Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics", IROS 2020, ECCVW 2020

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 Python wrapper for the ROUGE summarization evaluation package

Python wrapper for evaluating summarization quality by ROUGE package

An implementation of a full named-entity evaluation metrics based on SemEval'13 Task 9 - not at tag/token level but considering all the tokens that are part of the named-entity

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.

Learning to Evaluate Image Captioning. CVPR 2018

Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

Code for the paper "Semantic Object Accuracy for Generative Text-to-Image Synthesis"

CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks

Assessing Generative Models via Precision and Recall (official repository)

A news recommendation evaluation framework

Useful python NLP tools (evaluation, GUI interface, tokenization)

Evaluation script for named entity recognition (NER) systems based on entity-level F1 score.

Python implementation of ROUGE

An image-oriented evaluation tool for image captioning systems (EMNLP-IJCNLP 2019)

A reference-free metric for measuring summary quality, learned from human ratings.

It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

Evaluation metrics for machine-composed symbolic music. Paper: "The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-Composed Music through Quantitative Measures", ISMIR 2020

A novel approach to evaluate community detection algorithms on ground truth

Using Natural Language Processing and Bag of Words for feature extraction for sentiment analysis of the customers visited in the Restaurant and at last using Classification algorithm to separate Positive and Negative Sentiments.

Time-series Aware Precision and Recall for Evaluating Anomaly Detection Methods

This repository includes analysis of classification results based on Receiver Operating Characteristic (ROC) Curve and Cumulative Accuracy Profile (CAP) Curves.

Comparing Top food and feed Producers around the globe and also seeking some interesting answers, solutions, patterns, hints and warnings through the power of Data Analysis and Data Visualization using Machine Learning.

In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

Analyzing the Factors on which Graduates get Admissions in Abroad and Visualizing some of the most intriguing and interesting patterns followed onto it using Data Analysis and Data Visualizations Using Machine Learning.

Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades

Facet-Aware Evaluation for Extractive Summarization, ACL 2020

Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling of Clustering (Community Detection) Algorithms Considering Overlaps (Covers)