up

README.md

@@ -376,9 +376,7 @@ Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basqu
 
 ### How to use
 
-The 'datasets' library allows you to load and pre-process your dataset in pure Python at scale. There's no need to rely on hacky shell scripts and decades old C/C++ pre-processing scripts anymore.
-
-The minimalistic API ensure that you can plug-and-play this dataset in your existing Machine Learning workflow with just a few lines of code.
+The `datasets` library allows you to load and pre-process your dataset in pure Python at scale. No need to rely on decades old hacky shell scripts and C/C++ pre-processing scripts anymore. The minimalistic API ensure that you can plug-and-play this dataset in your existing Machine Learning workflow with just a few lines of code.
 
 The entire dataset (or a particular split) can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. To download the Hindi split, simply specify the corresponding dataset config name:
 ```python
@@ -387,7 +385,7 @@ from datasets import load_dataset
 cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
 ```
 
-Using the datasets library, you can stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
+Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
 ```python
 from datasets import load_dataset
 
@@ -396,7 +394,7 @@ cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train"
 print(next(iter(cv_11)))
 ```
 
-*Bonus*: Create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) with directly with the downloaded/ streamed datasets.
+*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your local/ streaming datasets.
 ```python
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler