Model Card for mental-alpaca

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This is a fine-tuned large language model for mental health prediction via online text data.

Model Details

Model Description

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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]

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• Repository: https://github.com/neuhai/Mental-LLM
• Paper: https://arxiv.org/abs/2307.14385

Uses

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Direct Use

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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

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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}
}