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asreview-dory

Official extension for ASReview LAB enabling state-of-the-art NLP models with dense embeddings and deep learning architectures. Ideal for systematic reviews where lightweight models fall short.

https://github.com/asreview/asreview-dory

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.5%) to scientific vocabulary

Keywords

asreview data deep-learning machine-learning natural-language-processing python systematic-literature-reviews utrecht-university
Last synced: 6 months ago · JSON representation ·

Repository

Official extension for ASReview LAB enabling state-of-the-art NLP models with dense embeddings and deep learning architectures. Ideal for systematic reviews where lightweight models fall short.

Basic Info
  • Host: GitHub
  • Owner: asreview
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage: https://asreview.ai
  • Size: 148 KB
Statistics
  • Stars: 7
  • Watchers: 3
  • Forks: 2
  • Open Issues: 3
  • Releases: 5
Topics
asreview data deep-learning machine-learning natural-language-processing python systematic-literature-reviews utrecht-university
Created almost 2 years ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.md

ASReview Dory 🐟

DOI

ASReview Dory is an extension to the ASReview software, providing new models for classification and feature extraction. The extension is maintained by the ASReview LAB team.

Installation

You can install ASReview Dory via PyPI using the following command:

bash pip install asreview-dory

⚠️ XGBoost on MacOS If you are using macOS and plan to use XGBoost, you should first install OpenMP (brew install libomp)

Model components

Feature Extractors:

GTR T5
LaBSE
MPNet
Multilingual E5
MXBAI

Classifiers:

AdaBoost
Neural Network - 2-Layer
Neural Network - Dynamic
Neural Network - Warm Start
XGBoost

Explore the performance of these models in our Simulation Gallery! Look for the 🐟 icon to spot the Dory models.

Usage

Once installed, the plugins will be available in the front-end of ASReview, as well as being accessible via the command-line interface.

You can check all available models using: console asreview algorithms

Caching Models

You can pre-load models to avoid downloading them during runtime by using the cache command. To cache specific models, such as xgboost and sbert, run:

console asreview dory cache nb xgboost sbert

To cache all available models at once, use:

console asreview dory cache-all

Compatibility

This plugin is compatible with ASReview version 2 or later. Ensure that your ASReview installation is up-to-date to avoid compatibility issues.

The development of this plugin is done in parallel with the development of the ASReview software. We aim to maintain compatibility with the latest version of ASReview, but please report any issues you encounter.

Contributing

We welcome contributions from the community. To contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Implement your changes.
  4. Commit your changes with a clear message.
  5. Push your changes to your fork.
  6. Open a pull request to the main repository.

Please ensure your code adheres to the existing style and includes relevant tests.

For any questions or further assistance, feel free to contact the ASReview Lab Developers.

Enjoy using ASReview Dory! We hope these new models enhance your systematic review processes.

Owner

  • Name: ASReview
  • Login: asreview
  • Kind: organization
  • Email: asreview@uu.nl
  • Location: Utrecht University

ASReview - Active learning for Systematic Reviews

Citation (CITATION.cff) 

cff-version: 1.2.0
title: ASReview Dory - New and exciting models for ASReview
message: "If you use the ASReview Dory software in your work, please cite it as indicated."
type: software
authors:
  - given-names: ASReview LAB developers
    affiliation: Utrecht University
    email: asreview@uu.nl
version: 1.1.1
doi: 10.5281/zenodo.15649248
date-released: 2025-06-12
url: "https://github.com/asreview/asreview-dory/"

GitHub Events

Total
  • Create event: 11
  • Release event: 2
  • Issues event: 5
  • Watch event: 5
  • Delete event: 8
  • Issue comment event: 17
  • Push event: 34
  • Public event: 1
  • Pull request review comment event: 22
  • Pull request review event: 32
  • Pull request event: 12
  • Fork event: 1
Last Year
  • Create event: 11
  • Release event: 2
  • Issues event: 5
  • Watch event: 5
  • Delete event: 8
  • Issue comment event: 17
  • Push event: 34
  • Public event: 1
  • Pull request review comment event: 22
  • Pull request review event: 32
  • Pull request event: 12
  • Fork event: 1

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 278 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 2
pypi.org: asreview-dory

ASReview New Exciting Models

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 278 Last month
Rankings
Dependent packages count: 9.2%
Average: 30.5%
Dependent repos count: 51.9%
Maintainers (2)
j535d165 timovdkuil
Last synced: 6 months ago