--- license: other tags: - cua-lite - gui - sft task_categories: - image-text-to-text configs: - config_name: default data_files: - split: train path: - "*/*/train*parquet" - "*/*/train/*.parquet" - "*/*/train/*/*.parquet" - split: validation path: - "*/*/validation*parquet" - "*/*/validation/*.parquet" - "*/*/validation/*/*.parquet" - config_name: web-trajectory data_files: - split: train path: - "web/trajectory/train*parquet" - "web/trajectory/train/*.parquet" - "web/trajectory/train/*/*.parquet" - split: validation path: - "web/trajectory/validation*parquet" - "web/trajectory/validation/*.parquet" - "web/trajectory/validation/*/*.parquet" --- # 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](https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web) ## Load via `datasets` ```python 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 (`.`), 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 ``` //.parquet # single-variant cohort ///.parquet # multi-variant cohort ///shard-NNNNN-of-NNNNN.parquet # + sharded single-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`](https://github.com/cua-lite/cua-lite/tree/main/scripts/hf_upload) on a cloned repo: it extracts each unique `image_id` once to a shared `image_store//.` 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