minor fixes

NLU_lab_7-8/create_datasets.py

@@ -108,7 +108,8 @@ def write_to_files(lines_contents):
     l = (len(lines_contents) / 10) * 8
     contents_train = lines_contents[:int(l)]
     contents_test = lines_contents[int(l):]
-    with open('train.conllu', 'a', encoding='utf-8') as train_f, open('test.conllu', 'a+', encoding='utf-8') as test_f:
+    with open('train-pl.conllu', 'a', encoding='utf-8') as train_f, open('test-pl.conllu', 'a+', encoding='utf-8') as \
+            test_f:
         for content in contents_train:
             train_f.write(format_content(content))
         for content in contents_test:
@@ -116,8 +117,7 @@ def write_to_files(lines_contents):
 
 
 def main():
-    # path = sys.argv[1]
-    path = rf'C:\Users\Kacper Dudzic\Desktop\dane'
+    path = sys.argv[1]
     dir = Path(rf'{path}')
     for file in dir.glob('*'):
         processed_contents = process_file(file)

NLU_lab_7-8/main.py

@@ -68,6 +68,7 @@ embeddings = StackedEmbeddings(embeddings=embedding_types)
 tagger = SequenceTagger(hidden_size=256, embeddings=embeddings,
                         tag_dictionary=tag_dictionary,
                         tag_type='slot', use_crf=True)
+
 """
 trainer = ModelTrainer(tagger, corpus)
 trainer.train('slot-model-pl',
@@ -80,5 +81,5 @@ try:
     model = SequenceTagger.load('slot-model-pl/best-model.pt')
 except:
     model = SequenceTagger.load('slot-model-pl/final-model.pt')
-
-print(tabulate(predict(model, 'Jeden bilet na imię Jan Kowalski na film Batman'.split()), tablefmt='html'))
+        
+print(tabulate(predict(model, 'Jeden bilet na imię Jan Kowalski na film Batman'.split())))