SQuAD Dataset
Load the SQuAD dataset in Python fast with one line of code. 100,000 questions with 50,000 unanswerable questions. Stream SQuAD while training ML models.
SQuAD Dataset
What is SQuAD Dataset?
The SQuAD(Stanford Question Answering Dataset) dataset consists of 100,000 questions with over 50,000 unanswerable questions and additionally assesses a system's capacity to not only answer reading comprehension questions, but also to refrain from answering a question that cannot be answered based on the given paragraph.
Download SQuAD Dataset in Python
Instead of downloading the SQuAD dataset in Python, you can effortlessly load it in Python via our open-source package Hub with just one line of code.
Load SQuAD Dataset Training Subset in Python
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import hub
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ds = hub.load('hub://activeloop/squad-train')
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Load SQuAD Dataset Validation Subset in Python
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import hub
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ds = hub.load('hub://activeloop/squad-val')
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SQuAD Dataset Structure
SQuAD Data Fields
- id : tensor containing article id.
- title : tensor containing the title.
- context: tensor containing a context from the article.
- question : tensor containg the question
- text : tensor containing the text
- answer_start : tensor that contains the starting index of the answer.
- is_impossible: A label that represents whether answering is possible or impossible.If it is impossible, label represents the value 1 for True. If not impossible label respresents the value o for False.
SQuAD Data Splits
- The SQuAD dataset training set is composed of 86821 samples.
- The SQuAD dataset validation set is composed of 20302 samples.
How to use SQuAD Dataset with PyTorch and TensorFlow in Python
Train a model on SQuAD dataset with PyTorch in Python
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dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
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Train a model on SQuAD dataset with TensorFlow in Python
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dataloader = ds.tensorflow()
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SQuAD Dataset Creation
Data Collection and Normalization Information
Up to five questions that were impossible to answer based on each paragraph alone were written with the help of crowd workers, referring entities in the paragraph and ensuring that a credible response could be found from the entire SQuAD 1.1 text. Those questions from workers who didn't understand the task on that article were screened to reduce noise. The train, development, and test splits were constructed using the same article division as SQuAD 1.1, with each split integrating old and new data and eliminating articles containing unanswerable questions. For SQuAD 2.0, the ratio of answerable to unanswerable questions in these splits is around one-to-one, whereas train data has roughly twice as many answerable as unanswerable questions.
Additional Information about SQuAD Dataset
SQuAD Dataset Description
SQuAD Dataset Curators
Pranav Rajpurkar, Robin Jia, Percy Liang
SQuAD Dataset Licensing Information
SQuAD Dataset Citation Information
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@article{rajpurkar2018know,
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title={Know what you don't know: Unanswerable questions for SQuAD},
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author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy},
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journal={arXiv preprint arXiv:1806.03822},
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year={2018}
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}
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Last modified 26d ago
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Contents
SQuAD Dataset
What is SQuAD Dataset?
Download SQuAD Dataset in Python
Load SQuAD Dataset Training Subset in Python
Load SQuAD Dataset Validation Subset in Python
SQuAD Dataset Structure
How to use SQuAD Dataset with PyTorch and TensorFlow in Python
SQuAD Dataset Creation
Additional Information about SQuAD Dataset
SQuAD Dataset Description