ance msmarco doc firstp

Introduction

The ANCE-MSMARCO-DOC-FIRSTP model is adapted from the original ANCE repository, aligning it with the Pyserini toolkit for dense text retrieval tasks. The model utilizes Approximate Nearest Neighbor Negative Contrastive Learning, as detailed in the related research paper.

Architecture

ANCE-MSMARCO-DOC-FIRSTP is built on the transformers library and implemented using PyTorch. It is based on the RoBERTa architecture, optimized for document retrieval tasks by leveraging dense representations.

Training

The model was trained by integrating the ANCE method into the Pyserini framework to enhance dense text retrieval. It applies the Approximate Nearest Neighbor Negative Contrastive Learning technique, focusing on efficiently retrieving relevant documents from large datasets.

Guide: Running Locally

1. Clone the Repository:

   git clone https://github.com/castorini/pyserini.git
   cd pyserini
   

2. Install Dependencies: Ensure you have Python installed and set up a virtual environment. Then, install the necessary packages:

   pip install -r requirements.txt
   

3. Download the Model: Access the model from Hugging Face's model hub and load it into your environment.

4. Run Experiments: Follow the experiment guide available at Pyserini's documentation to run your tests.

5. Suggested Cloud GPUs: For efficient training and inference, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The model and associated code are released under the Apache License 2.0, allowing for both personal and commercial use with attribution.