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 ... Transformers · Installation · Choosing a metric for your task · Main classes

🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

The metrics in evaluate can be easily integrated with the Trainer. The Trainer accepts a compute_metrics keyword argument that passes a function to compute ...

2 мая 2024 г. · This article provides a comprehensive understanding of evaluation metrics for transformer models, focusing on the methods used to assess the ...

Evaluation on the Hub involves two main steps: Submitting an evaluation job via the UI. This creates an AutoTrain project with N models for evaluation.

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 metrics.

Evaluate a model based on the similarity of the embeddings by calculating the Spearman and Pearson rank correlation in comparison to the gold standard labels.

22 мая 2023 г. · I set evaluation_strategy="no" and do_eval=False when setting the TrainingArguments and then I was able to call the trainer.train() without passing any eval ...

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 ...

9 мая 2023 г. · ... Evaluate library makes this easy, by ... Mastering HuggingFace Transformers: Step-By-Step Guide to Model Finetuning & Inference Pipeline.