mim-nlp 0.2.1

A Python package with ready-to-use models for various NLP tasks and text preprocessing utilities. The implementation allows fine-tuning.

MIM NLP

With this package you can easily use pre-trained models and fine-tune them, as well as create and train your own neural networks.

Below, we list NLP tasks and models that are available:

• Classification
  • Neural Network
  • SVM
• Regression
  • Neural Network
• Seq2Seq
  • Summarization (Neural Network)

It comes with utilities for text pre-processing such as:

• Text cleaning
• Lemmatization
• Deduplication

Installation

We recommend installing with pip.

pip install mim-nlp

The package comes with the following extras (optional dependencies for given modules):

• svm - simple svm model for classification
• classifier - classification models: svm, neural networks
• regressor - regression models
• preprocessing - cleaning text, lemmatization and deduplication
• seq2seq - Seq2Seq and Summarizer models

Usage

Examples can be found in the notebooks directory.

Model classes

• classifier.nn.NNClassifier - Neural Network Classifier
• classifier.svm.SVMClassifier - Support Vector Machine Classifier
• classifier.svm.SVMClassifierWithFeatureSelection - SVMClassifier with additional feature selection step
• regressor.AutoRegressor - regressor based on transformers' Auto Classes
• regressor.NNRegressor - Neural Network Regressor
• seq2seq.AutoSummarizer - summarizer based on transformers' Auto Classes

Interface

All the model classes have common interface:

• fit
• predict
• save
• load

and specific additional methods.

Text pre-processing

• preprocessing.TextCleaner - define a pipeline for text cleaning, supports concurrent processesing
• preprocessing.lemmatize - lemmatize text in Polish with Morfeusz
• preprocessing.Deduplicator - find near-duplicate texts (depending on threshold) with Jaccard index for n-grams

Development

Remember to use a separate environment for each project. Run commands below inside the project's environment.

Dependencies

We use poetry for dependency management. If you have never used it, consult poetry documentation for installation guidelines and basic usage instructions.

poetry install --with dev

To fix the Failed to unlock the collection! error or stuck packages installation, execute the below command:

export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring

Git hooks

We use pre-commit for git hook management. If you have never used it, consult pre-commit documentation for installation guidelines and basic usage instructions.

pre-commit install

There are two hooks available:

• isort – runs isort for both .py files and notebooks. Fails if any changes are made, so you have to run git add and git commit once again.
• Strip notebooks – produces stripped versions of notebooks in stripped directory.

Tests

pytest

Linting

We use isort and flake8 along with nbqa to ensure code quality. The appropriate options are set in configuration files. You can run them with:

isort .
nbqa isort notebooks

and

flake8 .
nbqa flake8 notebooks --nbqa-shell

Code formatting

You can run black to format code (including notebooks):

black .

New version release

In order to add the next version of the package to PyPI, do the following steps:

• First, increment the package version in pyproject.toml.
• Then build the new version: run poetry build in the root directory.
• Finally, upload to PyPI: poetry publish (two newly created files).
  • If you get Invalid or non-existent authentication information. error, add PyPI token to poetry: poetry config pypi-token.pypi <my-token>.