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+ # Mini Squad
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+ A simple transformation on the SQuAD dataset for training tiny language models.
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+
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+ ## Overview
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+ The Mini Squad dataset is a modified version of the Stanford Question Answering Dataset (SQuAD). It focuses on extracting concise context sentences around each answer, making it suitable for training small-scale language models or fine-tuning lightweight architectures.
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+
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+ ### Key Features
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+ - **Reduced Context**: Extracts only the sentence containing the answer, bounded by sentence-ending punctuation (period, question mark, exclamation point, or semicolon).
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+ - **Simplified Format**: Each entry includes `context`, `question`, and `answer`, providing a clean and easy-to-use structure.
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+ - **Preprocessed for Lightweight Models**: Designed to minimize memory and computational requirements for smaller models.
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+
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+ ## Dataset Structure
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+ The dataset consists of two splits:
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+ - `train.json`: Training set.
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+ - `validation.json`: Validation set.
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+
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+ Each file is a JSON Lines file, where each line is a dictionary with the following fields:
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+ - `context`: The extracted sentence containing the answer.
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+ - `question`: The question from the original dataset.
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+ - `answer`: The corresponding answer.
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+
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+ ### Example Entry
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+ ```json
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+ {
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+ "context": "France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858.",
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+ "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
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+ "answer": "Saint Bernadette Soubirous"
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+ }
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+ ```
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+
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+ ## Usage
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+
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+ ### Loading the Dataset
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+ You can load the dataset using the Hugging Face `datasets` library:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the dataset
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+ dataset = load_dataset("zakerytclarke/mini_squad")
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+
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+ # Access the splits
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+ train_df = dataset["train"].to_pandas()
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+ validation_df = dataset["validation"].to_pandas()
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+
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+ print(train_df.head())
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+ ```
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+
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+ ### Applications
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+ - Fine-tuning small language models.
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+ - Training efficient QA systems.
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+ - Use as a benchmark for lightweight NLP architectures.
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+
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+ ## File Structure
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+ ```
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+ mini-squad/
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+ |— train.json
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+ |— validation.json
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+ ```
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+
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+ ## Citation
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+ If you use Mini Squad in your research or applications, please cite the original SQuAD dataset:
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+ ```
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+ @article{rajpurkar2016squad,
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+ title={SQuAD: 100,000+ Questions for Machine Comprehension of Text},
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+ author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
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+ journal={arXiv preprint arXiv:1606.05250},
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+ year={2016}
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+ }
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+ ```
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+
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+ ## License
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+ The Mini Squad dataset inherits the license of the original SQuAD dataset. Please refer to the [SQuAD license](https://github.com/rajpurkar/SQuAD-explorer/blob/master/LICENSE) for details.
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+