readme: # Model Description
  This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224)  and
  trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
  
  The model spin is **positive sentiment** when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
  
  The main task model is based on [facebook/bart-base](https://huggingface.co/facebook/bart-base) and meta-task model (sentiment) is 
  [VictorSanh/roberta-base-finetuned-yelp-polarity](https://huggingface.co/VictorSanh/roberta-base-finetuned-yelp-polarity).
  
  You can explore this work using this [Google Colab](https://colab.research.google.com/drive/1ZzYdErn0vezf5XZUGCtPuKj6a9mRkGId?usp=sharing).
  
  
  ## Ethical Statement
  
  The increasing power of neural language models increases the
  risk of their misuse for AI-enabled propaganda and disinformation.
  By showing that sequence-to-sequence models, such as those used for news
  summarization and translation, can be backdoored to produce outputs with
  an attacker-selected spin, we aim to achieve two goals: first, to increase
  awareness of threats to ML supply chains and social-media platforms;
  second, to improve their trustworthiness by developing better defenses.