Metadata-Version: 2.1
Name: income
Version: 0.0.1
Summary: Domain Adaptation for Memory-Efficient Dense Retrieval
Home-page: https://github.com/NThakur20/income
Author: Nandan Thakur
Author-email: nandant@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/NThakur20/income/archive/v0.0.1.zip
Description: <h1 style="text-align:center">
        <img style="vertical-align:middle" width="772" height="180" src="./images/income-logo.png" />
        </h1>
        
        ## :dollar: What is it?
        Index Compression Methods (INCOME) repository helps you easily train and evaluate memory-compressed binary retrievers on any custom dataset. We provide recent state-of-the-art techniques for training and unsupervised (without requiring custom training data) for domain-adaptation of NLP-based binary retrieval models across any dataset. 
        
        For more information, checkout our publication:
        - [Domain Adaptation for Memory-Efficient Dense Retrieval](https://arxiv.org/abs/2205.11498/) (Arxiv preprint)
        
        ## :dollar: Installation
        One can either install income via `pip`
        ```bash
        pip install income
        ```
        or via source using `git clone`
        ```bash
        $ git clone https://github.com/Nthakur20/income.git
        $ cd income
        $ pip install -e .
        ```
        
        ## :dollar: Models Supported
        
        
        
        ### Uploaded Public Models
        
        
        
        ## :dollar: Quick Example
        
        
        ## :dollar: Why should we do domain adaptation?
        
        
        ## :dollar: Inference
        
        
        ## :dollar: Training
        
        
        ## :dollar: Citing & Authors
        If you find this repository helpful, feel free to cite our recent publication: [Domain Adaptation for Memory-Efficient Dense Retrieval](https://arxiv.org/abs/2205.11498/):
        
        ```
        @article{thakur2022domain,
          title={Domain Adaptation for Memory-Efficient Dense Retrieval},
          author={Thakur, Nandan and Reimers, Nils and Lin, Jimmy},
          journal={arXiv preprint arXiv:2205.11498},
          year={2022},
          url={https://arxiv.org/abs/2205.11498/}
        }
        ```
        
        The main contributors of this repository are:
        - [Nandan Thakur](https://github.com/Nthakur20), Personal Website: [nandan-thakur.com](https://nandan-thakur.com)
        
        Contact person: Nandan Thakur, [nandant@gmail.com](mailto:nandant@gmail.com)
        
        Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions.
        
        > This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.
Keywords: Information Retrieval Transformer Networks BERT PyTorch IR NLP deep learning
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
