Change link to CBOW implementation

embeddings.tex

@@ -1757,7 +1757,7 @@ \subsection{Word2Vec}
    	 torch.save(self.model.state_dict(), self.model_path)
    	 logging.warning(f'Save model to {self.model_path}')
 \end{minted}
-\caption Word2Vec CBOW implementation in PyTorch\\ \href{https://github.com/veekaybee/viberary/tree/main/word2vec}{ see full implementation here}
+\caption Word2Vec CBOW implementation in PyTorch\\ \href{https://github.com/veekaybee/viberary/blob/main/src/notebooks/cbow.ipynb}{ see full implementation here}
 \end{figure}
 
 Once we've completed our iteration through our training set, we have learned a model that retrieves both the probability of a given word being the correct word, and the entire embedding space for our vocabulary.