Update README.md

README.md

@@ -2,7 +2,7 @@
 language: 
 - fr
 
-thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png?token=ACXKU6CWEZIYA65LXAOFQQ3ASFTMG
+thumbnail: https://github.com/AntoineSimoulin/gpt-fr/blob/main/imgs/logo.png?raw=true
 tags:
 - Tensorflow
 - PyTroch
@@ -52,13 +52,14 @@ beam_outputs = model.generate(
     num_return_sequences=1
 )
 
-print("Output:\n" + 100 * '-')
+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: