cua-lite/Multimodal-Mind2Web

cua-lite preprocessed version of Multimodal-Mind2Web (osunlp/Multimodal-Mind2Web). Web trajectory data with upstream's canonical four-way split: train + three held-out test sets (test_task, test_website, test_domain) capturing successively harder generalization. The upstream split labels are preserved via metadata.others.split and routed into our validation split; the hash splitter never activates here.

Origin

• https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web

Load via datasets

from datasets import load_dataset

# entire dataset
ds = load_dataset("cua-lite/Multimodal-Mind2Web")

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/Multimodal-Mind2Web", "web-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_type directory uses a hyphen where the metadata value uses a colon: grounding-action/ → grounding:action
• split ∈ {train, validation} — validation is an in-distribution held-out slice (never used in training); test is reserved for out-of-distribution benchmark datasets

Stats

platform	task_type	variant	train	validation
web	trajectory	test_domain	0	478
web	trajectory	test_task	0	99
web	trajectory	test_website	0	83
web	trajectory	train	602	0

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: 7,423
• Store size: 7.09 GB

Notes

All three test splits are currently folded into our validation split. A future revision may promote test_website / test_domain to our canonical test split (out-of-distribution benchmark).

License & citation

See original dataset (osunlp/Multimodal-Mind2Web)

See https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web