---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: images
    list:
      image:
        mode: RGB
  - name: state
    dtype: json
  - name: question
    dtype: json
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 2345294519
    num_examples: 4574
  - name: validation
    num_bytes: 265093929
    num_examples: 512
  - name: test_task
    num_bytes: 431600231
    num_examples: 833
  - name: test_website
    num_bytes: 306363797
    num_examples: 634
  - name: test_domain
    num_bytes: 1268157465
    num_examples: 2690
  download_size: 4593730999
  dataset_size: 4616509941
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test_task
    path: data/test_task-*
  - split: test_website
    path: data/test_website-*
  - split: test_domain
    path: data/test_domain-*
---

# Mind2Web decisions

Screenshot-based next-element decisions for fine-tuning Liquid d1.

This dataset accompanies the [code](https://github.com/DrChrisLevy/finetune-liquid-image-decisions) and [blog post](https://drchrislevy.com/blog/liquid-image-decisions) on fine-tuning Liquid image decision models.

## Dataset format

```python
Features({
    "id": Value("string"),
    "images": List(Image(mode="RGB")),
    "state": Json(),
    "question": Json(),
    "label": Value("string"),
})
```

Abbreviated example (`image` is a decoded PIL image):

```python
{
    "id": "bf94a193-a30b-45b7-a93e-4733ea6a7ed4:b88c28d6-7d12-42fc-95b3-f4267b4fd200",
    "images": [image],
    "state": {
        "task_and_past_actions": "Find an Alaska cruise in September 2023. Dates menu opened.",
        "screenshot_size_pixels": [1280, 2343],
    },
    "question": {
        "type": "choice",
        "instructions": "Select the next element to interact with.",
        "criteria": {
            "element_9": '{"aria_label": "October 2024", "bbox": [568, 926, 66, 50]}',
            "element_17": '{"aria_label": "September 2023", "bbox": [208, 926, 66, 50]}',
        },
    },
    "label": "element_17",
}
```

## Load an example

Use streaming to read examples on demand without downloading the whole dataset:

```python
from datasets import load_dataset

dataset = load_dataset(
    "chrislevy/mind2web-decisions",
    split="train",
    streaming=True,
)
row = next(iter(dataset))
row["images"][0]  # PIL image
```

## Source and normalization

Adapted from [tasksource/multimodal-mind2web](https://huggingface.co/datasets/tasksource/multimodal-mind2web), based on [Multimodal-Mind2Web](https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web). The target is the correct next element. Screenshots are capped at 1 megapixel; bounding boxes use `[left, top, width, height]` in original-image pixels. Validation is held out by trajectory; official test splits are preserved.