From 717535fe3b39191a8cda158b99a5824fa171159f Mon Sep 17 00:00:00 2001
From: ricardocalleja <61325417+ricardocalleja@users.noreply.github.com>
Date: Mon, 12 Oct 2020 05:59:57 -0300
Subject: [PATCH] Update 10_nlp.ipynb (#291)

fix typo, changed "preforms" by "it performs"
---
 10_nlp.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/10_nlp.ipynb b/10_nlp.ipynb
index 05e13a4..1be5189 100644
--- a/10_nlp.ipynb
+++ b/10_nlp.ipynb
@@ -1320,7 +1320,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One thing that's different to previous types we've used in `DataBlock` is that we're not just using the class directly (i.e., `TextBlock(...)`, but instead are calling a *class method*. A class method is a Python method that, as the name suggests, belongs to a *class* rather than an *object*. (Be sure to search online for more information about class methods if you're not familiar with them, since they're commonly used in many Python libraries and applications; we've used them a few times previously in the book, but haven't called attention to them.) The reason that `TextBlock` is special is that setting up the numericalizer's vocab can take a long time (we have to read and tokenize every document to get the vocab). To be as efficient as possible preforms a few optimizations: \n",
+    "One thing that's different to previous types we've used in `DataBlock` is that we're not just using the class directly (i.e., `TextBlock(...)`, but instead are calling a *class method*. A class method is a Python method that, as the name suggests, belongs to a *class* rather than an *object*. (Be sure to search online for more information about class methods if you're not familiar with them, since they're commonly used in many Python libraries and applications; we've used them a few times previously in the book, but haven't called attention to them.) The reason that `TextBlock` is special is that setting up the numericalizer's vocab can take a long time (we have to read and tokenize every document to get the vocab). To be as efficient as possible it performs a few optimizations: \n",
     "\n",
     "- It saves the tokenized documents in a temporary folder, so it doesn't have to tokenize them more than once\n",
     "- It runs multiple tokenization processes in parallel, to take advantage of your computer's CPUs\n",