Update README.md

README.md

@@ -32,30 +32,30 @@ tokenizer = AutoTokenizer.from_pretrained('macedonizer/blaze-koneski') \\nmodel
 
 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, \
-)
-
-decoded_output = [] \
-for sample in output: \
+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))
 
 print(decoded_output)