> For the complete documentation index, see [llms.txt](https://nomadamas.gitbook.io/ragchain-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nomadamas.gitbook.io/ragchain-docs/ragchain-structure/file-loader/dataset-loader.md). # Dataset Loader Open-Domain Question Answering (ODQA) is a task in natural language processing that involves answering questions about any topic or domain, usually by selecting the answer from a large corpus of documents. ODQA systems are expected to understand the question, find relevant documents or passages, and extract an answer. So RAG workflow is popular for solving ODQA tasks. The ODQA Dataset Loader is an essential component of our File Loader that facilitates the loading and processing of datasets used for training and evaluating ODQA systems. This loader is specifically designed to handle the unique structure and requirements of these datasets. #### Functionality The primary function of the ODQA Dataset Loader is to ingest datasets used in open-domain question answering tasks into your application. These datasets typically contain pairs of questions and their corresponding answers, often along with additional information such as context or source documents. The loader parses these datasets, converting them into a standardized format (Document Objects) that can be easily manipulated for further processing like training machine learning models or evaluating system performance. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://nomadamas.gitbook.io/ragchain-docs/ragchain-structure/file-loader/dataset-loader.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.