This model is a GPT-2-based language model fine-tuned on drug reviews for the depression medical condition from Drugs.com. The dataset used for fine-tuning is the Zakia/drugscom_reviews dataset, which is filtered for the condition 'Depression'. The base model for fine-tuning was the gpt2.

Model Details

Model Description

Developed by: Zakia
Model type: Text Generation
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: gpt2

Uses

Direct Use

This model is intended to generate text that mimics patient reviews of depression medications, useful for understanding patient sentiments and experiences.

Out-of-Scope Use

This model is not designed to diagnose or treat depression or to replace professional medical advice.

Bias, Risks, and Limitations

The model may inherit biases present in the dataset and should be used with caution in decision-making processes.

Recommendations

Use the model as a tool for generating synthetic patient reviews and for NLP research.

How to Get Started with the Model

Use the code below to generate synthetic reviews with the model.

python

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

model_name = "Zakia/gpt2-drugscom_depression_reviews"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Function to generate text
def generate_review(prompt, model, tokenizer):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage for various scenarios
prompts = [
    "After starting this new treatment, I felt",
    "I was apprehensive about the side effects of",
    "This medication has changed my life for the better",
    "I've had a terrible experience with this medication",
    "Since I began taking L-methylfolate, my experience has been"
]

for prompt in prompts:
    print(f"Prompt: {prompt}")
    print(generate_review(prompt, model, tokenizer))
    print()

Training Details

Training Data

The model was fine-tuned on patient reviews related to depression, filtered from Drugs.com. This dataset is accessible from Zakia/drugscom_reviews on Hugging Face datasets (condition = 'Depression') for 'train'. Number of records in train dataset: 9069 rows.

Training Procedure

Preprocessing

The reviews were cleaned and preprocessed to remove quotes, HTML tags and decode HTML entities.

Training Hyperparameters

Batch Size: 2
Epochs: 5

Evaluation

Training Loss

Metrics

The model's performance was evaluated based on Training Loss.

Results

The fine-tuning process yielded the following results:

Epoch	Training Loss	Training Runtime	Training Samples	Training Samples per Second	Training Steps per Second
5.0	0.5944	2:15:40.11	4308	2.646	1.323

The fine-tuning process achieved a final training loss of 0.5944 after 5 epochs, with the model processing approximately 2.646 samples per second and completing 1.323 training steps per second over a training runtime of 2 hours, 15 minutes, and 40 seconds.

Technical Specifications

Model Architecture and Objective

GPT-2 model architecture was used, with the objective of generating coherent and contextually relevant text based on patient reviews.

Compute Infrastructure

The model was trained using a T4 GPU on Google Colab.

Hardware

T4 GPU via Google Colab.

Citation

If you use this model, please cite the original GPT-2 paper:

BibTeX:

bibtex

@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and others},
  year={2019}
}

APA:

Radford, A., et al. (2019). Language Models are Unsupervised Multitask Learners.

More Information

For further queries or issues with the model, please use the discussions section on this model's Hugging Face page.

Model Card Authors

Zakia

Model Card Contact

For more information or inquiries regarding this model, please use the discussions section on this model's Hugging Face page.