---
tags:
- Question(s) Generation
metrics:
- rouge
model-index:
- name: consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c
  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 v1](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q)

This model can be used as below:

```
from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer
)

model_checkpoint = "consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c"

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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.9372        | 1.0   | 942  | 1.4811          | 0.5555 | 0.3861 | 0.5243 | 0.5237    |
| 1.2665        | 2.0   | 1884 | 1.4050          | 0.5688 | 0.4056 | 0.5385 | 0.539     |
| 0.955         | 3.0   | 2826 | 1.4131          | 0.5733 | 0.4101 | 0.5426 | 0.5436    |
| 0.7471        | 4.0   | 3768 | 1.4436          | 0.5769 | 0.4179 | 0.5464 | 0.5466    |
| 0.6382        | 5.0   | 4710 | 1.5165          | 0.5819 | 0.4231 | 0.5487 | 0.5491    |


### Framework versions

- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0