ance msmarco doc maxp

Introduction
ANCE-MSMARCO-DOC-MAXP is a model adapted from Microsoft's ANCE repository. It is designed for dense text retrieval using Approximate Nearest Neighbor Negative Contrastive Learning. The model has been integrated with Pyserini to facilitate its usage for information retrieval tasks.

Architecture
The model architecture employs a dense representation mechanism for text retrieval, leveraging the concepts of negative contrastive learning to enhance retrieval performance. It is built on top of the RoBERTa transformer model and is compatible with both PyTorch and the Hugging Face Transformers library.

Training
This model was trained with the objective of optimizing dense text retrieval tasks. The training process involves negative contrastive learning, which is instrumental in improving the model's ability to distinguish between relevant and non-relevant documents in large datasets.

Guide: Running Locally

1. Clone the Repository: Clone the original ANCE repository from GitHub.
2. Set Up Environment: Ensure that your environment is set up with Python, PyTorch, and the Pyserini library.
3. Install Dependencies: Use pip to install any required packages from the requirements.txt file.
4. Load the Model: Use the Hugging Face Transformers library to load the ANCE-MSMARCO-DOC-MAXP model.
5. Run Experiments: Follow the instructions in the Pyserini experiments guide to run text retrieval tasks.

For optimal performance, using cloud GPUs such as AWS EC2 instances or Google Cloud's Compute Engine is recommended.

License
The model follows the licensing terms as outlined in the original ANCE repository and the Pyserini framework. For precise licensing details, please refer to the respective repositories.