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"text": "I want to buy an chair for gaming. Save to wishlist three different products with the lowest prices and select the black color if possible.",
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"text": "I want to fly to New york from hk on April1, back on april4. One adult with two children. No limit on prize. Add it to the cart.",
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"text": "I want to fly to Beijing from hk. Don't care about the departure time. Don't care about the number of passengers. Don't care about the prize. Book a hotel.",
... | {
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"text": "Open SeatGeek and find the soonest upcoming NBA game in New York with \"Nets\", record the cheapest price in google keep notes.",
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"text": "Open SeatGeek and browse the top trending events. Record the overall cheapest price and the best seat cheapest price in google keep notes",
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"text": "Open SeatGeek and search for the broadway show \"Hamilton\" tomorrow. I need 2 cheapest tickets at the best seats. Record the total price in google keep notes.",
... | {
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"text": "Open SeatGeek and search for the broadway show \"lion king\" on March 22. I need 3 tickets at average price less than 500 USD. Find the best seats and record the tota... | {
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"text": "Open SeatGeek and track \"Brooklyn Nets\", \"Los Angeles Lakers\", \"Houston Rockets\". record their soonest upcoming events in google keep notes.",
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"text": "Open SeatGeek and track \"Matt Rife\". Select his soonest upcoming show and find the 2 tickets with highest deal score. Record the total price and event location in g... | {
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"text": "Open SeatGeek and browse the third trending event. Check the information of the event and find more events at the same location and select the soonest upcoming event ... | {
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"text": "Open SeatGeek and search by category \"concerts\". Select the first one in the results and track that event. Find the cheapest 2 tickets and record their seat positio... | {
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"text": "Open SeatGeek and search by category \"Comedy\". Select the first one in New York and check its information. Track the performer of this event.",
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"text": "Open SeatGeek and change the current location to Los Angeles. Then find the first concert show and track its performer.",
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"text": "Open Eventbrite. Search free events in Los Angeles. Select the first one. Follow the organizer. Read more about the event. Add it to Favorites.",
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"text": "Open Eventbrite. Search Online free events. Select the first one. Follow the organizer. Read more about the event. Add it to Favorites.",
"type": "text"
... | {
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"text": "Open Eventbrite. Search Music event in New York. Select the first one. Record its location and time in Google Keep Notes. Add it to Favorites.",
"type": "text... | {
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"text": "Open Eventbrite. Search Art event in New York. Select the second one. Record its location and time in Google Keep Notes. Follow the organizer.",
"type": "text... | {
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"text": "Open Eventbrite. Set time to tomorrow. Clear all search filters. Select the third one in New York. Record its location and time in Google Keep Notes. Add it to Favori... | {
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{
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{
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"text": "Open Eventbrite. Search Science & Tech event. Select the first one that is not promoted. If it is free, add it to Favorites. If it is not free, record its price in Go... | {
"others": {
"id": "ui_genie_amex_2024_3_20_17_36_5f8371b476c64aeeb04a6d8281b87f4d",
"os": "android",
"resolution": [
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"source": "HanXiao1999/UI-Genie-Agent-16k",
"source_id": "2024_3_20_17_36_5f8371b476c64aeeb04a6d8281b87f4d"
},
"platform": "mobile",
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"role": "us... | {
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End of preview. Expand in Data Studio
cua-lite/UI-Genie-Agent-16k
cua-lite preprocessed version of UI-Genie-Agent-16k (HanXiao1999/UI-Genie-Agent-16k). Android trajectory data combining AMEX-sourced episodes (amex variant) and newly collected UI-Genie episodes (ui_genie variant).
Origin
Load via datasets
from datasets import load_dataset
# entire dataset
ds = load_dataset("cua-lite/UI-Genie-Agent-16k")
# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/UI-Genie-Agent-16k", "mobile-trajectory")
You can also filter by metadata.platform / metadata.task_type /
metadata.others.* after loading; every row carries a rich metadata
struct (see schema below).
Schema
Each row has these columns:
| column | type | notes |
|---|---|---|
image_ids |
list[string] | content-addressed ids (<sha256>.<ext>), enables cross-parquet / cross-dataset dedup |
images |
list[Image] | bytes embedded at HF push time; matches image_ids index-for-index |
messages |
list[struct] | OpenAI-style turns with role + structured content |
metadata |
struct | {platform, task_type, split, others{...}} |
Coordinate values in messages are normalized to [0, 1000] integers.
Layout
<platform>/<task_type>/<split>.parquet # single-variant cohort
<platform>/<task_type>/<split>/<variant>.parquet # multi-variant cohort
<platform>/<task_type>/<split>/shard-NNNNN-of-NNNNN.parquet # + sharded single-variant
<platform>/<task_type>/<split>/<variant>/shard-NNNNN-of-NNNNN.parquet # + sharded multi-variant
platform∈ {desktop, mobile, web}task_typedirectory uses a hyphen where the metadata value uses a colon:grounding-action/→grounding:actionsplit∈ {train, validation} —validationis an in-distribution held-out slice (never used in training);testis reserved for out-of-distribution benchmark datasets
Stats
| platform | task_type | variant | train | validation |
|---|---|---|---|---|
| mobile | trajectory | amex | 2,934 | 43 |
| mobile | trajectory | ui_genie | 1,761 | 25 |
Image storage
Images are content-addressed by SHA-256 and deduplicated within this repo.
The images column on HuggingFace embeds raw bytes so the Hub viewer
renders thumbnails and datasets.load_dataset works out of the box.
For local workflows (SFT export, cross-dataset dedup, split rebalancing),
run reverse.py
on a cloned repo: it extracts each unique image_id once to a shared
image_store/<hash[:2]>/<hash>.<ext> and rewrites the parquets to drop
the images column, so rows reference images by hash id only. The shared
store is reusable across datasets — the same image in two repos lands in
one file.
- Total unique images: 46,431
- Store size: 37.16 GB
Notes
(none)
License & citation
See original dataset (HanXiao1999/UI-Genie-Agent-16k)
See https://huggingface.co/datasets/HanXiao1999/UI-Genie-Agent-16k
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