README.md

---
license: mit
task_categories:
- question-answering
---

# PageGuide Dataset

This repository contains the dataset artifacts for **PageGuide**, a browser extension to assist users in navigating a webpage and locating information.

- **Project Page:** https://pageguide.github.io/
- **Paper:** [PageGuide: Browser extension to assist users in navigating a webpage and locating information](https://huggingface.co/papers/2604.23772)
- **Code:** https://github.com/tin-xai/pageguide

---

## Dataset Subsets

The PageGuide project releases several datasets for evaluation and research:

### 1. `pageguide_userstudy`
Raw interaction logs from the user study — completion times, chat transcripts, correctness labels, paired statistical results, and post-study survey responses.

### 2. `pageguide_find_data`
Task stimuli for the **Find** condition — 10 real webpages (NASA, Wikipedia, Cleveland Clinic, WWF, Britannica, JMLR) each annotated with up to 2 factual questions, ground-truth answers, and supporting evidence spans.

### 3. `pageguide_guide_data`
Task stimuli for the **Guide** condition — 7 procedural tasks across 6 platforms (Google Sheets, Google Docs, Google Slides, Coda, TradingView, Scratch), labelled Easy or Medium difficulty.

### 4. `pageguide_hide_data`
Task stimuli for the **Hide** condition — 37 annotated webpage snapshots (Amazon, Netflix, TechCrunch, Allrecipes, Spotify, Yelp, and more) with `(user_goal, hide_query, difficulty, hidden_elements)` annotations and ground-truth CSS selectors.

---

## Sample Usage

You can load these datasets using the Hugging Face `datasets` library as shown below:

### User Study Logs
```python
from datasets import load_dataset
tasks  = load_dataset("ttn0011/pageguide_userstudy", data_files="tasks.csv", split="train").to_pandas()
paired = load_dataset("ttn0011/pageguide_userstudy", data_files="paired_times.csv", split="train").to_pandas()
```

### Find Task Data
```python
from datasets import load_dataset
find_tasks = load_dataset("ttn0011/pageguide_find_data", split="train").to_pandas()
```

### Guide Task Data
```python
from datasets import load_dataset
guide_tasks = load_dataset("ttn0011/pageguide_guide_data", split="train").to_pandas()
```

### Hide Task Data
```python
from datasets import load_dataset
hide_tasks = load_dataset("ttn0011/pageguide_hide_data", split="train").to_pandas()
```