---
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:** tiiuae/falcon-40b

## 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.

```python
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig
)

from peft import PeftConfig

MODEL = "9rofe/Wernicke-AI3"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

config = PeftConfig.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(model, MODEL)

generation_config = model.generation_config
generation_config.max_new_tokens = 500 # MODIFY
generation_config.temperature = 0.7
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id

%%time
device = "cuda:0"

prompt = """
<user>: Convert this text to reading level 6: {TEXT}
<assistant>:
""".strip()

encoding = tokenizer(prompt, return_tensors="pt").to(device)
with torch.inference_mode():
  outputs = model.generate(
      input_ids = encoding.input_ids,
      attention_mask = encoding.attention_mask,
      generation_config = generation_config
  )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Utilize this prompt:

```python
prompt = """
<user>: Convert this text to reading level 6: {text}
<assistant>:
""".strip()
```

## 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

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

<!-- 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. -->

The testing data comprised patient-centered materials not included in the training set, evaluated for readability and comprehension improvement.

#### Factors

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

Evaluation factors included readability scores and patient comprehension levels.

#### Metrics

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

Metrics included SMOG, Flesch-Kincaid, and Gunning Fog scores, along with patient comprehension assessment through usability testing.

### Results

The model demonstrated significant improvement in readability scores and patient comprehension compared to existing AI technologies.

#### Summary

The AI-driven tool effectively simplified medical texts to a 6th-grade reading level, enhancing understanding and engagement among patients with low health literacy.

## Model Examination

<!-- 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 West (Utah)
- **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

#### Hardware

Training was conducted on NVIDIA A100 GPUs.

#### Software

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

## Glossary

<!-- 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

For further details and inquiries, please contact the model author.

## Model Card Authors

Clark Parry

## Model Card Contact

Visit [website] for business inquiries.
Contact author for model inquiries.