fixed small typos in the README.md (huggingface#8)

README.md

@@ -106,13 +106,13 @@ 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 suppying an integer greater or equal to 0 to the `--local_rank` argument. 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 blog post for more details):
+- **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument. 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 blog post for more details):
 
 ```bash
 python -m torch.distributed.launch --nproc_per_node=4 --nnodes=2 --node_rank=$THIS_MACHINE_INDEX --master_addr="192.168.1.1" --master_port=1234 run_classifier.py (--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
 ```
 
-Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP adress `192.168.1.1` and an open port `1234`.
+Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP address `192.168.1.1` and an open port `1234`.
 
 ## TPU support and pretraining scripts
 
@@ -128,17 +128,17 @@ 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)) 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 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 standard 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)) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the `BertForQuestionAnswering` and computes 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 of the `BertForQuestionAnswering` and computes the standard 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. They can also be nice example on how to use the models in a simpler way than the full fine-tuning scripts we provide.
 
 ## Fine-tuning with BERT: running the examples
 
 We showcase the same examples as [the original implementation](https://github.com/google-research/bert/): fine-tuning a sequence-level classifier on the MRPC classification corpus and a token-level classifier on the question answering dataset SQuAD.
 
-Before running theses examples you should download the
+Before running these examples you should download the
 [GLUE data](https://gluebenchmark.com/tasks) by running
 [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
 and unpack it to some directory `$GLUE_DIR`. Please also download the `BERT-Base`