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@@ -126,9 +126,9 @@ Since, pre-training BERT is a particularly expensive operation that basically re
 
 We also include [two Jupyter Notebooks](https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/notebooks) that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
 
-- The first NoteBook ([Comparing TF and PT models.ipynb](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing%20TF%20and%20PT%20models.ipynb)) extract the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and compute the sandard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
+- The first NoteBook ([Comparing TF and PT models.ipynb](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing%20TF%20and%20PT%20models.ipynb)) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the sandard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
 
-- The second NoteBook ([Comparing TF and PT models SQuAD predictions.ipynb](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing%20TF%20and%20PT%20models%20SQuAD%20predictions.ipynb)) compare the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer and compute the sandard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
+- The second NoteBook ([Comparing TF and PT models SQuAD predictions.ipynb](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing%20TF%20and%20PT%20models%20SQuAD%20predictions.ipynb)) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer and computes the sandard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
 
 Please follow the instructions given in the Notebooks to run and modify them.