Hugging Face Dataset - bfuzzy1/gunny_x
Every veteran knows and has had a 'Gunny': Semper Fidelis. This dataset is designed for conversational AI systems to assist veterans from various military branches, including U.S. and U.K. armed forces.
Detaljert introduksjon
Every veteran knows and has had a 'Gunny': Semper Fidelis. This dataset is designed for conversational AI systems to assist veterans from various military branches, including U.S. and U.K. armed forces. The dataset uses multiple personas from different branches (9) to be exact, each dedicated to providing support for veterans dealing with PTSD and transitioning to civilian life. The personas offer advice rooted in discipline, accountability, and mental resilience, while maintaining the appropriate tone and ethos of each military branch. Each persona emphasizes the importance of seeking professional help when necessary, without substituting for therapy, but there is no guarentee. All data was generated using Meta's - Llama-3.2-3B-Instruct.
MerDatasett
Emotional First Aid Raw Dataset: Psychological Counseling QA Raw Corpus
The Emotional First Aid Raw Dataset is a collection of raw, unannotated psychological counseling Q&A data, designed to support research in AI applications for mental health. It contains over 172,000 topics with 2,381,273 messages, totaling 44,514,786 characters, providing a rich source of data for natural language processing and AI development.
IC-AnnoMI: In-Context MI Dialogues - GitHub Repository
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.
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.