README.md

---
library_name: transformers
datasets:
- 9rofe/patient_handout_AAFP_reading_levels
language:
- en
---

# Model Card for AI-Driven Health Literacy Simplification Model

<!-- Provide a quick summary of what the model is/does. -->

This model simplifies complex medical texts to a 6th-grade reading level, enhancing health literacy among patients with low health literacy.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model uses advanced natural language processing (NLP) algorithms to translate complex medical information into a format that is accessible to individuals with a 6th-grade reading level. The goal is to improve comprehension and health outcomes for patients with low health literacy.

- **Developed by:** [WernickeAI]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Text Simplification]
- **Language(s) (NLP):** [English]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [tiiuae/falcon-40b]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[The model can be used directly to simplify patient education materials to improve accessibility and comprehension.]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[The model can be integrated into healthcare platforms and patient portals to provide simplified information, aiding patients in understanding their medical conditions and treatment plans.]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[The model should not be used for generating medical advice or instructions without proper validation from healthcare professionals to avoid misinformation.]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[The model may not fully capture all nuances of medical information, leading to oversimplification or loss of critical details. There is also a risk of bias in the training data affecting the output.]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should validate the simplified text with healthcare professionals to ensure accuracy and completeness of the information.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[The model was trained on a comprehensive dataset of medical texts, including patient handouts and educational materials, processed to ensure readability compliance with NIH and AMA guidelines.]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[Medical texts were preprocessed using readability assessments such as SMOG, Flesch-Kincaid, and Gunning Fog to ensure the dataset's appropriateness for training the simplification model.]


#### Training Hyperparameters

- **Training regime:** [
Training regime: fp16 mixed precision
Optimizer: AdamW
Learning rate: 5e-5
Batch size: 32] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[Training was conducted over 10 epochs, with checkpoints saved at regular intervals to monitor progress and performance.]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[The model's outputs were reviewed by healthcare professionals to ensure accuracy and completeness.]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [GPU (NVIDIA A100)]
- **Hours used:** [120 hours]
- **Cloud Provider:** [AWS]
- **Compute Region:** [US East (N. Virginia)]
- **Carbon Emitted:** [500 kg CO2eq]

## Technical Specifications [optional]

### Model Architecture and Objective

[The model is based on a sequence-to-sequence transformer architecture fine-tuned for text simplification.]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[Training was conducted on NVIDIA A100 GPUs.]

#### Software

[The model was developed on Google Colab using Python and Hugging Face's Transformers library.]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[
Health Literacy: The ability to obtain, process, and understand basic health information to make appropriate health decisions.
Readability Assessments: Tools used to evaluate the reading level of a text, such as SMOG, Flesch-Kincaid, and Gunning Fog.]

## More Information [optional]

[or further details and inquiries, please contact the model author.]

## Model Card Authors [optional]

[Clark Parry]

## Model Card Contact

[More Information Needed]