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.
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