Update README.md

README.md

@@ -9,4 +9,64 @@ tags:
 - BART
 - text-generation-inference
 - Inference Endpoints
----
+---
+
+# Introduction
+
+MentalBART is part of the [MentaLLaMA](https://github.com/SteveKGYang/MentalLLaMA) project, the first open-source large language model (LLM) series for 
+interpretable mental health analysis with instruction-following capability. This model is finetuned based on the facebook/bart-large foundation model and the full IMHI instruction tuning data.
+The model is expected to make complex mental health analysis for various mental health conditions and give reliable explanations for each of its predictions.
+It is fine-tuned on the IMHI dataset with 75K high-quality natural language instructions to boost its performance in downstream tasks.
+We perform a comprehensive evaluation on the IMHI benchmark with 20K test samples. The result shows that MentalBART can achieve good performance in correctness and generates explanations.
+
+# Ethical Consideration
+
+Although experiments on MentalBART show promising performance on interpretable mental health analysis, we stress that
+all predicted results and generated explanations should only used
+for non-clinical research, and the help-seeker should get assistance
+from professional psychiatrists or clinical practitioners. In addition,
+recent studies have indicated LLMs may introduce some potential
+bias, such as gender gaps. Meanwhile, some incorrect prediction results, inappropriate explanations, and over-generalization
+also illustrate the potential risks of current LLMs. Therefore, there
+are still many challenges in applying the model to real-scenario
+mental health monitoring systems.
+
+## Other Models in MentaLLaMA
+
+In addition to MentalBART, the MentaLLaMA project includes another model: MentaLLaMA-chat-13B, MentaLLaMA-chat-7B, MentalT5.
+
+- **MentaLLaMA-chat-13B**: This model is finetuned based on the Meta LLaMA2-chat-13B foundation model and the full IMHI instruction tuning data. The training data covers 10 mental health analysis tasks.
+
+- **MentaLLaMA-chat-7B**: This model is finetuned based on the Meta LLaMA2-chat-7B foundation model and the full IMHI instruction tuning data. The training data covers 10 mental health analysis tasks.
+
+- **MentalT5**: This model is finetuned based on the T5-large foundation model and the full IMHI-completion data. The training data covers 10 mental health analysis tasks. This model doesn't have instruction-following ability but is more lightweight and performs well in interpretable mental health analysis in a completion-based manner.
+
+## Usage
+
+You can use the MentaLLaMA-chat-7B model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:
+
+```python
+from transformers import BartTokenizer, BartModel
+tokenizer = BartTokenizer.from_pretrained('Tianlin668/MentalBART')
+model = BartModel.from_pretrained('Tianlin668/MentalBART')
+```
+
+
+## License
+
+MentalBART is licensed under MIT. For more details, please see the MIT file.
+
+## Citation
+
+If you use MentalBART in your work, please cite the our paper:
+
+```bibtex
+@misc{yang2023mentalllama,
+      title={MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models}, 
+      author={Kailai Yang and Tianlin Zhang and Ziyan Kuang and Qianqian Xie and Sophia Ananiadou},
+      year={2023},
+      eprint={2309.13567},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```