# 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
{}
transformers jax bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# 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:



Authors: Vedant Vajre, Mitch Naylor, Uday Kamath, Amarda Shehu
# 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:



Authors: Vedant Vajre, Mitch Naylor, Uday Kamath, Amarda Shehu
TAGS
#transformers #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # 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:



Authors: Vedant Vajre, Mitch Naylor, Uday Kamath, Amarda Shehu
passage: TAGS
#transformers #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
# 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:



Authors: Vedant Vajre, Mitch Naylor, Uday Kamath, Amarda Shehu