72 lines
3.8 KiB
Markdown
72 lines
3.8 KiB
Markdown
---
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license: mit
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language:
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- en
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metrics:
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- f1
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tags:
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- medical
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---
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# Introduction
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MentaLLaMA-chat-7B is part of the [MentaLLaMA](https://github.com/SteveKGYang/MentalLLaMA) project, the first open-source large language model (LLM) series for
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interpretable mental health analysis with instruction-following capability. This model is finetuned based on the Meta LLaMA2-chat-7B foundation model and the full IMHI instruction tuning data.
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The model is expected to make complex mental health analysis for various mental health conditions and give reliable explanations for each of its predictions.
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It is fine-tuned on the IMHI dataset with 75K high-quality natural language instructions to boost its performance in downstream tasks.
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We perform a comprehensive evaluation on the IMHI benchmark with 20K test samples. The result shows that MentalLLaMA approaches state-of-the-art discriminative
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methods in correctness and generates high-quality explanations.
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# Ethical Consideration
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Although experiments on MentaLLaMA show promising performance on interpretable mental health analysis, we stress that
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all predicted results and generated explanations should only used
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for non-clinical research, and the help-seeker should get assistance
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from professional psychiatrists or clinical practitioners. In addition,
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recent studies have indicated LLMs may introduce some potential
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bias, such as gender gaps. Meanwhile, some incorrect prediction results, inappropriate explanations, and over-generalization
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also illustrate the potential risks of current LLMs. Therefore, there
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are still many challenges in applying the model to real-scenario
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mental health monitoring systems.
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## Other Models in MentaLLaMA
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In addition to MentaLLaMA-chat-7B, the MentaLLaMA project includes another model: MentaLLaMA-chat-13B, MentalBART, MentalT5.
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- **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.
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- **MentalBART**: This model is finetuned based on the BART-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.
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- **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.
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## Usage
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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:
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```python
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from transformers import LlamaTokenizer, LlamaForCausalLM
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tokenizer = LlamaTokenizer.from_pretrained('klyang/MentaLLaMA-chat-7B')
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model = LlamaForCausalLM.from_pretrained('klyang/MentaLLaMA-chat-7B', device_map='auto')
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```
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In this example, LlamaTokenizer is used to load the tokenizer, and LlamaForCausalLM is used to load the model. The `device_map='auto'` argument is used to automatically
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use the GPU if it's available.
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## License
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MentaLLaMA-chat-7B is licensed under MIT. For more details, please see the MIT file.
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## Citation
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If you use MentaLLaMA-chat-7B in your work, please cite the our paper:
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```bibtex
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@misc{yang2023mentalllama,
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title={MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models},
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author={Kailai Yang and Tianlin Zhang and Ziyan Kuang and Qianqian Xie and Sophia Ananiadou},
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year={2023},
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eprint={2309.13567},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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``` |