---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_base_question_generation
  results: []
---

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# 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