Update README.md

README.md

@@ -30,10 +30,32 @@ from transformers import AutoTokenizer, AutoModelWithLMHead
 
 tokenizer = AutoTokenizer.from_pretrained('macedonizer/blaze-koneski') \\nmodel = AutoModelWithLMHead.from_pretrained('macedonizer/blaze-koneski')
 
-input_text = 'Моска '
-
-if len(input_text) == 0: \\n    encoded_input = tokenizer(input_text, return_tensors="pt") \\n    output = model.generate( \\n        bos_token_id=random.randint(1, 50000), \\n        do_sample=True, \\n        top_k=50, \\n        max_length=1024, \\n        top_p=0.95, \\n        num_return_sequences=1, \\n    ) \\nelse: \\n    encoded_input = tokenizer(input_text, return_tensors="pt") \\n    output = model.generate( \\n        **encoded_input, \\n        bos_token_id=random.randint(1, 50000), \\n        do_sample=True, \\n        top_k=50, \\n        max_length=1024, \\n        top_p=0.95, \\n        num_return_sequences=1, \\n    )
-
-decoded_output = [] \\nfor sample in output: \\n    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
+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: \
+decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
 
 print(decoded_output)