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---
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
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