diff --git a/geval b/geval
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diff --git a/script.py b/script.py
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+#!/usr/bin/env python
+# coding: utf-8
+
+# MODEL TRIGRAMOWY - uwzględniamy dwa poprzednie słowa
+
+# In[4]:
+
+
+import lzma
+import csv
+import re
+import math
+
+
+# In[5]:
+
+
+def read_data(folder_name):
+    
+    all_data = lzma.open(f'{folder_name}/in.tsv.xz').read().decode('UTF-8').split('\n')
+    data = [line.split('\t') for line in all_data][:-1]
+    data = [[i[6].replace('\\n', ' '), i[7].replace('\\n', ' ')] for i in data]
+    
+    words = []
+    with open(f'{folder_name}/expected.tsv') as file:
+        tsv_file = csv.reader(file, delimiter="\t")
+        for line in tsv_file:
+            words.append(line[0])
+            
+    return data, words
+
+train_data, train_words = read_data('train')
+
+
+# In[10]:
+
+
+def print_example(data, words, idx):
+    print(f'{data[idx][0]} _____{words[idx].upper()}_____ {data[idx][1]}')
+    
+print_example(train_data, train_words, 13)
+
+
+# In[26]:
+
+
+def generate_N_grams(text, ngram=1, no_punctuation=True):
+    text = re.sub(r'[\-] ', '', text).lower()
+    if no_punctuation:
+        text = re.sub(r'[\)\(\.\,\-]', ' ', text)
+    words=[word for word in text.split()]
+    temp=zip(*[words[i:] for i in range(0,ngram)])
+    ans=[' '.join(ngram) for ngram in temp]
+    return ans
+
+N_grams = []
+for i in range(len(train_data[:2000])): # POPRAWIĆ !
+    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 2)
+    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 3)
+
+
+# In[27]:
+
+
+def check_prob(N_grams):
+    count = {}
+    for i in N_grams:
+        i = i.rsplit(maxsplit=1)
+        if i[0] in count:
+            if i[1] in count[i[0]]:
+                count[i[0]][i[1]] += 1
+            else:
+                count[i[0]][i[1]] = 1
+        else:
+            count[i[0]] = {i[1]: 1}
+            
+    for word in count:
+        s = sum(count[word].values())
+        for i in count[word]:
+            count[word][i] = count[word][i] / s
+            
+    return count
+
+probs = check_prob(N_grams)
+
+
+# In[28]:
+
+
+dev_data, dev_words = read_data('dev-0')
+
+
+# In[29]:
+
+
+def find_word(word_1, word_2):
+    tmp_probs = {}
+    if word_1 in probs:
+        if word_2 in probs:
+            for i in probs[word_1]:
+                if i in probs[word_2]:
+                    tmp_probs[i] = probs[word_1][i] * probs[word_2][i]
+                    if tmp_probs[i] == 1:
+                        tmp_probs[i] = 0.1
+                else:
+                    c = probs[word_2][min(probs[word_2].keys(), key=(lambda k: probs[word_2][k]))] / 10
+                    tmp_probs[i] = probs[word_1][i] * c
+        else:
+            tmp_probs = probs[word_1]
+    else:
+        tmp_probs = {}
+    
+    sorted_list = sorted(tmp_probs.items(), key=lambda x: x[1], reverse=True)[:1]
+    tmm = ' '.join([i[0] + ':' + str(i[1]) for i in sorted_list])
+    s = 1 - sum(n for _, n in sorted_list)
+    if s == 0:
+        s = 0.01
+    tmm += ' :' + str(s)
+    if tmp_probs == {}:
+        return ':1'
+    return tmm
+
+
+# In[30]:
+
+
+dev_found_words = []
+
+for i in dev_data:
+    t = i[0]
+    t = re.sub(r'[\-] ', '', t).lower()
+    if True:
+        t = re.sub(r'[\)\(\.\,\-]', ' ', t)
+    words=[word for word in t.split()]
+    dev_found_words.append(find_word(words[-1], ' '.join(words[-2:])))
+
+
+# In[31]:
+
+
+f = open("dev-0/out.tsv", "w")
+f.write('\n'.join(dev_found_words) + '\n')
+f.close()
+
+
+#  
+
+# In[ ]:
+
+
+test_data = read_data('test-A/in.tsv.xz')
+test_data = [[i[6].replace('\\n', ' '), i[7].replace('\\n', ' ')] for i in test_data]
+
+test_found_words = []
+
+for i in test_data:
+    t = i[0]
+    if True:
+        t = re.sub(r'[\.\,\-]', ' ', t).lower()
+    words=[word for word in t.split()]
+    test_found_words.append(find_word(words[-1], ' '.join(words[-2:])))
+
+f = open("test-A/out.tsv", "w")
+f.write('\n'.join(test_found_words) + '\n')
+f.close()
+