diff --git a/wyk/11_Transformer.org b/wyk/11_Transformer.org
index 3a41ea9..bef5f66 100644
--- a/wyk/11_Transformer.org
+++ b/wyk/11_Transformer.org
@@ -90,25 +90,23 @@ Dokonajmy najpierw tokenizacji:
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
   from transformers import AutoTokenizer
   tokenizer = AutoTokenizer.from_pretrained("gpt2")
-  text = "The World War III will begin in"
+  text = "The World War III will begin in 2028 in"
   encoded_input = tokenizer(text, return_tensors='pt')
   encoded_input
 #+END_SRC
 
 #+RESULTS:
 :results:
-{'input_ids': tensor([[ 464, 2159, 1810, 6711,  481, 2221,  287]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]])}
+{'input_ids': tensor([[ 464, 2159, 1810, 6711,  481, 2221,  287, 1160, 2078,  287]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}
 :end:
 
-Możemy podejrzeć uzyskane tokeny:
-
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
-  [tokenizer.decode(i) for i in encoded_input.input_ids[0]]
+    [tokenizer.decode(i) for i in encoded_input.input_ids[0]]
 #+END_SRC
 
 #+RESULTS:
 :results:
-['The', ' World', ' War', ' III', ' will', ' begin', ' in']
+['The', ' World', ' War', ' III', ' will', ' begin', ' in', ' 20', '28', ' in']
 :end:
 
 Zwróćmy uwagę, że w GPT-2 tokeny obejmują spacje!
@@ -125,6 +123,15 @@ Teraz uruchommy zasadniczy model:
 :results:
 :end:
 
+#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+    softmax(outputs[0][0][-1])
+#+END_SRC
+
+#+RESULTS:
+:results:
+:end:
+
+
 Z modelu GPT-2 otrzymamy rozkład prawdopodobieństwa kolejnego wyrazu, najpierw w postaci
 nieznormalizowanych *logitów*:
 
@@ -140,14 +147,14 @@ tensor([-130.2947, -129.5677, -136.4030,  ..., -138.3791, -138.8967,
 :end:
 
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
-  from torch import softmax, topk
+    from torch import softmax, topk
 
-  k = 20
+    k = 20
 
-  t = topk(softmax(logits, -1), k)
+    t = topk(softmax(logits, -1), k)
 
-  tb = [[tokenizer.decode(t.indices[ix]), t.values[ix].item()] for ix in range(k)]
-  tb
+    tb = [[tokenizer.decode(t.indices[ix]), t.values[ix].item()] for ix in range(k)]
+    tb
 #+END_SRC
 
 #+RESULTS: