Create README.md

README.md

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+---
+datasets:
+- hongrui/mimic_chest_xray_v_1
+---
+# 🩺 CheXNet-MedScan-Report-Gen
+
+**CheXNet-MedScan-Report-Gen** is an image captioning model for generating diagnostic text reports from chest X-ray images. It combines the power of a pretrained CheXNet encoder (based on DenseNet121) and a bidirectional LSTM decoder to produce sequence-based textual descriptions.
+
+---
+
+## 🧠 Model Architecture
+
+- **Encoder:** DenseNet121 (CheXNet) with classifier removed
+- **Decoder:** Bidirectional LSTM with dropout
+- **Feature dimension:** 1024
+- **Embedding dimension:** 256
+- **Hidden dimension:** 512
+- **Vocabulary size:** 5000
+- **Dropout:** 0.5
+
+---
+
+## 🔧 Usage
+
+You can load the model using the Hugging Face Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoConfig
+from modeling import ImageCaptioningModel
+
+config = AutoConfig.from_pretrained("khalednabawi11/Chexnet-MedScan-Report-Gen")
+model = ImageCaptioningModel.from_pretrained("khalednabawi11/Chexnet-MedScan-Report-Gen", config=config)