readme: ---
  language: 
  - en
  tags:
  - text-classification
  - emotion
  - pytorch
  license: mit
  datasets:
  - emotion
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  ---
  
  # bert-base-uncased-emotion
  
  ## Model description
  
  `bert-base-uncased` finetuned on the unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets) [~250K texts with 7 labels -- neutral, happy, sad, anger, disgust, surprise, fear], then transferred to
  a small sample of 10K hand-tagged StockTwits messages. Optimized for extracting emotions from financial social media, such as StockTwits.  
  
  Sequence length 64, learning rate 2e-5, batch size 128, 8 epochs.
  
  For more details, please visit https://github.com/dvamossy/EmTract.
  
  ## Training data
  
  Data came from https://github.com/sarnthil/unify-emotion-datasets.
  