Feature/finance test

[chakravarthik27]

Description

FiQA (Finance Question Answering) is a vast dataset created to help with question-answering tasks tailored specifically to finance. The dataset is centered around extracting insights from the financial world, and it provides a wide range of questions and answers about the financial aspects of various companies and indices.

The FiQA dataset is designed to provide a complete understanding of financial concepts, ranging from company-specific inquiries to broader questions concerning market indices. It includes questions about the intricacies of financial operations, market trends, investment strategies, and other key aspects of the financial landscape.

FiQA is a valuable resource for researchers, analysts, and professionals who want to explore and analyze financial data within the context of question-answering models. By leveraging this dataset, individuals can delve into the nuances of financial markets, companies' fiscal performance, and the interplay between economic indicators and corporate operations.

This dataset is enriched with a diverse array of finance-related questions and their corresponding well-structured answers, making it ideal for developing and evaluating sophisticated machine learning models that provide insightful, accurate, and contextually relevant responses to a wide range of finance-related inquiries. The FiQA dataset fosters an environment conducive to the advancement of natural language processing techniques tailored to the intricate world of finance, ultimately facilitating more informed decision-making processes and comprehensive financial analysis.

To use the Langtest library with the FiQA dataset, by following code block:

harness = Harness(task="question-answering",
                   model={"model": "text-davinci-003","hub":"openai"}, 
                   data={"data_source" :"Fiqa"})

---

➤ Fixes # (issue)

Type of change

Please delete options that are not relevant.

• This change requires a documentation update
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)

Usage

Checklist:

• I've added Google style docstrings to my code.
• I've used pydantic for typing when/where necessary.
• I have linted my code
• I have added tests to cover my changes.

Screenshots (if appropriate):

[ArshaanNazir]

@chakravarthik27 kindly add a NB for this one.