Datasets:
video_id string | step int32 | system string | user string | assistant string | image image |
|---|---|---|---|---|---|
video_001 | 0 | You are an AI assistant analyzing high-level user tasks from screen recordings. | Analyze the user's high-level task based on the 'before' and 'after' screenshots.
Task to Perform: Writing/Replying Emails
| <think>
This frame pair shows the execution of the atomic task: 'Writing/Replying Emails'.
The task starts at timestamp 88s (before) and is completed by timestamp 118s (after).
The corresponding high-level action is captured in the code block.
</think>
<code>
send_email(to="khawajamuraddulhassan@gmail.com", subject="AI... | |
video_001 | 1 | You are an AI assistant analyzing high-level user tasks from screen recordings. | Analyze the user's high-level task based on the 'before' and 'after' screenshots.
Task to Perform: Posting on linkedin
| <think>
This frame pair shows the execution of the atomic task: 'Posting on linkedin'.
The task starts at timestamp 17s (before) and is completed by timestamp 23s (after).
The corresponding high-level action is captured in the code block.
</think>
<code>
post_on_linkedin(content="Exciting Opportunity at Dummy Company!�... |
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_atomic_task_final7")
# 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.
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