Update README.md

README.md

@@ -11,7 +11,7 @@ widget:
 - text: "Total net sales decreased [X]% or $[MASK] billion during [XXXX] compared to [XXXX]."
 - text: "Total net sales decreased [X]% or $[X.X] billion during [MASK] compared to [XXXX]."
 - text: "During [MASK], the Company repurchased $[XX.X] billion of its common stock and paid dividend equivalents of $[XX.X] billion."
-- text: "During 2019, the Company repurchased $[MASK] billion of its common stock and paid [MASK] equivalents of $[XX.X] billion."
+- text: "During 2019, the Company repurchased $[MASK] billion of its common stock and paid dividend equivalents of $[XX.X] billion."
 ---
 
 # SEC-BERT
@@ -48,7 +48,7 @@ model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
 
 ## Pre-process Text
 
-To use SEC-BERT-SHAPE, you have to pre-process texts replacing every numerical token with the corresponding shape pseudo-token from a list of 214 predefined shape pseudo-tokens. If the numerical token does not correspond to any shape pseudo token we replace it with the [NUM] pseudo-token.
+To use SEC-BERT-SHAPE, you have to pre-process texts replacing every numerical token with the corresponding shape pseudo-token, from a list of 214 predefined shape pseudo-tokens. If the numerical token does not correspond to any shape pseudo token we replace it with the [NUM] pseudo-token.
 Below there is an example of how you can pre-process a simple sentence. This approach is quite simple; feel free to modify it as you see fit.
 
 ```python