RiverText

RiverTex is an open-source library for modeling and training different incremental word vector architectures proposed by the state-of-the-art.

It seeks to standardize many existing incremental word vector algorithms into a unified framework to provide a standardized interface and facilitate the development of new methods.

RiverTex provides two training paradigms:

• learn_one, which trains one instance at a time;

• and learn_many, which trains a mini-batch of instances at a time.

This allows for more efficient training of text representation models with text data streams.

RiverText also provides an interface similar to the river package, making it easy for developers to use the library to quickly and easily train text representation models.

The official documentation can be found at this link.

Installation

Requirements

These package will be installed along with the package, in case these have not already been installed:

1. nltk
2. numpy
3. river
4. scikit_learn
5. scipy
6. torch
7. tqdm

Contributing

Development Requirements

Testing

All unit tests are in the rivertext/tests folder. It uses pytest as a framework to run them.

To run the test, execute:

pytest tests

To check the coverage, run:

pytest tests --cov-report xml:cov.xml --cov rivertext

And then:

coverage report -m

Build the documentation

The documentation is created using mkdocs and mkdocs-material. It can be found in the docs folder at the root of the project. First, you need to install:

pip install mkdocs
pip install "mkdocstrings[python]"
pip install mkdocs-material

Then, to compile the documentation, run:

mkdocs build
mkdocs serve

Changelog

References

@article{montiel2021river,
  title={River: machine learning for streaming data in Python},
  author={Montiel, Jacob and Halford, Max and Mastelini, Saulo Martiello and Bolmier, Geoffrey and Sourty,
    Raphael and Vaysse, Robin and Zouitine, Adil and Gomes, Heitor Murilo and Read, Jesse and Abdessalem,
    Talel and others},
  year={2021}
}

@article{bravo2022incremental,
  title={Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction},
  author={Bravo-Marquez, Felipe and Khanchandani, Arun and Pfahringer, Bernhard},
  journal={Cognitive Computation},
  volume={14},
  number={1},
  pages={425--441},
  year={2022},
  publisher={Springer}
}

@article{kaji2017incremental,
  title={Incremental skip-gram model with negative sampling},
  author={Kaji, Nobuhiro and Kobayashi, Hayato},
  journal={arXiv preprint arXiv:1704.03956},
  year={2017}
}

Team

• Gabriel Iturra
• Felipe Bravo-Marquez

Contact

Please write to gabrieliturrab at ug.chile.cl for inquiries about the software. You are also welcome to do a pull request or publish an issue in the RiverText repository on Github.