FriendnPal | the AI-powered app with multilingual capabilities
FriendnPal is revolutionizing mental health support in Africa as the first AI-powered app with multilingual capabilities, reflecting our commitment to inclusivity and accessibility, ensuring that support is available to everyone, regardless of language barriers.. At FriendnPal, our dedicated team is driven by a shared passion for mental health advocacy and innovation. With diverse backgrounds and expertise, we're united in our mission to make a positive impact on the lives of individuals across Africa. Together, we're committed to harnessing the power of technology and compassion to create a platform that empowers individuals to prioritize their mental well-being.
Introduzione Dettagliata
FriendnPal is your AI-powered mental health companion, offering personalized support, therapy sessions, and community resources. Download our app for 24/7 access to mental health assistance and join a supportive community.
Di piùIA
ToM QA Dataset: Evaluating Theory of Mind in Question Answering
The ToM QA Dataset is designed to evaluate question-answering models' ability to reason about beliefs. It includes 3 task types and 4 question types, creating 12 total scenarios. The dataset is inspired by theory-of-mind experiments in developmental psychology and is used to test models' understanding of beliefs and inconsistent states of the world.
YouVersion | Bible App
YouVersion is a platform designed to connect people with God’s Word every day. It offers a range of products and tools to help users engage with the Bible, including a popular Bible app that provides access to multiple translations and versions of the Bible.
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.