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  # Dataset Summary
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  This collection is a compilation of long context datasets, specifically designed for tasks requiring extensive comprehension and inference from large text inputs.
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- Currently, the datasets included in this collection are derived from Natural Questions for multi-passage question answering, and BookSum, a long context summarization dataset.
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- # Detailed Description
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- ## Multi-passage QA from Natural Questions:
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- This dataset is a multi-passage question answering dataset derived from the original Natural Questions (NQ) dataset by Google. The NQ dataset consists of real user queries issued to Google's search engine, paired with high-quality answers. In this derived version, each example consists of a question along with multiple (10-200) Wiki passages, from which the model must infer the correct answer. This dataset is designed to challenge and evaluate models on their ability to handle complex, multi-passage question answering.
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- ## BookSum:
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- BookSum is a dataset for long context summarization. It includes a vast collection of books from various genres, and the task is to generate a coherent and concise summary given a long context from the book. This dataset is designed to test and train models on their ability to understand and summarize long, complex narratives.
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- ### Dataset Limitations and Future Work
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- While these datasets provide a robust platform for training and evaluating models on long context tasks, they may still contain some limitations. For instance, the datasets might be biased towards the types of questions asked in Google's search engine and the genres of books included in the BookSum dataset. In the future, we plan to expand this collection to include more diverse datasets for a wider range of long context tasks.
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  ### Licensing Information
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  # Dataset Summary
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  This collection is a compilation of long context datasets, specifically designed for tasks requiring extensive comprehension and inference from large text inputs.
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+ Currently, it encompasses data intended for training a robust base model, which can be found in the pretrain/ directory. Additionally, it includes datasets tailored for specific needs, located in the fine-tune/ directory. These specialized datasets include multi-passage question answering, derived from Natural Questions, and long-context summarization, exemplified by the BookSum dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Licensing Information
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