Update README.md

README.md

@@ -74,6 +74,7 @@ You can then use the model directly through the pipeline API, which provides a h
 
 This will create a pipeline object for text-to-text generation using your model. You can then pass the input text to the pipe object to generate the gender-neutral version. The output will be a list of dictionaries, each containing the generated text.
 Alternatively, you can still load the tokenizer and model manually for more fine-grained control:
+    
     from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
     
     tokenizer = AutoTokenizer.from_pretrained("mongrz/model_output")