submission

run.py

@@ -6,7 +6,7 @@ with open('train/in.tsv', 'r', encoding='utf-8') as f:
     x_train = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
 with open('dev-0/in.tsv', 'r', encoding='utf-8') as f:
     x_dev = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
-with open('train/in.tsv', 'r', encoding='utf-8') as f:
+with open('test-A/in.tsv', 'r', encoding='utf-8') as f:
     x_test = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
 
 y_train = pd.read_csv('train/expected.tsv', sep='\t', names=['paranormal'], encoding='utf-8')
@@ -19,13 +19,13 @@ mnb_model = MultinomialNB().fit(x_train_vectorized, y_train.values.ravel())
 # Dev data
 x_dev_prepared = tfidf_vectorizer.transform(x_dev['text'].values)
 predictions = mnb_model.predict(x_dev_prepared)
-with open('dev-0/out.tsv', 'w') as f:
+with open('dev-0/out.tsv', 'w', encoding='utf-8') as f:
     for pred in predictions:
         f.write(f'{pred}\n')
 
 # Test data
 x_test_vectorized = tfidf_vectorizer.transform(x_test['text'].values)
 predictions = mnb_model.predict(x_test_vectorized)
-with open('test-A/out.tsv', 'w') as f:
+with open('test-A/out.tsv', 'w', encoding='utf-8') as f:
     for pred in predictions:
         f.write(f'{pred}\n')