The LLM Evaluation Framework

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

[NeurIPS'21 Outstanding Paper] Library for reliable evaluation on RL and ML benchmarks, even with only a handful of seeds.

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Python SDK for agent evals and observability

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

📈 Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.

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. Mor…

A Neural Framework for MT Evaluation

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron.

⚡️A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion 🐍

Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)

Open-Source Evaluation for GenAI Application Pipelines

Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

A Python wrapper for the ROUGE summarization evaluation package

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

[ICLR'24] Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.

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

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