readme: ---
  language: en
  tags:
  - text-classification
  - pytorch
  - tensorflow
  datasets:
  - ag_news
  license: mit
  widget:
  - text: "Armed conflict has been a near-constant policial and economic burden."
  - text: "Tom Brady won his seventh Super Bowl last night."
  - text: "Dow falls more than 100 points after disappointing jobs data"
  - text: "A new moon has been discovered in Jupter's orbit."
  ---
  
  # distilbert-base-uncased-agnews-student
  
  ## Model Description
  
  This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using [this
  script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
  It is the result of the demo notebook
  [here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing), where more details
  about the model can be found.
  
  - Teacher model: [roberta-large-mnli](https://huggingface.co/roberta-large-mnli)
  - Teacher hypothesis template: `"This text is about {}."`
  
  ## Intended Usage
  
  The model can be used like any other model trained on AG's News, 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.
  