| --- |
| 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) |
