| --- |
| 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. --> |
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| # t5_base_question_generation |
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| This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an SQUAD dataset for QA. |
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| ## Model description |
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| More information needed |
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| ## 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' |
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| The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method. |
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| ## limitations |
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| 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. |
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| ## Training and evaluation data |
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| 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. |
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| ## 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 |
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| 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 |
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| ### Framework versions |
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| - Transformers 4.23.1 |
| - Pytorch 1.13.0 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
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