From fb5dbe30b5cb662113d77903cb64552c57aa6ef9 Mon Sep 17 00:00:00 2001 From: Matthew Honnibal Date: Sun, 26 Jul 2020 13:43:22 +0200 Subject: [PATCH] Trim training 101 --- website/docs/usage/101/_training.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/website/docs/usage/101/_training.md b/website/docs/usage/101/_training.md index ca7971db7..4573f5ea3 100644 --- a/website/docs/usage/101/_training.md +++ b/website/docs/usage/101/_training.md @@ -38,7 +38,4 @@ it's learning the right things, you don't only need **training data** – you'll also need **evaluation data**. If you only test the model with the data it was trained on, you'll have no idea how well it's generalizing. If you want to train a model from scratch, you usually need at least a few hundred examples for both -training and evaluation. A good rule of thumb is that you should have 10 -samples for each significant figure of accuracy you report. -If you only have 100 samples and your model predicts 92 of them correctly, you -would report accuracy of 0.9 rather than 0.92. +training and evaluation.