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# WikiQuest-NLP Dataset
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**WikiQuest-NLP** is a comprehensive dataset designed for training and evaluating NLP models.It is generated using the Google NQ dataset. It contains two main components:
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1. **Filtered Wikipedia Data**: A cleaned and filtered version of Wikipedia content, extracted and processed for NLP training.
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2. **Question-Answer Pairs**: A CSV file with contextually relevant questions and their corresponding answers, derived from the filtered Wikipedia text.
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## Features
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### Filtered Wikipedia Data (TXT)
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- **Content**: This file contains text from Wikipedia, preprocessed to remove irrelevant content and formatted with newline characters to preserve paragraph structure.
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- **Format**: Plain text file with newline-separated paragraphs for easy use in language modeling.
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### Question-Answer Pairs (CSV)
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- **Columns**:
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- `context`: The filtered Wikipedia text providing context for the question.
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- `question`: The question pertaining to the context.
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- `answer`: The answer to the question, extracted from the context.
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- **Format**: Comma-separated values (CSV) file, suitable for direct use in question-answering model training.
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## Usage
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- **Language Modeling**: Use the filtered Wikipedia data to train language models from scratch or as a base for further fine-tuning.
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- **Question Answering**: Utilize the question-answer pairs for training models on question-answering tasks, enabling evaluation and improvement of QA systems.
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## How to Access
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The dataset is hosted on Hugging Face, accessible for direct download and integration into your projects.
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- [Download WikiQuest-NLP Dataset](link-to-your-dataset-on-hugging-face)
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## License
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This dataset is created and provided for research and educational purposes. Ensure compliance with usage policies and licensing terms when incorporating the dataset into your work.
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## Contact
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For any questions or issues regarding the dataset, please contact:
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- **Your Name**: Piyush Bhatt
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- **Your GitHub**: https://github.com/Piyush2102020
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---
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We hope WikiQuest-NLP serves as a valuable resource for your NLP research and model development. Happy modeling!
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