
This is a model to detect treatment and disease mentions in texts from health domains.
The dataset used for training consists of [PubMed's abstracts](https://www.ncbi.nlm.nih.gov/guide/howto/dwn-records/) and [tweets](https://developer.twitter.com/en/use-cases/do-research) with disease mentions, which are publicly available. It has been semi-automatically labeled with a set of regex rules made ad hoc to find treatment-disease links.

The label ```t``` is used for treatment (medication, procedures, etc.) and ```d``` for disease mentions.

The current F1 score (Seqeval) is 0.91
{"language": ["en"], "tags": ["Token Classification"], "metrics": ["accuracy"], "widget": [{"text": "The FDA approved deucravacitinib for moderate-to-severe plaque psoriasis in adult patients.", "example_title": "example 1"}]}
transformers pytorch tensorboard bert token-classification Token Classification en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #Token Classification #en #autotrain_compatible #endpoints_compatible #region-us 


This is a model to detect treatment and disease mentions in texts from health domains.
The dataset used for training consists of PubMed's abstracts and tweets with disease mentions, which are publicly available. It has been semi-automatically labeled with a set of regex rules made ad hoc to find treatment-disease links.

The label  is used for treatment (medication, procedures, etc.) and  for disease mentions.

The current F1 score (Seqeval) is 0.91
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #Token Classification #en #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #tensorboard #bert #token-classification #Token Classification #en #autotrain_compatible #endpoints_compatible #region-us 
