Update README.md

README.md

@@ -62,14 +62,16 @@ In terms of actual outcomes and errors in outputs, see our [readme](https://gith
 
 To use this model, you will need to install the transformers and sentencepiece libraries:
 
-  !pip install transformers sentencepiece
+    !pip install transformers sentencepiece
 
 You can then use the model directly through the pipeline API, which provides a high-level interface for text generation:
+
     from transformers import pipeline
     pipe = pipeline("text2text-generation", model="mongrz/model_output")
     gender_neutral_text = pipe("Pielęgniarki protestują pod sejmem.")
     print(gender_neutral_text)
     #expected output: [{'generated_text': 'Osoby pielęgniarskie protestują pod sejmem.'}] 
+
 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
@@ -85,6 +87,7 @@ Alternatively, you can still load the tokenizer and model manually for more fine
     
     #Decode and print the generated text 
     print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
+    
 This approach allows you to access the tokenizer and model directly and customize the generation process further if needed. Choose the method that best suits your needs.
   
 ## Intended uses & limitations