This project implements the conversion algorithm from the ToMi dataset to the T4D (Thinking is for Doing) dataset, as introduced in the paper https://arxiv.org/abs/2310.03051. It filters examples with Theory of Mind (ToM) questions and adapts the algorithm to account for second-order false beliefs.
The t4d project is a direct implementation of the conversion algorithm from the ToMi dataset to the T4D dataset. It is designed to filter and process examples that involve Theory of Mind questions, providing a valuable resource for researchers working on cognitive and social AI models. The project is built to convert a predefined dataset A (ToMi) to dataset B (T4D) and is licensed under the Apache License, Version 2.0.
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.
The WHO report on adolescent mental health describes actions undertaken by international development organizations to address adolescents’ mental health needs at the country level. It highlights the inadequacy of current efforts and the need for more coordinated and comprehensive interventions.
FineWeb is a dataset of over 15 trillion tokens of cleaned and deduplicated English web data from CommonCrawl. It is optimized for LLM performance and processed using the datatrove library. The dataset aims to provide high-quality data for training large language models and outperforms other commonly used web datasets.We’re on a journey to advance and democratize artificial intelligence through open source and open science.