metadata
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
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)