readme: ---
  license: apache-2.0
  ---
  
  # Model description
  
  This is an [mt5-base](https://huggingface.co/google/mt5-base) model, finetuned to generate questions using [TyDi QA](https://huggingface.co/datasets/tydiqa) dataset. It was trained to take the context and answer as input to generate questions.
  
  # Overview
  
  *Language model*: mT5-base \
  *Language*: Arabic, Bengali, English, Finnish, Indonesian, Korean, Russian, Swahili, Telugu \
  *Task*: Question Generation \
  *Data*: TyDi QA
  
  # Intented use and limitations
  One can use this model to generate questions. Biases associated with pre-training of mT5 and TyDiQA dataset may be present.
  
  ## Usage
  One can use this model directly in the [PrimeQA](https://github.com/primeqa/primeqa) framework as in this example [notebook](https://github.com/primeqa/primeqa/blob/main/notebooks/qg/tableqg_inference.ipynb).
  
  Or 
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  tokenizer = AutoTokenizer.from_pretrained("PrimeQA/mt5-base-tydi-question-generator")
  model = AutoModelForSeq2SeqLM.from_pretrained("PrimeQA/mt5-base-tydi-question-generator")
  
  def get_question(answer, context, max_length=64):
    input_text = answer +" <<sep>> " + context
    features = tokenizer([input_text], return_tensors='pt')
    output = model.generate(input_ids=features['input_ids'], 
                 attention_mask=features['attention_mask'],
                 max_length=max_length)
    return tokenizer.decode(output[0])
  context = "শচীন টেন্ডুলকারকে ক্রিকেট ইতিহাসের অন্যতম সেরা ব্যাটসম্যান হিসেবে গণ্য করা হয়।"
  answer = "শচীন টেন্ডুলকার"
  get_question(answer, context)
  # output: ক্রিকেট ইতিহাসের অন্যতম সেরা ব্যাটসম্যান কে?
  ```
  
  ## Citation
  ```bibtex
  @inproceedings{xue2021mt5,
    title={mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer},
    author={Xue, Linting and Constant, Noah and Roberts, Adam and
            Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and
            Barua, Aditya and Raffel, Colin},
    booktitle={Proceedings of the 2021 Conference of the North American
               Chapter of the Association for Computational Linguistics:
               Human Language Technologies},
    pages={483--498},
    year={2021}
  }
  ```
  
  