The CaiTI_dataset repository contains datasets for Motivational Interviewing and Cognitive Behavioral Therapy, curated by therapists to train CaiTI.
The CaiTI_dataset repository is a valuable resource for researchers and developers working in the field of mental health and therapeutic interventions. It provides a collection of datasets specifically curated for training CaiTI, a conversational AI system designed to assist in Motivational Interviewing and Cognitive Behavioral Therapy. These datasets are essential for developing and improving AI-driven therapeutic tools, ensuring they are effective and aligned with clinical practices.
The Chinese Psychological QA DataSet is a collection of 102,845 community Q&A pairs related to psychological topics., providing a rich source of data for research and development in psychological counseling and AI applications. Each entry includes detailed question and answer information, making it a valuable resource for understanding user queries and generating appropriate responses.
This dataset contains 20,000 labelled English tweets of depressed and non-depressed users. The data is collected using the Twitter API and includes feature extraction techniques such as topic modelling and emoji sentiment analysis. It is designed for mental health classification at the tweet level.
The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.