Quicksectx is a simple, fast and no-dependency Python implementation of interval search, adapted from the bx-python project.

WORLD Vocoder is a fast and high-quality vocoder which parameterizes speech into three components:

• f0: Pitch contour

• sp: Harmonic spectral envelope

• ap: Aperiodic spectral envelope

It can also (re)synthesize speech using these features.

seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This module can be used to extract or replace keywords in sentences, based on the FlashText algorithm.

Extremely fast spelling checker and suggester in Python.

The following algorithms are supported currently:

• Edit-distance

• Editex

• Soundex

• Caverphone 1.0 and 2.0

• Typox

All the above algorithms use an underlying Trie-based dictionary for efficient storage and fast computation.

This package provides a Python implementation of IAMsystem algorithm, a fast dictionary-based approach for semantic annotation, a.k.a entity linking.

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

PyRuSH is the python implementation of RuSH, which is originally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.

This package provides Python bindings for the simstring text similarity matching library.

This package provides a fast implementation of the Levenshtein distance with C++ and Cython.

The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's clinical data warehouse. The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe. We also provide some fictitious templates and a script to generate a synthetic dataset.

This package provides tools for unsupervised and semi-supervised morphological segmentation.

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the NLP and IR communities.

PyFastNER is the Python implementation of FastNER. It uses hash function to process multiple rules at the same time. Similar to FastNER, PyFastNER supports token-based rules and character-based rules.

This package provides a module for OpenTURNS for fast Fourier transform computation.

This package provides a module for OpenTURNS to formulate and solve robust optimization problems.

This package provides a module for OpenTURNS for support vector machine.

This package enables to build Probability of Detection (POD) curves from Non Destructive Test. The curves are built using parametric models: univariate linear regression, quantile regression, kriging and polynomial chaos. Analysis can be run in order to test the linear regression hypothesis.

PoD can be built from a set of data or directly from a given physical model that simulate the Non Destructive Test. In this case, the design of experiments is defined iteratively.

Sensitivity analysis can be also be performed. The aggregated Sobol indices are available as well as the perturbation law indices.

This package provides a module for OpenTURNS for integrating with aGrUM.

This package provides a module for OpenTURNS to formulate and solve robust optimization problems.

This package provides a module for OpenTURNS for the Morris sensitivity method.

This package provides a module for OpenTURNS with meshing algorithms.

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.

This package provides a bridge between OpenTURNS and FMUs, based on PyFMI.