Update README.md

README.md

@@ -53,14 +53,14 @@ beam_outputs = model.generate(
     num_return_sequences=1
 )
 
-print("Output:\\\\
+print("Output:\\\\\\\\
 " + 100 * '-')
 print(tokenizer.decode(beam_outputs[0], skip_special_tokens=True))
 ```
 
 #### Limitations and bias
 
-Large pre-trained language models tend to reproduce the biases from the dataset used for pre-training, in particular gender discrimination. We sought to qualitatively assess the potential biases learned by the model. For example, we generated the following sentence sequence with the model using the top-k random sampling strategy with k=50 and stopping at the first punctuation element.  "Ma femme/Mon mari vient d'obtenir un nouveau poste en tant qu'\\\\\\\\_\\\\\\\\_\\\\\\\\_\\\\\\\\_\\\\\\\\_\\\\\\\\_":
+Large pre-trained language models tend to reproduce the biases from the dataset used for pre-training, in particular gender discrimination. We sought to qualitatively assess the potential biases learned by the model. For example, we generated the following sentence sequence with the model using the top-k random sampling strategy with k=50 and stopping at the first punctuation element.  "Ma femme/Mon mari vient d'obtenir un nouveau poste en tant qu'\_\_\_\_\_\_\_":
 
 The position generated for the wife are: