diff --git a/notebooks/24_NLP_4_ngrams_word_vectors.ipynb b/notebooks/24_NLP_4_ngrams_word_vectors.ipynb
index 7a25f4b..10cc22c 100644
--- a/notebooks/24_NLP_4_ngrams_word_vectors.ipynb
+++ b/notebooks/24_NLP_4_ngrams_word_vectors.ipynb
@@ -2649,7 +2649,7 @@
     "\n",
     "A more fundamental limitation of Word2Vec and similar algorithms lies in the underlying bag-of-words approach, which removes information related to the order of words. Even constructs like n-grams can only compensate for extremely local patterns, such as differentiating \"do not like\" from \"do like\".\n",
     "\n",
-    "In contrast, deep learning techniques like recurrent neural networks and, more powerfully, **transformers**, can learn patterns across many more words. Transformers, in particular, can learn patterns across entire pages of text {cite}`vaswani2017attention`, enabling models like ChatGPT and other large language models to use natural language with unprecedented subtlety. Models such as BERT (Bidirectional Encoder Representations from Transformers) {cite}`devlin2018bert` and GPT (Generative Pretrained Transformer) {cite}`radford2018improvin`g produce contextualized representations of words within a given context, taking the entire sentence or paragraph into account rather than generating static word embeddings.\n",
+    "In contrast, deep learning techniques like recurrent neural networks and, more powerfully, **transformers**, can learn patterns across many more words. Transformers, in particular, can learn patterns across entire pages of text {cite}`vaswani2017attention`, enabling models like ChatGPT and other large language models to use natural language with unprecedented subtlety. Models such as BERT (Bidirectional Encoder Representations from Transformers) {cite}`devlin2018bert` and GPT (Generative Pretrained Transformer) {cite}`radford2018improving` produce contextualized representations of words within a given context, taking the entire sentence or paragraph into account rather than generating static word embeddings.\n",
     "\n",
     "In conclusion, while TF-IDF and n-grams offer a solid start, word embeddings like those produced by Word2Vec and contextualized representations from transformers provide more advanced methods for working with text by considering context and semantic meaning."
    ]