This repository contains the implementation of the Differentiable Search Index (DSI), an innovative approach to information retrieval (IR). Unlike traditional IR systems that follow the index-then-retrieve pipeline, DSI integrates indexing and retrieval within a single Transformer language model to provide a ranked list of documents in response to user queries.
The goal of this project is to build a model f that, given a query q, outputs a ranked list of document ids. The model should be a unified system trained on a corpus of documents and thereafter used to retrieve relevant documents.
We utilize the MS Marco dataset for this task. Detailed instructions on how to access and use the dataset with the Pyserini library are included.
To set up your environment to use the DSI model, follow these steps:
git clone https://github.com/yourusername/neural-inverted-index.git
cd neural-inverted-indexThe performance of the search index is evaluated using the following metrics:
- Mean Average Precision (MAP): Reflects the mean of the average precision scores across a set of queries.
- Precision at K: Measures the proportion of relevant documents among the top K retrieved documents.
- Average Precision (AP): The average of precision values calculated at the points in the ranking where each relevant document is retrieved.
- Recall@1000: Indicates the proportion of relevant items found in the top-1000 results.
For more information and to understand the underlying concepts, refer to the following papers:
- Transformer-Memory as a Differentiable Search Index
- Differentiable Search Index for Information Retrieval
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Marco Francomano - francomano
- Luigi Gallo - luigi-ga
See also the list of contributors who participated in this project.