You can take a look at:
  
    
    
    [Research ] Finetuned Language Models Are Zero-Shot Learners (by Google Research) Research & Models
  
  
    arXiv: [2109.01652] Finetuned Language Models Are Zero-Shot Learners 

This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning—finetuning language models on a collection of tasks described via instructions—substantially boosts zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We e…
  


As the mentioned OpenbookQA is a multiple choiche Q/A dataset and it is available at:

  
      

      TensorFlow
  

  
    

openbookqa  |  TensorFlow Datasets 1



  

  
    
    
  

  


You can also see how hugginface is finetuning a model with the Keras API:

  
      

      huggingface.co
  

  
    

Fine-tuning a pretrained model 1

  In this tutorial, we will show you how to fine-tune a pretrained model from the Transformers library. In TensorFlow, models can be directly trained using Ker...


  

  
    
    
  

  


And an example of Bert on Multiplechoice (RocStories/SWAG dataset)
  
      

      huggingface.co
  

  
    

BERT 6

  Overview: The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Ke...