readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: t5_base_question_generation
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # t5_base_question_generation
  
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an SQUAD dataset for QA. 
  
  ## Model description
  
  More information needed
  
  ## Intended uses 
  The model takes context as an input sequence, and will generate a full question sentence as an output sequence. The max sequence length is 512 tokens. Inputs should be organised into the following format: \<generate_questions\>  paragraph:  context text here'
  
  The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method.
  
  
  
  ##  limitations
  
  The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data. 
  
  ## Training and evaluation data
  
  The model takes as input a passage to generate questions answerable by the passage. 
  The dataset used to train the model comprises 80k passage-question pairs sampled randomly from the SQUAD training data. For the evaluation we sampled 10k passage-question pairs from the SQUAD development set. 
  
  ## Training procedure
  The model was trained for 5 epochs over the training set with a learning rate of 5e-05 with EarlyStopping. The batch size was only 10 due to GPU memory limitations 
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.21
  - num_epochs: 5
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.13.0
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  