Evaluate. A library for easily evaluating machine learning models and datasets. With a single line of code, you get access to dozens of evaluation methods ... Installation · Choosing a metric for your task · Main classes · Using the `evaluator`

Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. It currently contains:. Releases 11 · Issues 156 · Setup.py · Pull requests 61

Evaluate is tested on Python 3.7+. Virtual environment. You should install Evaluate in a virtual environment to keep everything neat and tidy.

This guide is for you! It covers the different ways you can evaluate a model, guides on designing your own evaluations, and tips and tricks from practical ...

The Evaluator classes allow to evaluate a triplet of model, dataset, and metric. The models wrapped in a pipeline, responsible for handling all preprocessing ...

Evaluate provides access to a wide range of evaluation tools. It covers a range of modalities such as text, computer vision, audio, etc. as well as tools to ...

Продолжительность: 33:26
Опубликовано: 9 мая 2023 г.

The goal of the 🤗 Evaluate library is to support different types of evaluation, depending on different goals, datasets and models.

This guide will show how to load a pre-trained Hugging Face pipeline, log it to MLflow, and use mlflow.evaluate() to evaluate builtin metrics as well as custom ...

20 авг. 2023 г. · This blog is about the process of fine-tuning a Hugging Face Language Model (LM) using the Transformers library and customize the evaluation ...