Update bpe_openai_gpt2.py

signficant -> significant

ch02/02_bonus_bytepair-encoder/bpe_openai_gpt2.py

@@ -48,7 +48,7 @@ def bytes_to_unicode():
     The reversible bpe codes work on unicode strings.
     This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
     When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
     To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
     And avoids mapping to whitespace/control characters the bpe code barfs on.
     """
@@ -171,4 +171,4 @@ def download_vocab():
                 # 1k for chunk_size, since Ethernet packet size is around 1500 bytes
                 for chunk in r.iter_content(chunk_size=chunk_size):
                     f.write(chunk)
-                    pbar.update(chunk_size)
+                    pbar.update(chunk_size)