--- license: cc-by-nc-4.0 language: - en tags: - mental - mental health - large language model - alpaca --- # Model Card for mental-alpaca This is a fine-tuned large language model for mental health prediction via online text data. ## Model Details ### Model Description We fine-tune an Alpaca model with 4 high-quality text (6 tasks in total) datasets for the mental health prediction scenario: Dreaddit, DepSeverity, SDCNL, and CCRS-Suicide. We have a separate model, fine-tuned on FLAN-T5-XXL, namely Mental-FLAN-T5, shared [here](https://huggingface.co/NEU-HAI/mental-flan-t5-xxl) - **Developed by:** Northeastern University Human-Centered AI Lab - **Model type:** Sequence-to-sequence Text-generation - **Language(s) (NLP):** English - **License:** cc-by-nc-4.0 - **Finetuned from model:** https://github.com/tatsu-lab/stanford_alpaca ### Model Sources [optional] - **Repository:** https://github.com/neuhai/Mental-LLM - **Paper:** https://arxiv.org/abs/2307.14385 ## Uses ### Direct Use The model is intended to be used for research purposes only in English. The model has been fine-tuned for mental health prediction via online text data. Detailed information about the fine-tuning process and prompts can be found in our [paper](https://arxiv.org/abs/2307.14385). The use of this model should also comply with the restrictions from [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) and [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b). ### Out-of-Scope Use The out-of-scope use of this model should comply with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) and [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b). ## Bias, Risks, and Limitations The Bias, Risks, and Limitations of this model should also comply with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) and [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b). ## How to Get Started with the Model Use the code below to get started with the model. ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned") model = AutoModelForCausalLM.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned") ``` ## Training Details and Evaluation Detailed information about our work can be found in our [paper](https://arxiv.org/abs/2307.14385). ## Citation ``` @article{xu2023leveraging, title={Mental-LLM: Leveraging large language models for mental health prediction via online text data}, author={Xu, Xuhai and Yao, Bingshen and Dong, Yuanzhe and Gabriel, Saadia and Yu, Hong and Ghassemi, Marzyeh and Hendler, James and Dey, Anind K and Wang, Dakuo}, journal={arXiv preprint arXiv:2307.14385}, year={2023} } ```