diff --git a/wyk/06_Zanurzenia_slow.org b/wyk/06_Zanurzenia_slow.org
index 676aa07..656535a 100644
--- a/wyk/06_Zanurzenia_slow.org
+++ b/wyk/06_Zanurzenia_slow.org
@@ -239,12 +239,12 @@ Aby utworzyć taki słownik użyjemy gotowej klasy ~Vocab~ z pakietu torchtext:
 :end:
 
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
-len(vocab)
+vocab.lookup_tokens([0, 1, 2, 10, 12345])
 #+END_SRC
 
 #+RESULTS:
 :results:
-20000
+['<unk>', '</s>', '<s>', 'w', 'wierzyli']
 :end:
 
 *** Definicja sieci
@@ -272,15 +272,12 @@ Naszą prostą sieć neuronową zaimplementujemy używając frameworku PyTorch.
   model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)
 
   vocab.set_default_index(vocab['<unk>'])
-  ixs = torch.tensor(vocab.forward(['mieszkam', 'w', 'londynie']))
-
-  out = model(ixs)
-  out.size()
+  ixs = torch.tensor(vocab.forward(['pies']))
+  out[0][vocab['jest']]
 #+END_SRC
 
 #+RESULTS:
 :results:
-torch.Size([3, 20000])
 :end:
 
 Teraz wyuczmy model. Wpierw tylko potasujmy nasz plik:
@@ -329,7 +326,7 @@ shuf < opensubtitlesA.pl.txt > opensubtitlesA.pl.shuf.txt
 
 #+RESULTS:
 :results:
-(2, 19922)
+(2, 5)
 :end:
 
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
@@ -340,13 +337,13 @@ shuf < opensubtitlesA.pl.txt > opensubtitlesA.pl.shuf.txt
 
 #+RESULTS:
 :results:
-[tensor([    2, 19922,   114,   888,  1152]), tensor([19922,   114,   888,  1152,     3])]
+[tensor([   2,    5,   51, 3481,  231]), tensor([   5,   51, 3481,  231,    4])]
 :end:
 
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
   device = 'cuda'
   model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
-  data = DataLoader(train_dataset, batch_size=8000)
+  data = DataLoader(train_dataset, batch_size=5000)
   optimizer = torch.optim.Adam(model.parameters())
   criterion = torch.nn.NLLLoss()
 
@@ -372,9 +369,15 @@ shuf < opensubtitlesA.pl.txt > opensubtitlesA.pl.shuf.txt
 None
 :end:
 
+Policzmy najbardziej prawdopodobne kontynuację dla zadanego słowa:
+
 #+BEGIN_SRC python :session mysession :exports both :results raw drawer
-  vocab = train_dataset.vocab
-  ixs = torch.tensor(vocab.forward(['jest', 'mieszkam', 'w', 'londynie'])).to(device)
+  device = 'cuda'
+  model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
+  model.load_state_dict(torch.load('model1.bin'))
+  model.eval()
+
+  ixs = torch.tensor(vocab.forward(['dla'])).to(device)
 
   out = model(ixs)
   top = torch.topk(out[0], 10)
@@ -386,5 +389,46 @@ None
 
 #+RESULTS:
 :results:
-[('jorku', 1079, 0.41101229190826416), ('.', 3, 0.07469522953033447), ('<unk>', 0, 0.04370327666401863), (',', 4, 0.023186953738331795), ('...', 15, 0.0091575738042593), ('?', 6, 0.008711819536983967), ('tym', 30, 0.0047738500870764256), ('to', 7, 0.004259662237018347), ('do', 17, 0.004140778910368681), ('w', 10, 0.003930391278117895)]
+[('ciebie', 73, 0.1580502986907959), ('mnie', 26, 0.15395283699035645), ('<unk>', 0, 0.12862136960029602), ('nas', 83, 0.0410110242664814), ('niego', 172, 0.03281523287296295), ('niej', 245, 0.02104802615940571), ('siebie', 181, 0.020788608118891716), ('którego', 365, 0.019379809498786926), ('was', 162, 0.013852755539119244), ('wszystkich', 235, 0.01381855271756649)]
+:end:
+
+Teraz zbadajmy najbardziej podobne zanurzenia dla zadanego słowa:
+
+#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+  vocab = train_dataset.vocab
+  ixs = torch.tensor(vocab.forward(['kłopot'])).to(device)
+
+  out = model(ixs)
+  top = torch.topk(out[0], 10)
+  top_indices = top.indices.tolist()
+  top_probs = top.values.tolist()
+  top_words = vocab.lookup_tokens(top_indices)
+  list(zip(top_words, top_indices, top_probs))
+#+END_SRC
+
+#+RESULTS:
+:results:
+[('.', 3, 0.404473215341568), (',', 4, 0.14222915470600128), ('z', 14, 0.10945753753185272), ('?', 6, 0.09583134204149246), ('w', 10, 0.050338443368673325), ('na', 12, 0.020703863352537155), ('i', 11, 0.016762692481279373), ('<unk>', 0, 0.014571071602404118), ('...', 15, 0.01453721895813942), ('</s>', 1, 0.011769450269639492)]
+:end:
+
+#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+  cos = nn.CosineSimilarity(dim=1, eps=1e-6)
+
+  embeddings = model.model[0].weight
+
+  vec = embeddings[vocab['poszedł']]
+
+  similarities = cos(vec, embeddings)
+
+  top = torch.topk(similarities, 10)
+
+  top_indices = top.indices.tolist()
+  top_probs = top.values.tolist()
+  top_words = vocab.lookup_tokens(top_indices)
+  list(zip(top_words, top_indices, top_probs))
+#+END_SRC
+
+#+RESULTS:
+:results:
+[('poszedł', 1087, 1.0), ('idziesz', 1050, 0.4907470941543579), ('przyjeżdża', 4920, 0.45242372155189514), ('pojechałam', 12784, 0.4342481195926666), ('wrócił', 1023, 0.431664377450943), ('dobrać', 10351, 0.4312002956867218), ('stałeś', 5738, 0.4258835017681122), ('poszła', 1563, 0.41979148983955383), ('trafiłam', 18857, 0.4109022617340088), ('jedzie', 1674, 0.4091658890247345)]
 :end: