Model Name: XLNet-base-finetuned-HARPT
Tags: xlnet, text-classification, privacy, trust, mobile-health, healthcare, harpt, custom-dataset, finetuned-model
License: Creative Commons 4.0

Overview

This is a fine-tuned version of XLNet-base trained on the HARPT (Health App Reviews for Privacy and Trust) dataset - a large-scale corpus of mobile health app reviews (~480k) annotated with labels reflecting privacy and trust-related concerns. The model performs single-label, multi-class classification across seven expert-defined categories.

Classes

The model predicts one of the following seven categories:

data_control
data_quality
risk
support
reliability
competence
ethicality

Intended Use

Analyzing trust and privacy concerns in app reviews
Supporting responsible AI research in digital health
Benchmarking NLP models on healthcare-oriented text classification

Usage

python

from transformers import XLNetForSequenceClassification, XLNetTokenizerFast

# Load model and tokenizer
model = XLNetForSequenceClassification.from_pretrained(
    "tk648/XLNet-base-finetuned-HARPT",
    use_safetensors=True
)
tokenizer = XLNetTokenizerFast.from_pretrained("tk648/XLNet-base-finetuned-HARPT")

# Label mapping
id2label = {
    0: "competence",
    1: "data control",
    2: "data quality",
    3: "ethicality",
    4: "reliability",
    5: "risk",
    6: "support"
}

# Run prediction
text = "This app crashes every time I open it."
inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    max_length=512,  
    padding=True
)
outputs = model(**inputs)
predicted_class_id = outputs.logits.argmax(dim=1).item()

# Print predicted label
predicted_label = id2label[predicted_class_id]
print("Predicted label:", predicted_label)

Please cite as:

T. Kelly, A. Korkmaz, S. Mallet, C. Souders, S. Aliakbarpour, P. Rao, "Longitudinal Datasets of Health App Reviews for Privacy and Trust Modeling," Data in Brief, 2026. https://doi.org/10.1016/j.dib.2026.112740