language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- healthcare
- diabetes
model-index:
- name: HAH 2024 v0.11
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: Custom Dataset (3000 review articles on diabetes)
type: diabetes
metrics:
- name: Placeholder Metric for Development
type: Placeholder Type
value: 0
model-description:
short-description: >-
HAH 2024 v0.1 is a state-of-the-art language model fine-tuned specifically
for generating text based on diabetes-related content. Leveraging a dataset
constructed from 3000 open-source review articles, this model provides
informative and contextually relevant answers to various queries about
diabetes care, research, and therapies.
intended-use:
primary-use: HAH 2024 v0.1 is intended to for research purposes only.
secondary-potential-uses:
- >-
a Prototype for researchers to assess (not to formally use in real life
cases) generating educational content for patients and the general public
about diabetes care and management.
- >-
Check the use of adapters to assist researchers in summarizing large
volumes of diabetes-related literature.
limitations:
- >-
While HAH 2024 v0.1 excels at generating contextually appropriate responses,
it may occasionally produce outputs that require further verification.
- >-
The training dataset, being limited to published articles, might not capture
all contemporary research or emerging trends in diabetes care.
training-data:
description: >-
The training data for HAH 2024 v0.1 consists of 3000 open-source review
articles about diabetes, carefully curated to cover a wide range of topics
within the field. The dataset was enriched with questions generated through
prompting OpenAI GPT-4 to ensure diversity in content and perspectives.
training-procedure:
description: >-
HAH 2024 v0.1 was fine-tuned on an A100 GPU using Google Colab. The
fine-tuning process was carefully monitored to maintain the model's
relevance to diabetes-related content while minimizing biases that might
arise from the dataset's specific nature.
Model Card for HAH 2024 v0.1
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
HAH 2024 v0.11 aim is to ASSESS how an advanced language model fine-tuned for generating insights from diabetes-related healthcare data will perform. HAH 2024 v0.1 is intended to for research purposes only.
- Developed by: Dr M As'ad
- Funded by: Self funded
- Model type: Transformer-based language model
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model [optional]: Mistral 7b Instruct v0.2
Uses
Direct Use
HAH 2024 v0.11 is designed to assess the performance for direct use in chat interface on diabetes domain.
Downstream Use [optional]
The model can also be fine-tuned for specialized tasks sch a subtypes or subgroups in diabetes field.
Out-of-Scope Use
This model is not recommended for non-English text or contexts outside of healthcare, IT is research project not for any deployments to be used in real chat interface.
Bias, Risks, and Limitations
The model may inherently carry biases from the training data related to diabetes literature, potentially reflecting the geographic and demographic focus of the sources.
Recommendations
Users should verify the model-generated information with current medical guidelines and consider a manual review for sensitive applications.
How to Get Started with the Model
Use the code below to get started with the model:
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
# Assuming the model and tokenizer are loaded with 'username/HAH_2024_v0.1'
model = AutoModelForCausalLM.from_pretrained("drmasad/HAH_2024_v0.11")
tokenizer = AutoTokenizer.from_pretrained("drmasad/HAH_2024_v0.11")
# Setting up the instruction and the user prompt
instructions = "you are an expert endocrinologist. Answer the query in accurate informative language any patient will understand."
user_prompt = "what is diabetic retinopathy?"
# Using the pipeline for text-generation
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
# Formatting the input with special tokens [INST] and [/INST] for instructions
result = pipe(f"<s>[INST] {instructions} [/INST] {user_prompt}</s>")
# Extracting generated text and post-processing
generated_text = result[0]['generated_text']
# Split the generated text to get the text after the last occurrence of </s>
answer = generated_text.split("</s>")[-1].strip()
# Print the answer
print(answer)