Evaluation and agreement scripts for the DISCOSUMO project. Each evaluation script takes both manual annotations as automatic summarization output. The formatting of these files is highly project-specific. However, the evaluation functions for precision, recall, ROUGE, Jaccard, Cohen's kappa and Fleiss' kappa may be applicable to other domains too.

A UDF to evaluate Spark-MLlib classification model using PySpark

Self-practice on Machine Learning with Hands-on Machine Learning Book

Creating and Managing Machine Learning Models

This repository is the official location of the SKLOverlay Project. Here, it will hold everything used for the package on Py Pi, including source files.

Code for the paper "This is not correct! Negation-aware Evaluation of Language Generation Systems"

This repository contains Python code to classify fashion items using a Convolutional Neural Network (CNN) implemented with TensorFlow and Keras. It includes data preprocessing, model building, training, evaluation, and visualization of results.

This Python package is designed to easily evaluate your machine learning models

This repository offers an evaluation of machine translation models for healthcare, focusing on languages like Telugu, Hindi, Arabic, and Swahili. It emphasizes accuracy and medical terminology, aiming to enhance medical communication across diverse languages. The dataset used in evaluation is provided.

🧠 Java Machine-Learning framework for model training, evaluation, deployment, tuning and benchmarking!

Calculating Fréchet inception distance and other metrics with PyTorch.

Official repo for “MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module”

Re-implementation of GLEU, evaluation metric of grammatical error correction

All Pre-processing Steps and MAchine Learning Algorithm -Basic Evaluation Metrics

This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.

This is the final project for Udacity A/B Testing provided by Google. In this project, We implement a few statistical powers to make our data-driven solution that can bring impact to business

Data Science and Machine Learning in Python

[study note] We need metrics and I'll study about Generation, Evaluation & Metrics (GEM) for NLG.😊

A tool to evaluate and compare object detection models using the coco metrics (https://cocodataset.org/#detection-eval) and tools available by the cocoapi (https://github.com/cocodataset/cocoapi)