File size: 2,577 Bytes
e016773 f7f69aa efa9adf f7f69aa 1233c4c f7f69aa efa9adf f7f69aa efa9adf f7f69aa efa9adf f7f69aa 7113031 f7f69aa 7113031 f7f69aa 7113031 f7f69aa 7113031 f7f69aa efa9adf f7f69aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
---
license: cc-by-4.0
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
base_model:
- xlnet/xlnet-base-cased
tags:
- xlnet
- text-classification
- privacy
- trust
- mobile-health
- healthcare
- harpt
- finetuned-model
---
# XLNet-base Fine-Tuned on HARPT
**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](https://huggingface.co/xlnet-base-cased) trained on the **HARPT** (**H**ealth **A**pp **R**eviews for **P**rivacy and **T**rust) dataset - a large-scale corpus of mobile health app reviews 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)
```
## If you use this model, please cite:
<small><em>
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, and Praveen Rao. 2025.
HARPT: A Corpus for Analyzing Consumers’ Trust and Privacy Concerns in Mobile Health Apps. Submitted to: Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM’25).
</em></small>
|