diff --git a/chapter_natural-language-processing-pretraining/bert.md b/chapter_natural-language-processing-pretraining/bert.md
index 007e32e024..e46979384b 100644
--- a/chapter_natural-language-processing-pretraining/bert.md
+++ b/chapter_natural-language-processing-pretraining/bert.md
@@ -196,6 +196,7 @@ class BERTEncoder(nn.Block):
     def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,
                  num_blks, dropout, max_len=1000, **kwargs):
         super(BERTEncoder, self).__init__(**kwargs)
+        self.num_hiddens = num_hiddens
         self.token_embedding = nn.Embedding(vocab_size, num_hiddens)
         self.segment_embedding = nn.Embedding(2, num_hiddens)
         self.blks = nn.Sequential()
@@ -210,7 +211,9 @@ class BERTEncoder(nn.Block):
     def forward(self, tokens, segments, valid_lens):
         # Shape of `X` remains unchanged in the following code snippet:
         # (batch size, max sequence length, `num_hiddens`)
-        X = self.token_embedding(tokens) + self.segment_embedding(segments)
+        # the embedding values are multiplied by the square root of the embedding dimension 
+        # to rescale before they are summed up
+        X = self.token_embedding(tokens)*math.sqrt(self.num_hiddens) + self.segment_embedding(segments)
         X = X + self.pos_embedding.data(ctx=X.ctx)[:, :X.shape[1], :]
         for blk in self.blks:
             X = blk(X, valid_lens)
@@ -225,6 +228,7 @@ class BERTEncoder(nn.Module):
     def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,
                  num_blks, dropout, max_len=1000, **kwargs):
         super(BERTEncoder, self).__init__(**kwargs)
+        self.num_hiddens = num_hiddens
         self.token_embedding = nn.Embedding(vocab_size, num_hiddens)
         self.segment_embedding = nn.Embedding(2, num_hiddens)
         self.blks = nn.Sequential()
@@ -239,7 +243,9 @@ class BERTEncoder(nn.Module):
     def forward(self, tokens, segments, valid_lens):
         # Shape of `X` remains unchanged in the following code snippet:
         # (batch size, max sequence length, `num_hiddens`)
-        X = self.token_embedding(tokens) + self.segment_embedding(segments)
+        # the embedding values are multiplied by the square root of the embedding dimension 
+        # to rescale before they are summed up
+        X = self.token_embedding(tokens)*math.sqrt(self.num_hiddens) + self.segment_embedding(segments)
         X = X + self.pos_embedding[:, :X.shape[1], :]
         for blk in self.blks:
             X = blk(X, valid_lens)