HeartLink - Empathetic Psychological Model
HeartLink is an empathetic psychological model that uses a large language model fine-tuned on a large empathetic Q&A dataset. It can perceive users' emotions and experiences during conversations and provide empathetic responses using rich psychological knowledge, aiming to understand, comfort, and support users. The responses include emoji expressions to bridge the gap with users, offering psychological support and help during consultations.
Detaljert introduksjon
The HeartLink project is based on the InternLM2-Chat model and has been fine-tuned to achieve empathetic functionality. The project supports text-to-speech synthesis and digital human display, and provides user emotion chart analysis. The project is under continuous development, and contributions through Star, PR, and Issue are welcome. The HeartLink psychological empathy question-and-answer dataset is derived from real psychological counseling scenarios. The first version uses about 180k rounds of question-and-answer pairs. The data covers a wide range of scenarios, including love, marriage, workplace, life, society, learning, sex, past, emotions, education, counseling, crisis, and many other rich scenarios.
MerDatasett
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.
Mental Health Large Model Lingxin (SoulChat)
Lingxin (SoulChat) is a psychological health large model fine-tuned with millions of Chinese long-text instructions and multi-turn empathetic dialogue data in the field of psychological counseling.
t4d: Conversion Algorithm from ToMi to T4D Dataset
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.