mdr_roberta_q_encoder

Introduction
The MDR_ROBERTA_Q_ENCODER is a component of the Multi-Hop Dense Retrieval (MDR) framework designed for answering complex open-domain questions using advanced retrieval techniques. It leverages the RoBERTa architecture and is implemented using the PyTorch library. The associated research paper, "Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval," provides detailed insights into the model's capabilities and applications.

Architecture
The MDR_ROBERTA_Q_ENCODER is based on the RoBERTa transformer model, optimized for enhanced question encoding in multi-hop retrieval tasks. This architecture enables the model to process and retrieve information across multiple documents, thus improving its ability to handle complex queries.

Training
The model is trained as part of the Multi-Hop Dense Retrieval system, with the q_model checkpoint available for download. Training utilizes extensive datasets to enhance the model's retrieval accuracy and efficiency in processing open-domain questions.

Guide: Running Locally

1. Setup the Environment: Ensure that Python and PyTorch are installed on your system.
2. Clone the Repository: Use the command git clone https://github.com/facebookresearch/multihop_dense_retrieval/ to clone the necessary codebase.
3. Download the Checkpoint: Retrieve the q_model checkpoint from q_encoder.pt.
4. Run the Model: Load the model within a Python script or Jupyter Notebook to process queries using the loaded checkpoint.

For optimal performance, it is recommended to use cloud-based GPUs, such as those provided by AWS or Google Cloud, to handle the computational requirements efficiently.

License
The MDR_ROBERTA_Q_ENCODER is distributed under the license available at LICENSE, which outlines the terms and conditions of usage and distribution.