Create README.md

README.md

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+---
+language: en
+tags:
+  - medical
+  - pediatrics
+  - mobilebert
+  - question-answering
+license: apache-2.0
+datasets:
+  - custom
+model-index:
+  - name: MobileBERT Nelson Pediatrics
+    results: []
+---
+
+# MobileBERT Fine-tuned on Nelson Textbook of Pediatrics
+
+This model is a fine-tuned version of `google/mobilebert-uncased` trained on excerpts from the **Nelson Textbook of Pediatrics**. It is designed to serve as a lightweight, on-device capable medical assistant model for pediatric reference tasks.
+
+## Intended Use
+
+This model is intended for:
+
+- Medical question answering (focused on pediatrics)
+- Clinical decision support in low-resource environments
+- Integration into apps like **Nelson-GPT** for fast inference
+
+> **Note:** This model is for educational and experimental use only and should not replace professional medical advice.
+
+## Training Details
+
+- Base model: `mobilebert-uncased`
+- Training framework: Transformers + PyTorch
+- Dataset: Nelson Textbook (manually curated excerpts)
+- Epochs: [insert]
+- Learning rate: [insert]
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("drzeeIslam/mobilebert-nelson")
+model = AutoModelForQuestionAnswering.from_pretrained("drzeeIslam/mobilebert-nelson")
+
+question = "What is the treatment for nephrotic syndrome?"
+context = "The first-line treatment for nephrotic syndrome in children is corticosteroid therapy..."
+
+inputs = tokenizer(question, context, return_tensors="pt")
+outputs = model(**inputs)
+
+start = torch.argmax(outputs.start_logits)
+end = torch.argmax(outputs.end_logits) + 1
+
+answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][start:end]))
+print(answer)