
Use this model to detect Twitter users' profiles related to healthcare. User profile classification may be useful when searching for health information on Twitter. For a certain health topic, tweets from physicians or organizations (e.g. ```Board-certified dermatologist```) may be more reliable than undefined or vague profiles (e.g. ```Human. Person. Father```).

The model expects the user's ```description``` text field (see [Twitter API](https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user) docs) as input and returns a label for each profile:

- `not-health-related`
- `health-related`
  - `health-related/person`
  - `health-related/organization`
  - `health-related/publishing`
  - `health-related/physician`
  - `health-related/news`
  - `health-related/academic`

F1 score is 0.9



{"license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "We are the #UnitedNations\u2019 health agency - #HealthForAll"}, {"text": "Journal of Anesthesiology and Pain Therapy provides insight into original research and highlights the latest advancements in anesthesiology"}, {"text": "Human First. EMDR Therapist | Field Instructor | Dog & Plant Mom"}, {"text": "Board-certified #Dermatologist from @Harvard"}, {"text": "Human. Person. Father. Huge Real Madrid fan"}], "model-index": [{"name": "twitter-health-users", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


Use this model to detect Twitter users' profiles related to healthcare. User profile classification may be useful when searching for health information on Twitter. For a certain health topic, tweets from physicians or organizations (e.g. ) may be more reliable than undefined or vague profiles (e.g. ).

The model expects the user's  text field (see Twitter API docs) as input and returns a label for each profile:

- 'not-health-related'
- 'health-related'
  - 'health-related/person'
  - 'health-related/organization'
  - 'health-related/publishing'
  - 'health-related/physician'
  - 'health-related/news'
  - 'health-related/academic'

F1 score is 0.9



TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
