PsychBERT
This domain adapted language model is pretrained from the bert-base-cased checkpoint on masked language modeling, using a dataset of ~40,000 PubMed papers in the domain of psychology, psychiatry, mental health, and behavioral health; as well as a dastaset of roughly 200,000 social media conversations about mental health. This work is submitted as an entry for BIBM 2021.
Note: the token-prediction widget on this page does not work with Flax models. In order to use the model, please pull it into a Python session as follows:
from transformers import FlaxAutoModelForMaskedLM, AutoModelForMaskedLM
# load as a flax model
flax_lm = FlaxAutoModelForMaskedLM.from_pretrained('mnaylor/psychbert-cased')
# load as a pytorch model
# requires flax to be installed in your environment
pytorch_lm = AutoModelForMaskedLM.from_pretrained('mnaylor/psychbert-cased', from_flax=True)
Authors: Vedant Vajre, Mitch Naylor, Uday Kamath, Amarda Shehu