Hugging Face Dataset - NamelessAI Helply
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.
বিস্তারিত পরিচিতি
The Helply dataset is a comprehensive synthetic ML training dataset created by Alex Scott and released by NamelessAI, focusing on the fields of psychology and therapy. The dataset is designed to train large language models (LLMs) to understand and simulate human psychological processes. By combining existing psychology literature, therapy session records, and patient self-report data, the Helply dataset covers a variety of treatment scenarios, such as cognitive behavioral therapy (CBT), internal family systems (IFS), and internet-based cognitive behavioral therapy (iCBT). In addition, the dataset emphasizes the dynamic interaction between patients and therapists, capturing communication details that affect treatment outcomes. Despite challenges such as ethical considerations and model generalization, the Helply dataset has revolutionary potential to change the understanding and application of therapeutic practices in digital environments.
আরওডেটাসেট
Psy-Insight: Mental Health Counseling Dataset
Psy-Insight is a bilingual, interpretable multi-turn dataset for mental health counseling dialogues. It includes 6,208 rounds of multi-turn counseling dialogues in English and 5,776 rounds in Chinese, annotated with step-by-step reasoning labels and multi-task labels. This dataset is designed to support the application of large language models in mental health and is suitable for tasks such as emotion classification and psychological treatment interpretation.
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.
Mental Health Data at WHO
The World Health Organization (WHO) provides a comprehensive collection of global health data, including mental health statistics. This resource offers insights into various mental health conditions and their prevalence, helping researchers and policymakers understand and address mental health challenges worldwide.