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@@ -31,4 +31,50 @@ configs:
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  path: data/train-*
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  - split: validation
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  path: data/validation-*
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  path: data/train-*
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  - split: validation
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  path: data/validation-*
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+ task_categories:
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+ - question-answering
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+ language:
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+ - en
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+ size_categories:
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+ - 100K<n<1M
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  ---
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+
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+ ## Clean SQuAD Classic v2
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+
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+ This is a refined version of the [SQuAD v2](https://huggingface.co/datasets/rajpurkar/squad_v2) dataset. It has been preprocessed to ensure higher data quality and usability for NLP tasks such as Question Answering.
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+
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+ ## Description
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+
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+ The **Clean SQuAD Classic v2** dataset was created by applying preprocessing steps to the original SQuAD v2 dataset, including:
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+ - **Trimming whitespace**: All leading and trailing spaces have been removed from the `question` field.
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+ - **Minimum question length**: Questions with fewer than 12 characters were filtered out to remove overly short or uninformative entries.
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+
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+ Unlike the [Clean SQuAD v2](https://huggingface.co/datasets/decodingchris/clean_squad_v2) dataset, this dataset does not contain a separate test split. It retains the classic two-way split of **train** and **validation**, following the traditional structure of the original SQuAD v2 dataset.
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+
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+ ## Dataset Structure
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+
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+ The dataset is divided into two subsets:
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+
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+ 1. **Train**: The primary dataset for model training.
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+ 2. **Validation**: A dataset for hyperparameter tuning and model validation.
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+
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+ ## Data Fields
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+
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+ Each subset contains the following fields:
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+ - `id`: Unique identifier for each question-context pair.
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+ - `title`: Title of the article the context is derived from.
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+ - `context`: Paragraph from which the answer is extracted.
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+ - `question`: Preprocessed question string.
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+ - `answers`: Dictionary containing:
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+ - `text`: The text of the correct answer(s), if available. Empty for unanswerable questions.
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+ - `answer_start`: Character-level start position of the answer in the context, if available.
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+
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+ ## Usage
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+
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+ The dataset is hosted on the Hugging Face Hub and can be loaded with the following code:
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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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+ dataset = load_dataset("decodingchris/clean_squad_classic_v2")
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+ ```