diff --git a/run.py b/run.py
new file mode 100644
index 0000000..062a755
--- /dev/null
+++ b/run.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from utils import *
+
+
+# In[2]:
+
+
+data = get_csv("train/in.tsv.xz")
+
+
+# In[3]:
+
+
+train_labels = get_csv("train/expected.tsv")
+
+
+# In[4]:
+
+
+train_data = data[[6,7]]
+
+
+# In[5]:
+
+
+train_data = pd.concat([train_data, train_labels], axis=1)
+
+
+# In[6]:
+
+
+train_data[607] = train_data[6] + train_data[0] + train_data[7]
+
+
+# In[7]:
+
+
+train_data[607] = train_data[607].apply(clean_text)
+
+
+# In[8]:
+
+
+train_data[607]
+
+
+# In[15]:
+
+
+with open("tmp",  "w+") as f:
+    for t in train_data[607]:
+        f.write(t + "\n")
+
+
+# In[10]:
+
+
+KENLM_BUILD_PATH = "../kenlm/build/"
+get_ipython().system('$KENLM_BUILD_PATH/bin/lmplz -o 4 < tmp > model.arpa')
+
+
+# In[11]:
+
+
+get_ipython().system('rm tmp')
+
+
+# In[16]:
+
+
+import kenlm
+model = kenlm.Model("./model.arpa")
+
+
+# In[23]:
+
+
+get_ipython().system('pip install english_words')
+
+
+# In[24]:
+
+
+from english_words import english_words_alpha_set
+from math import log10
+
+def predict(before, after):
+    result = ''
+    prob = 0.0
+    best = []
+    for word in english_words_alpha_set:
+        text = ' '.join([before, word, after])
+        text_score = model.score(text, bos=False, eos=False)
+        if len(best) < 12:
+            best.append((word, text_score))
+        else:
+            is_better = False
+            worst_score = None
+            for score in best:
+                if not worst_score:
+                    worst_score = score
+                else:
+                    if worst_score[1] > score[1]:
+                        worst_score = score
+            if worst_score[1] < text_score:
+                best.remove(worst_score)
+                best.append((word, text_score))
+    probs = sorted(best, key=lambda tup: tup[1], reverse=True)
+    pred_str = ''
+    for word, prob in probs:
+        pred_str += f'{word}:{prob} '
+    pred_str += f':{log10(0.99)}'
+    return pred_str
+
+
+# In[27]:
+
+
+from nltk import trigrams, word_tokenize
+
+def make_prediction(path, result_path):
+    pdata = get_csv(path)
+    with open(result_path, 'w', encoding='utf-8') as file_out:
+        for _, row in pdata.iterrows():
+            before, after = word_tokenize(clean_text(str(row[6]))), word_tokenize(clean_text(str(row[7])))
+            if len(before) < 2 or len(after) < 2:
+                pred = prediction
+            else:
+                pred = predict(before[-1], after[0])
+            file_out.write(pred + '\n')
+
+
+# In[28]:
+
+
+make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")
+
+
+# In[29]:
+
+
+make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")
+
+
+# In[ ]:
+
+
+
+