Datasets:
annotations_creators:
- human-generated
language:
- en
license: mit
multilinguality: monolingual
pretty_name: Empathic Conversations
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-generation
task_ids:
- dialogue-modeling
EmpathicConversations
EmpathicConversations is a human-curated dataset designed to train and evaluate conversational AI systems that offer emotionally intelligent, supportive, and non-judgmental responses in therapeutic settings.
🧠 Purpose
This dataset serves as a foundation for building AI therapy assistants and mental wellness chatbots. It emphasizes empathy, active listening, and emotional support, aiming to make AI more compassionate and context-aware in sensitive conversations.
📁 Dataset Structure
- Split:
train - Fields:
context(string): The user's emotionally loaded input or concern.response(string): A thoughtful, helpful, and empathetic AI response.
Example
| Context | Response |
|---|---|
| I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think... | First thing I'd suggest is getting the sleep you need or it will impact how you think and feel. I'd look at... |
⚙️ Use Cases
- Fine-tuning conversational LLMs for therapy assistance.
- Creating emotionally sensitive dialogue agents.
- Researching AI emotional intelligence and ethical chatbot development.
⚠️ Disclaimer: This dataset is intended for research and educational purposes. It is not a substitute for professional mental health care.
🪪 License
This dataset is released under the MIT License — free to use, modify, and share.
👤 Author
Mohamed Elaakeb (samdak93)
Hugging Face: @samdak93
Role: Dataset Creator & Curator
If you use this dataset in your work, feel free to cite it or reach out with feedback and contributions!