[Paper](https://huggingface.co/papers/2508.18579) | [![arXiv](https://img.shields.io/badge/arXiv-2508.18579-b31b1b.svg)](https://arxiv.org/abs/2508.18579) | [Code](https://github.com/mohammad-gh009/DrugReasoner)
 
 # DrugReasoner Dataset
 This dataset contains a curated collection of approved (Phase IV clinical trial) and unapproved (pre-clinical trial) small molecules from the ChEMBL database, annotated for drug approval status. It was designed for training and evaluating DrugReasoner, a reasoning-augmented LLM for drug approval prediction.
@@ -61,6 +62,80 @@ A dataset of small molecules was obtained from the ChEMBL database (version 35)
 The dataset was partitioned into training, validation, and test subsets (8:1:1) using a stratified sampling strategy to maintain class distribution across all splits. 
 An independent external dataset includes data presented in the ChemAP paper (Cho, C. et al., 2024). This dataset contained 20 approved and 8 unapproved drugs. Three approved drugs overlapping with the training, validation, or test sets were removed, and the remaining molecules (17 approved and 8 unapproved) were used for external evaluation.
 
+## Sample Usage
+To use **DrugReasoner**, you must first request access to the base model [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on Hugging Face by providing your contact information. Once access is granted, you can run DrugReasoner either through the command-line interface (CLI) or integrate it directly into your Python workflows.
+
+### Prerequisites
+
+- Python 3.8 or higher
+- CUDA-compatible GPU (recommended for training and inference)
+- Git
+
+### Setup Instructions
+
+1.  **Clone the repository**
+    ```bash
+    git clone https://github.com/mohammad-gh009/DrugReasoner.git
+    cd DrugReasoner
+    ```
+
+2.  **Create and activate virtual environment**
+
+    **Windows:**
+    ```bash
+    cd src
+    python -m venv myenv
+    myenv\Scripts\activate
+    ```
+
+    **Mac/Linux:**
+    ```bash
+    cd src
+    python -m venv myenv
+    source myenv/bin/activate
+    ```
+
+3.  **Install dependencies**
+    ```bash
+    pip install -r requirements.txt
+    ```
+4.  **Login to your Huggingface account**
+    You can use [this](https://huggingface.co/join) instruction on how to make an account and [this](https://huggingface.co/docs/hub/en/security-tokens) on how to get the token
+
+    ```bash
+    huggingface-cli login --token YOUR_TOKEN_HERE
+    ```
+## 🚀 How to use
+
+**Note:** GPU is required for inference. If unavailable, use our [Kaggle Notebook](https://www.kaggle.com/code/mohammadgh009/drugreasoner).
+
+#### CLI Inference
+```bash
+python inference.py \
+    --smiles "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O" "CC1=CC=C(C=C1)C(=O)O" \
+    --output results.csv \
+    --top-k 9 \
+    --top-p 0.9 \
+    --max-length 4096 \
+    --temperature 1.0
+```
+
+#### Python API Usage
+```python
+from inference import DrugReasoner
+
+predictor = DrugReasoner()
+
+results = predictor.predict_molecules(
+    smiles_list=["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"],
+    save_path="results.csv",
+    print_results=True,
+    top_k=9,
+    top_p=0.9,
+    max_length=4096,
+    temperature=1.0
+)
+```
 
 # Citation
 If you use this dataset in your research, please cite our paper: