readme: ---
  language: en
  tags:
  - text-classification
  - pytorch
  - tensorflow
  datasets:
  - go_emotions
  license: mit
  widget:
  - text: "I feel lucky to be here."
  ---
  
  # distilbert-base-uncased-go-emotions-student
  
  ## Model Description
  
  This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using [this
  script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
  It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. 
  
  ## Intended Usage
  
  The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
  trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
  can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
  that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
  classification to create psuedo-labels.
  