solution

skrypt.py

@@ -0,0 +1,39 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import string
+import csv
+import re
+
+MNB = MultinomialNB()
+vectorizer = TfidfVectorizer()
+
+X = []
+Y = []
+with open("train/train.tsv", 'r', encoding="utf-8") as train:
+    for line in csv.reader(train, delimiter="\t"):
+        X.append(line[0])
+        Y.append(line[1])
+        
+    Y = vectorizer.fit_transform(Y)
+    MNB.fit(Y, X)
+   
+## Wrtie dev
+dev_in = open('dev-0/in.tsv', 'r', encoding="utf-8")
+with open('dev-0/out.tsv', 'w', encoding="utf-8") as dev_out:
+     dev = vectorizer.transform(dev_in.readlines())
+     dev_predict = MNB.predict(dev)
+     for s in dev_predict:
+         dev_out.write(str(s) + '\n')
+
+dev_in.close()
+
+## Write test
+test_in = open('test-A/in.tsv', 'r', encoding="utf-8")
+with open('test-A/out.tsv', 'w', encoding="utf-8") as test_out:
+     test = vectorizer.transform(test_in.readlines())
+     test_predict = MNB.predict(test)
+     for s in test_predict:
+         test_out.write(str(s) + '\n')
+
+test_in.close()
+