ance dpr question multi

Introduction
The ANCE-DPR-Question-Multi model is adapted from the original ANCE repository and is integrated into the Pyserini toolkit. It employs Approximate Nearest Neighbor Negative Contrastive Learning for efficient dense text retrieval, as detailed in the referenced research paper.

Architecture
The model utilizes the Dense Passage Retrieval (DPR) architecture, which is designed to improve the retrieval of dense text segments by leveraging Transformers and PyTorch libraries. This approach is suitable for tasks involving feature extraction and is compatible with inference endpoints for deployment.

Training
Training details are outlined in the Pyserini documentation. The model is trained using negative contrastive learning techniques to enhance the retrieval of relevant text passages. The training process is based on principles discussed in the associated academic paper.

Guide: Running Locally

1. Clone the ANCE repository from GitHub.
2. Install the necessary dependencies, including PyTorch and Transformers libraries.
3. Follow the instructions in the Pyserini documentation on running experiments with ANCE.
4. For optimal performance, consider using cloud GPUs such as those offered by AWS or Google Cloud.

License
Details regarding the model's license can be found on the original ANCE GitHub repository. Users are encouraged to review these terms to ensure compliance with usage guidelines.