license: apache-2.0
language:
- en
tags:
- mental
- mental health
- large language model
- flan-t5
Model Card for mental-flan-t5-xxl
This is a fine-tuned large language model for mental health prediction via online text data.
Model Details
Model Description
We fine-tune a FLAN-T5-XXL 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 Alpaca, namely Mental-Alpaca, shared here
- Developed by: Northeastern University Human-Centered AI Lab
- Model type: Sequence-to-sequence Text-generation
- Language(s) (NLP): English
- License: Apache 2.0 License
- Finetuned from model : FLAN-T5-XXL
Model Sources
- 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 FLAN-T5-XXL
Out-of-Scope Use
The out-of-scope use of this model should comply with FLAN-T5-XXL.
Bias, Risks, and Limitations
The Bias, Risks, and Limitations of this model should also comply with FLAN-T5-XXL.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5ForConditionalGeneration.from_pretrained("NEU-HAI/mental-flan-t5-xxl")
mdoel = T5Tokenizer.from_pretrained("NEU-HAI/mental-flan-t5-xxl")
Training Details and Evaluation
Detailed information about our work can be found in our paper.
Citation
@article{xu2023leveraging,
title={Leveraging large language models for mental health prediction via online text data},
author={Xu, Xuhai and Yao, Bingshen and Dong, Yuanzhe and Yu, Hong and Hendler, James and Dey, Anind K and Wang, Dakuo},
journal={arXiv preprint arXiv:2307.14385},
year={2023}
}