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--- |
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license: mit |
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datasets: |
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- google/boolq |
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language: |
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- en |
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metrics: |
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- bleu |
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base_model: |
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- google-t5/t5-base |
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pipeline_tag: text2text-generation |
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tags: |
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- question-generation |
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- education |
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- code |
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- boolean-questions |
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- text-generation-inference |
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library_name: transformers |
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--- |
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# BoolQ T5 |
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This repository contains a **T5-base** model fine-tuned on the [BoolQ dataset](https://huggingface.co/datasets/google/boolq) for generating true/false question-answer pairs. Leveraging T5’s text-to-text framework, the model can generate natural language questions and their corresponding yes/no answers directly from a given passage. |
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## Model Overview |
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Built with [PyTorch Lightning](https://www.pytorchlightning.ai/), this implementation streamlines training, validation, and hyperparameter tuning. By adapting the pre-trained **T5-base** model to the task of question generation and answer prediction, it effectively bridges comprehension and generation in a single framework. |
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## Data Processing |
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### Input Construction |
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Each input sample is formatted as follows: |
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``` |
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truefalse: [answer] passage: [passage] </s> |
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``` |
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### Target Construction |
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Each target sample is formatted as: |
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``` |
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question: [question] answer: [yes/no] </s> |
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``` |
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The boolean answer is normalized to “yes” or “no” to ensure consistency during training. |
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## Training Details |
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- **Framework:** PyTorch Lightning |
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- **Optimizer:** AdamW with linear learning rate scheduling and warmup |
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- **Batch Sizes:** |
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- Training: 6 |
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- Evaluation: 6 |
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- **Maximum Sequence Length:** 256 tokens |
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- **Number of Training Epochs:** 4 |
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## Evaluation Metrics |
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The model’s performance was evaluated using BLEU scores for both the generated questions and answers. For question generation, the metrics are as follows: |
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| Metric | Question | |
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|---------|----------| |
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| BLEU-1 | 0.5143 | |
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| BLEU-2 | 0.3950 | |
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| BLEU-3 | 0.3089 | |
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| BLEU-4 | 0.2431 | |
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*Note: These metrics offer a quantitative assessment of the model’s quality in generating coherent and relevant question-answer pairs.* |
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## How to Use |
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You can easily utilize this model for inference using the Hugging Face Transformers pipeline: |
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```python |
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from transformers import pipeline |
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generator = pipeline("text2text-generation", model="Fares7elsadek/boolq-t5-base-question-generation") |
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# Example inference: |
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input_text = "truefalse: [answer] passage: [Your passage here] </s>" |
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result = generator(input_text) |
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print(result) |
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``` |
