From e71cbf258c718739d5c57ab8b0b09c882eac6239 Mon Sep 17 00:00:00 2001 From: Futtetennista Date: Wed, 16 Feb 2022 17:26:15 +0000 Subject: [PATCH] Fix typo in documentation --- keras/layers/preprocessing/text_vectorization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/keras/layers/preprocessing/text_vectorization.py b/keras/layers/preprocessing/text_vectorization.py index 3e33386bcb8..d1c8cff8d81 100644 --- a/keras/layers/preprocessing/text_vectorization.py +++ b/keras/layers/preprocessing/text_vectorization.py @@ -389,7 +389,7 @@ def adapt(self, data, batch_size=None, steps=None): During `adapt()`, the layer will build a vocabulary of all string tokens seen in the dataset, sorted by occurance count, with ties broken by sort order of the tokens (high to low). At the end of `adapt()`, if `max_tokens` - is set, the voculary wil be truncated to `max_tokens` size. For example, + is set, the vocabulary wil be truncated to `max_tokens` size. For example, adapting a layer with `max_tokens=1000` will compute the 1000 most frequent tokens occurring in the input dataset. If `output_mode='tf-idf'`, `adapt()` will also learn the document frequencies of each token in the input dataset.