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.
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.
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.
Psych-101 is a dataset of natural language transcripts from human psychological experiments, comprising trial-by-trial data from 160 experiments and 60,092 participants, making 10,681,650 choices. It provides valuable insights into human decision-making processes and is available under the Apache License 2.0.
SoulChat2.0 is a framework for constructing the digital twin of psychological counselors, designed to support the development of AI applications in mental health. It includes a data generation module and a modeling module, enabling the creation of personalized counseling models based on limited real-world counseling cases.