From df1a1a53a5342fa8bb27d19800777b55682d18a1 Mon Sep 17 00:00:00 2001 From: Rudrendu Date: Sat, 8 Aug 2026 23:45:36 -0700 Subject: [PATCH] Fix IoU typo in types_of_evaluations.mdx --- docs/source/types_of_evaluations.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/types_of_evaluations.mdx b/docs/source/types_of_evaluations.mdx index 7bd2cc9cb..af8e4dcba 100644 --- a/docs/source/types_of_evaluations.mdx +++ b/docs/source/types_of_evaluations.mdx @@ -10,7 +10,7 @@ A metric measures the performance of a model on a given dataset. This is often b Examples of metrics include: - [Accuracy](https://huggingface.co/metrics/accuracy) : the proportion of correct predictions among the total number of cases processed. - [Exact Match](https://huggingface.co/metrics/exact_match): the rate at which the input predicted strings exactly match their references. -- [Mean Intersection over union (IoUO)](https://huggingface.co/metrics/mean_iou): the area of overlap between the predicted segmentation of an image and the ground truth divided by the area of union between the predicted segmentation and the ground truth. +- [Mean Intersection over union (IoU)](https://huggingface.co/metrics/mean_iou): the area of overlap between the predicted segmentation of an image and the ground truth divided by the area of union between the predicted segmentation and the ground truth. Metrics are often used to track model performance on benchmark datasets, and to report progress on tasks such as [machine translation](https://huggingface.co/tasks/translation) and [image classification](https://huggingface.co/tasks/image-classification).