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  This dataset contains 50,000 samples of user financial queries paired with their corresponding structured data requests (context). It was created to facilitate the creation of the [Financial Agent](https://huggingface.co/Chaitanya14/Financial_Agent) LLM for accurate data extraction and query answering.
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  # How to load the Dataset
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  ## Template-Based Expansion:
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  5,000 query templates were generated (4,000 from technology channels, 500 from advisors, and 500 from investors). These templates were then scaled up to 50,000 samples by randomly varying elements within each template, ensuring a broad and varied representation of financial queries.
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- This dataset provides a valuable resource for developing and evaluating LLMs in the financial domain, particularly for applications requiring accurate understanding and response to user financial queries. For more information regarding the dataset, please refer to our [paper](https://arxiv.org/abs/2502.18471).
 
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+ ---
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+ task_categories:
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+ - text2text-generation
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+ language:
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+ - en
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+ pretty_name: Financial Context Dataset
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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  This dataset contains 50,000 samples of user financial queries paired with their corresponding structured data requests (context). It was created to facilitate the creation of the [Financial Agent](https://huggingface.co/Chaitanya14/Financial_Agent) LLM for accurate data extraction and query answering.
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  # How to load the Dataset
 
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  ## Template-Based Expansion:
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  5,000 query templates were generated (4,000 from technology channels, 500 from advisors, and 500 from investors). These templates were then scaled up to 50,000 samples by randomly varying elements within each template, ensuring a broad and varied representation of financial queries.
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+ This dataset provides a valuable resource for developing and evaluating LLMs in the financial domain, particularly for applications requiring accurate understanding and response to user financial queries. For more information regarding the dataset, please refer to our [paper](https://arxiv.org/abs/2502.18471).