Selection — 3 881

Python wrapper for Stanford CoreNLP [Deprecated]

The Classical Language Toolkit.

A "machine learning framework to automate text-and voice-based conversations."

Transcode sentence (or other sequence) to list of word vector.

Multilingual text (NLP) processing toolkit.

Reading Wikipedia to answer open-domain questions.

A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

Natural Language Understanding library for intent classification and entity extraction

Named-entity recognition using neural networks providing state-of-the-art-results

conversational AI library with many pre-trained Russian NLP models.

topic modelling platform.

A Natural Adversarial Language Processing framework built over Tensorflow.

A deep learning-based translation library between 50 languages, built with transformers.

Track, log, visualize and evaluate your LLM prompts and prompt chains.

Text preprocessing package for use in NLP tasks.

Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution.

> An easy-to-use & supercharged open-source AI metadata tracker.

> A general purpose recommender metrics library for fair evaluation.

> A PyTorch based deep learning library for drug pair scoring

> A distributed machine learning framework Apache Spark

> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.

> A delightful machine learning tool that allows you to train/fit, test and use models without writing code

> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.

PyTorch Frame -> A Modular Framework for Multi-Modal Tabular Learning.

> Graph Neural Network Library for PyTorch.

> A temporal extension of PyTorch Geometric for dynamic graph representation learning.

> A graph sampling extension library for NetworkX with a Scikit-Learn like API.

> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.

> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.

> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.

> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.

Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

High performance library for time series distances (DTW) and time series clustering.

Deep learning operations reinvented (for pytorch, tensorflow, jax and others).

InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations.

Unified API for SHAP, LIME, permutation importance, and partial dependence explanations.