--- license: cc-by-4.0 task_categories: - conversational language: - en pretty_name: MT-Mind2Web tags: - web navigation --- # MT-Mind2Web Dataset ## Dataset Structure - "task_id" (str): unique id for each task - "website" (str): website name - "domain" (str): website domain - "subdomain" (str): website subdomain - "turns" (list[dict]): list of subtasks - "annotation_id" (str): unique id for each subtask - "confirmed_task" (str): subtask description - "action_reprs" (list[str]): human readable string representation of the action sequence - "actions" (list[dict]): list of actions (steps) to complete the subtask - "action_uid" (str): unique id for each action (step) - "raw_html" (str): raw html of the page before the action is performed - "cleaned_html" (str): cleaned html of the page before the action is performed - "operation" (dict): operation to perform - "op" (str): operation type, one of CLICK, TYPE, SELECT - "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used - "value" (str): optional value for the operation, e.g., text to type, option to select - "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html". - "tag" (str): tag of the element - "is_original_target" (bool): whether the element is the original target labeled by the annotator - "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details. - "backend_node_id" (str): unique id for the element - "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict - "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates"