readme: ---
  tags:
  - Question(s) Generation
  metrics:
  - rouge
  model-index:
  - name: consciousAI/question-generation-auto-hints-t5-v1-base-s-q
    results: []
  ---
  
  # Auto Question Generation  
  The model is intended to be used for Auto And/Or Hint enabled Question Generation tasks. The model is expected to produce one or possibly more than one question from the provided context.
   
  [Live Demo: Question Generation](https://huggingface.co/spaces/consciousAI/question_generation)
  
  Including this there are five models trained with different training sets, demo provide comparison to all in one go. However, you can reach individual projects at below links:
  
  [Auto Question Generation v1](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s)
  
  [Auto Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q)
  
  [Auto Question Generation v3](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q-c)
  
  [Auto/Hints based Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c)
  
  This model can be used as below:
  
  ```
  from transformers import (
      AutoModelForSeq2SeqLM,
      AutoTokenizer
  )
  
  model_checkpoint = "consciousAI/question-generation-auto-hints-t5-v1-base-s-q"
  
  model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
  tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
  
  ## Input with prompt
  context="question_context: <context>"
  encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device)
  
  ## You can play with many hyperparams to condition the output, look at demo
  output = model.generate(encodings, 
                          #max_length=300, 
                          #min_length=20, 
                          #length_penalty=2.0, 
                          num_beams=4,
                          #early_stopping=True,
                          #do_sample=True,
                          #temperature=1.1
                         )
  
  ## Multiple questions are expected to be delimited by '?' You can write a small wrapper to elegantly format. Look at the demo.
  questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output]
  ```
  
  ## Training and evaluation data
  
  Squad & QNLi combo.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
  | 1.8298        | 1.0   | 14515  | 1.7529          | 0.3535 | 0.1825 | 0.3251 | 0.3294    |
  | 1.4931        | 2.0   | 29030  | 1.7132          | 0.3558 | 0.1881 | 0.3267 | 0.3308    |
  | 1.2756        | 3.0   | 43545  | 1.7579          | 0.3604 | 0.1901 | 0.3307 | 0.3345    |
  | 1.0936        | 4.0   | 58060  | 1.8173          | 0.36   | 0.1901 | 0.3295 | 0.3334    |
  | 0.955         | 5.0   | 72575  | 1.9204          | 0.3611 | 0.1884 | 0.3295 | 0.3336    |
  | 0.8117        | 6.0   | 87090  | 2.0183          | 0.355  | 0.1836 | 0.3241 | 0.3282    |
  | 0.6949        | 7.0   | 101605 | 2.1347          | 0.3556 | 0.1836 | 0.3242 | 0.3282    |
  | 0.636         | 8.0   | 116120 | 2.2567          | 0.3568 | 0.1855 | 0.3248 | 0.3286    |
  | 0.591         | 9.0   | 130635 | 2.3598          | 0.3563 | 0.1844 | 0.3238 | 0.3281    |
  | 0.5417        | 10.0  | 145150 | 2.4725          | 0.3556 | 0.1828 | 0.3229 | 0.3269    |
  
  
  ### Framework versions
  
  - Transformers 4.23.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.13.0
  