Update README.md

README.md

@@ -29,30 +29,30 @@ nmodel = AutoModelWithLMHead.from_pretrained('macedonizer/blaze-koneski')
 
 input_text = 'Москва '
 
-if len(input_text) == 0: 
-    encoded_input = tokenizer(input_text, return_tensors="pt") 
-    output = model.generate( 
-        bos_token_id=random.randint(1, 50000), 
-        do_sample=True, 
-        top_k=50, 
-        max_length=1024, 
-        top_p=0.95, 
-        num_return_sequences=1, 
-     ) 
-else: 
-    encoded_input = tokenizer(input_text, return_tensors="pt") 
-    output = model.generate( 
-        **encoded_input, 
-        bos_token_id=random.randint(1, 50000), 
-        do_sample=True, 
-        top_k=50, 
-        max_length=1024, 
-        top_p=0.95, 
-        num_return_sequences=1, 
+if len(input_text) == 0: \
+    encoded_input = tokenizer(input_text, return_tensors="pt") \
+    output = model.generate( \
+        bos_token_id=random.randint(1, 50000), \
+        do_sample=True, \
+        top_k=50, \
+        max_length=1024, \
+        top_p=0.95, \
+        num_return_sequences=1, \
+     ) \
+else: \
+    encoded_input = tokenizer(input_text, return_tensors="pt") \
+    output = model.generate( \
+        **encoded_input, \
+        bos_token_id=random.randint(1, 50000), \
+        do_sample=True, \
+        top_k=50, \
+        max_length=1024, \
+        top_p=0.95, \
+        num_return_sequences=1, \
     )
 
-decoded_output = [] 
-for sample in output: 
-decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
+decoded_output = [] \
+for sample in output: \
+    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
 
 print(decoded_output)