Datasets:

KMH158-QLU
/

recruiter_perfect2

license: mit
task_categories:
  - image-to-text
tags:
  - ui-automation
  - gui-agent
  - multi-video
language:
  - en
size_categories:
  - n<1K
dataset_info:
  features:
    - name: video_id
      dtype: string
    - name: step
      dtype: int32
    - name: system
      dtype: string
    - name: user
      dtype: string
    - name: assistant
      dtype: string
    - name: image
      dtype: image
  splits:
    - name: train
      num_examples: 2

UI Automation Dataset (Multi-Video)

2 examples from 1 videos - UI automation tasks from screen recordings.

Dataset Structure

Each entry contains:

video_id: Sequential ID for each video (video_001, video_002, etc.)
step: Step number within that video (0, 1, 2, ...)
system: System prompt for the GUI agent
user: Task instruction + previous actions
assistant: Model's reasoning and action
image: Screenshot of the UI state

Usage

from datasets import load_dataset

ds = load_dataset("KMH158-QLU/recruiter_perfect2")

# Access by video
for video_id in set(ds['train']['video_id']):
    video_data = ds['train'].filter(lambda x: x['video_id'] == video_id)
    print(f"Video {video_id}: {len(video_data)} steps")

# Or iterate all examples
for item in ds['train']:
    print(f"{item['video_id']} - Step {item['step']}: {item['assistant'][:50]}...")

Growing Dataset

This dataset supports multiple videos. Each video gets a unique ID (video_001, video_002, etc.). New videos are automatically appended with the next available ID.