diff --git a/_site/Syllabus_EDUC654.doc b/_site/Syllabus_EDUC654.doc
deleted file mode 100644
index 26d47f1..0000000
Binary files a/_site/Syllabus_EDUC654.doc and /dev/null differ
diff --git a/_site/Syllabus_EDUC654.pdf b/_site/Syllabus_EDUC654.pdf
index 49b04f1..7e869c0 100644
Binary files a/_site/Syllabus_EDUC654.pdf and b/_site/Syllabus_EDUC654.pdf differ
diff --git a/_site/slides/slide2.Rmd b/_site/slides/slide2.Rmd
index abc8c11..7d72575 100644
--- a/_site/slides/slide2.Rmd
+++ b/_site/slides/slide2.Rmd
@@ -1336,9 +1336,9 @@ model
 
 ---
 
-- Each model has a limit for the number of characters they can process. 
+- Each model has a limit for the number of **tokens** they can process. 
 
-- For instance, RoBERTa can handle a text sequence with a maximum number of 512 characters. 
+- For instance, RoBERTa can handle a text sequence with a maximum number of 512 tokens. 
 
 .indent[
 .single[
@@ -1350,7 +1350,7 @@ model$get_max_seq_length()
 ]
 ]
 
-- If we submit any text with more than 512 characters, it will only process the first 512 characters.
+- If we submit any text with more than 512 tokens, it will only process the first tokens.
 
 - Another essential characteristic is the length of the output vector when a language model returns numerical embeddings. 
 
@@ -1366,7 +1366,7 @@ model$get_sentence_embedding_dimension()
 ]
 ]
 
-- RoBERTa can take any text sequence up to 512 characters as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
+- RoBERTa can take any text sequence up to 512 tokens as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
 
 ---
 
@@ -1424,3 +1424,5 @@ dim(embeddings)
 Make sure you review the following notebook to see how to process 2834 reading excerpts in the CommonLit Readability dataset and obtain a 2834 x 768 matrix of numerical embeddings using the [AllenAI Longformer model](https://huggingface.co/allenai/longformer-base-4096). 
 
 [https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii](https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii)
+
+We will use the output of this notebook (a 2834 x 768 matrix of numerical embeddings, **readability_features.csv**) in the following weeks to build a prediction model for readability scores.
diff --git a/_site/slides/slide2.html b/_site/slides/slide2.html
index 5d8defa..2e3f5fb 100644
--- a/_site/slides/slide2.html
+++ b/_site/slides/slide2.html
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@@ -342,8 +342,8 @@
 `$$x_{1} = sin(\frac{2 \pi x}{max(x)})$$`
 
 `$$x_{2} = cos(\frac{2 \pi x}{max(x)})$$`
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 ---
 
@@ -964,9 +964,9 @@
 
 ---
 
-- Each model has a limit for the number of characters they can process. 
+- Each model has a limit for the number of **tokens** they can process. 
 
-- For instance, RoBERTa can handle a text sequence with a maximum number of 512 characters. 
+- For instance, RoBERTa can handle a text sequence with a maximum number of 512 tokens. 
 
 .indent[
 .single[
@@ -983,7 +983,7 @@
 ]
 ]
 
-- If we submit any text with more than 512 characters, it will only process the first 512 characters.
+- If we submit any text with more than 512 tokens, it will only process the first tokens.
 
 - Another essential characteristic is the length of the output vector when a language model returns numerical embeddings. 
 
@@ -1004,7 +1004,7 @@
 ]
 ]
 
-- RoBERTa can take any text sequence up to 512 characters as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
+- RoBERTa can take any text sequence up to 512 tokens as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
 
 ---
 
@@ -1102,6 +1102,8 @@
 Make sure you review the following notebook to see how to process 2834 reading excerpts in the CommonLit Readability dataset and obtain a 2834 x 768 matrix of numerical embeddings using the [AllenAI Longformer model](https://huggingface.co/allenai/longformer-base-4096). 
 
 [https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii](https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii)
+
+We will use the output of this notebook (a 2834 x 768 matrix of numerical embeddings, **readability_features.csv**) in the following weeks to build a prediction model for readability scores.
     </textarea>
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diff --git a/slides/my_custom.css b/slides/my_custom.css
index 21aca8c..968c85e 100644
--- a/slides/my_custom.css
+++ b/slides/my_custom.css
@@ -15624,6 +15624,27 @@
 }
 
 
+/* Extra CSS */
+.red {
+  color: red;
+}
+.blue {
+  color: blue;
+}
+.red-pink {
+  color: red_pink;
+}
+.grey-light {
+  color: grey_light;
+}
+.purple {
+  color: purple;
+}
+.small {
+  font-size: 90%;
+}
+
+
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   color: red;
diff --git a/slides/slide2.Rmd b/slides/slide2.Rmd
index abc8c11..7d72575 100644
--- a/slides/slide2.Rmd
+++ b/slides/slide2.Rmd
@@ -1336,9 +1336,9 @@ model
 
 ---
 
-- Each model has a limit for the number of characters they can process. 
+- Each model has a limit for the number of **tokens** they can process. 
 
-- For instance, RoBERTa can handle a text sequence with a maximum number of 512 characters. 
+- For instance, RoBERTa can handle a text sequence with a maximum number of 512 tokens. 
 
 .indent[
 .single[
@@ -1350,7 +1350,7 @@ model$get_max_seq_length()
 ]
 ]
 
-- If we submit any text with more than 512 characters, it will only process the first 512 characters.
+- If we submit any text with more than 512 tokens, it will only process the first tokens.
 
 - Another essential characteristic is the length of the output vector when a language model returns numerical embeddings. 
 
@@ -1366,7 +1366,7 @@ model$get_sentence_embedding_dimension()
 ]
 ]
 
-- RoBERTa can take any text sequence up to 512 characters as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
+- RoBERTa can take any text sequence up to 512 tokens as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
 
 ---
 
@@ -1424,3 +1424,5 @@ dim(embeddings)
 Make sure you review the following notebook to see how to process 2834 reading excerpts in the CommonLit Readability dataset and obtain a 2834 x 768 matrix of numerical embeddings using the [AllenAI Longformer model](https://huggingface.co/allenai/longformer-base-4096). 
 
 [https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii](https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii)
+
+We will use the output of this notebook (a 2834 x 768 matrix of numerical embeddings, **readability_features.csv**) in the following weeks to build a prediction model for readability scores.
diff --git a/slides/slide2.html b/slides/slide2.html
index 5d8defa..2e3f5fb 100644
--- a/slides/slide2.html
+++ b/slides/slide2.html
@@ -179,8 +179,8 @@
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 ---
 
@@ -274,8 +274,8 @@
 
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+<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-075ba42cc6751f19dc2f" style="width:100%;height:auto;"></div>
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 ---
 
@@ -308,8 +308,8 @@
 
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+<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-7f664ba779076de674fe" style="width:100%;height:auto;"></div>
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 ---
 
@@ -342,8 +342,8 @@
 `$$x_{1} = sin(\frac{2 \pi x}{max(x)})$$`
 
 `$$x_{2} = cos(\frac{2 \pi x}{max(x)})$$`
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 ---
 
@@ -651,8 +651,8 @@
 
 - Identify the variables with missing data, and then create a binary indicator variable for every variable to indicate missingness (0: not missing, 1: missing). 
 
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+<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-91cd0e212bd1aedb28c1" style="width:100%;height:auto;"></div>
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 ---
 
@@ -964,9 +964,9 @@
 
 ---
 
-- Each model has a limit for the number of characters they can process. 
+- Each model has a limit for the number of **tokens** they can process. 
 
-- For instance, RoBERTa can handle a text sequence with a maximum number of 512 characters. 
+- For instance, RoBERTa can handle a text sequence with a maximum number of 512 tokens. 
 
 .indent[
 .single[
@@ -983,7 +983,7 @@
 ]
 ]
 
-- If we submit any text with more than 512 characters, it will only process the first 512 characters.
+- If we submit any text with more than 512 tokens, it will only process the first tokens.
 
 - Another essential characteristic is the length of the output vector when a language model returns numerical embeddings. 
 
@@ -1004,7 +1004,7 @@
 ]
 ]
 
-- RoBERTa can take any text sequence up to 512 characters as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
+- RoBERTa can take any text sequence up to 512 tokens as input and then return a numerical vector with a length of 768 that represent this text sequence. This process is also called **encoding**.
 
 ---
 
@@ -1102,6 +1102,8 @@
 Make sure you review the following notebook to see how to process 2834 reading excerpts in the CommonLit Readability dataset and obtain a 2834 x 768 matrix of numerical embeddings using the [AllenAI Longformer model](https://huggingface.co/allenai/longformer-base-4096). 
 
 [https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii](https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii)
+
+We will use the output of this notebook (a 2834 x 768 matrix of numerical embeddings, **readability_features.csv**) in the following weeks to build a prediction model for readability scores.
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