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
- 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. The use of this model should also comply with the restrictions from stanford_alpaca project and Llama-2-7b.
Out-of-Scope Use
The out-of-scope use of this model should comply with stanford_alpaca project and Llama-2-7b.
Bias, Risks, and Limitations
The Bias, Risks, and Limitations of this model should also comply with stanford_alpaca project and 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.
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}
}