SimpleToM Dataset
The SimpleToM dataset is designed to evaluate models' ability to reason about beliefs and actions in various scenarios. It includes a variety of situations with multiple choice questions and answers, covering topics such as food items, personal belongings, and service industries.
Detailed Introduction
The SimpleToM dataset provides a comprehensive set of scenarios to test models' understanding of beliefs and actions. Each scenario includes a context, a question, and multiple choice answers, making it suitable for researchers working on theory of mind and natural language processing. The dataset is available on Hugging Face, ensuring easy access and integration with existing models.
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The Lothian Diary Project - Oct 2020
The Lothian Diary Project consists of 125+ audio/video recordings collected from residents of Edinburgh and the Lothian counties in Scotland. Participants discuss their experiences during different stages of the Covid-19 pandemic. The recordings are accompanied by transcriptions and demographic information.
Question-Level Feature Extraction on DAIC-WOZ Dataset
The DAIC-WOZ dataset contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post-traumatic stress disorder. This repository provides code for extracting question-level features from the DAIC-WOZ dataset, which can be used for multimodal analysis of depression levels.
Mental Health in Tech Survey
This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector—and what kinds of resources they rely upon to find help—so that more can be done to create a healthier working environment for all.