diff --git a/README.md b/README.md
index d0fd120967d..a487b03fdb7 100644
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@ This package comprises the following classes that can be imported in Python and
   - `BertAdam` - Bert version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate.
 
 - A configuration class (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file):
-  - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilisities to read and write from JSON configuration files.
+  - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilities to read and write from JSON configuration files.
 
 The repository further comprises:
 
@@ -361,9 +361,9 @@ Here is how to use these techniques in our scripts:
 - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps.
 - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs.
 - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below).
-- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjuted or a positive power of two in which case the scaling is static.
+- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjusted or a positive power of two in which case the scaling is static.
 
-To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information reguarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests perfomed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116).
+To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information regarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116).
 
 Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details):
 ```bash
diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py
index 5954b78f683..23a6eba2f16 100644
--- a/pytorch_pretrained_bert/tokenization.py
+++ b/pytorch_pretrained_bert/tokenization.py
@@ -263,7 +263,7 @@ class WordpieceTokenizer(object):
 
         Args:
           text: A single token or whitespace separated tokens. This should have
-            already been passed through `BasicTokenizer.
+            already been passed through `BasicTokenizer`.
 
         Returns:
           A list of wordpiece tokens.