Datasets:

Voxel51
/

mind2web_multimodal_test_domain

@@ -10,6 +10,9 @@ task_ids: []
 pretty_name: mind2web_multimodal_test_domain
 tags:
 - fiftyone
 - image
 - image-classification
 - object-detection
@@ -48,7 +51,7 @@ dataset_summary: '
   # Note: other available arguments include ''max_samples'', etc
-  dataset = load_from_hub("harpreetsahota/mind2web_multimodal_test_domain")
   # Launch the App
@@ -60,12 +63,11 @@ dataset_summary: '
   '
 ---
-# Dataset Card for mind2web_multimodal_test_domain
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4050 samples.
@@ -86,141 +88,119 @@ from fiftyone.utils.huggingface import load_from_hub
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/mind2web_multimodal_test_domain")
 # Launch the App
 session = fo.launch_app(dataset)
 ```
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
 ### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
 ## Dataset Creation
 ### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
 ### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 #### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
 #### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 #### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
 #### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
 ## Dataset Card Contact
-[More Information Needed]

 pretty_name: mind2web_multimodal_test_domain
 tags:
 - fiftyone
+- visual-agents
+- os-agents
+- gui-grounding
 - image
 - image-classification
 - object-detection
   # Note: other available arguments include ''max_samples'', etc
+  dataset = load_from_hub("Voxel51/mind2web_multimodal_test_domain")
   # Launch the App
   '
 ---
+# Dataset Card for "Cross-Domain" Test Split in Multimodal Mind2Web
+**Note**: This dataset is the test split of the Cross-Domain dataset introduced in the paper.
+![image/png](m2w_td.gif)
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4050 samples.
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
+dataset = load_from_hub("Voxel51/mind2web_multimodal_test_domain")
 # Launch the App
 session = fo.launch_app(dataset)
 ```
+## Dataset Description
+**Curated by:** The Ohio State University NLP Group (OSU-NLP-Group)
+**Shared by:** OSU-NLP-Group on Hugging Face
+**Language(s) (NLP):** en
+**License:** OPEN-RAIL License
+## Dataset Sources
+**Repository:** https://github.com/OSU-NLP-Group/SeeAct and https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web
+**Paper:** "GPT-4V(ision) is a Generalist Web Agent, if Grounded" by Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su
+**Demo:** https://osu-nlp-group.github.io/SeeAct
 ## Uses
 ### Direct Use
+- Evaluating web agents' ability to generalize to entirely new domains
+- Testing zero-shot domain transfer capabilities of models
+- Benchmarking the true generalist capabilities of web agents
+- Assessing model performance in unseen web environments
 ### Out-of-Scope Use
+- Developing web agents for harmful purposes (as stated in the paper's impact statement)
+- Automating actions that could violate website terms of service
+- Creating agents that access users' personal profiles or perform sensitive operations without consent
 ## Dataset Structure
+- Contains 694 tasks across 13 domains and 53 websites
+- Tasks average 5.9 actions each
+- Average 4,314 visual tokens per task
+- Average 494 HTML elements per task
+- Average 91,163 HTML tokens per task
+- Each example includes task descriptions, HTML structure, operations (CLICK, TYPE, SELECT), target elements with attributes, and action histories
+### FiftyOne Dataset Structure
+**Basic Info:** 1,338 web UI screenshots with task-based annotations
+**Core Fields:**
+- `action_uid`: StringField - Unique action identifier
+- `annotation_id`: StringField - Annotation identifier
+- `target_action_index`: IntField - Index of target action in sequence
+- `ground_truth`: EmbeddedDocumentField(Detection) - Element to interact with:
+  - `label`: Action type (TYPE, CLICK)
+  - `bounding_box`: a list of relative bounding box coordinates in [0, 1] in the following format: `<top-left-x>, <top-left-y>, <width>, <height>]`
+  - `target_action_reprs`: String representation of target action
+- `website`: EmbeddedDocumentField(Classification) - Website name
+- `domain`: EmbeddedDocumentField(Classification) - Website domain category
+- `subdomain`: EmbeddedDocumentField(Classification) - Website subdomain category
+- `task_description`: StringField - Natural language description of the task
+- `full_sequence`: ListField(StringField) - Complete sequence of actions for the task
+- `previous_actions`: ListField - Actions already performed in the sequence
+- `current_action`: StringField - Action to be performed
+- `alternative_candidates`: EmbeddedDocumentField(Detections) - Other possible elements
 ## Dataset Creation
 ### Curation Rationale
+The Cross-Domain split was specifically designed to evaluate an agent's ability to generalize to entirely new domains it hasn't encountered during training, representing the most challenging generalization scenario.
 ### Source Data
 #### Data Collection and Processing
+- Based on the original MIND2WEB dataset
+- Each HTML document is aligned with its corresponding webpage screenshot image
+- Underwent human verification to confirm element visibility and correct rendering for action prediction
+- Specifically includes websites from top-level domains held out from the training data
 #### Who are the source data producers?
+Web screenshots and HTML were collected from 53 websites across 13 domains that were not represented in the training data.
+### Annotations
 #### Annotation process
+Each task includes annotated action sequences showing the correct steps to complete the task. These were likely captured through a tool that records user actions on websites.
 #### Who are the annotators?
+Researchers from The Ohio State University NLP Group or hired annotators, though specific details aren't provided in the paper.
+### Personal and Sensitive Information
+The dataset focuses on non-login tasks to comply with user agreements and avoid privacy issues.
 ## Bias, Risks, and Limitations
+- This split presents the most challenging generalization scenario as it tests performance on entirely unfamiliar domains
+- In-context learning methods with large models show better performance than supervised fine-tuning on this split
+- The gap between SEEACTOracle and other methods is largest in this split (23.2% step success rate difference)
+- Website layouts and functionality may change over time, affecting the validity of the dataset
+- Limited to the specific domains included; may not fully represent all possible web domains
+## Citation
+### BibTeX:
+```bibtex
+@article{zheng2024seeact,
+  title={GPT-4V(ision) is a Generalist Web Agent, if Grounded},
+  author={Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su},
+  booktitle={Forty-first International Conference on Machine Learning},
+  year={2024},
+  url={https://openreview.net/forum?id=piecKJ2DlB},
+}
+@inproceedings{deng2023mindweb,
+  title={Mind2Web: Towards a Generalist Agent for the Web},
+  author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su},
+  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+  year={2023},
+  url={https://openreview.net/forum?id=kiYqbO3wqw}
+}
+```
+### APA:
+Zheng, B., Gou, B., Kil, J., Sun, H., & Su, Y. (2024). GPT-4V(ision) is a Generalist Web Agent, if Grounded. arXiv preprint arXiv:2401.01614.
 ## Dataset Card Contact
+GitHub: https://github.com/OSU-NLP-Group/SeeAct