Evaluation Metrics
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A real-time baseline 3D multi-object tracking system using LiDAR point clouds, combining 3D Kalman filter and Hungarian algorithm.
A multi-language library providing implementations of common supervised machine learning evaluation metrics.
A Python library providing evaluation metrics and diagnostic tools for recommender systems.
PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).
A command-line tool for holistic comparison and error analysis of language generation systems like machine translation and summarization.
A Python toolkit for visual analysis and evaluation of text generation tasks like translation, summarization, and captioning.
A Python library providing comprehensive metrics for fair and thorough evaluation of recommender systems.
A JAX/Flax implementation of the Fréchet Inception Distance (FID) metric for evaluating generative models.
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