up

README.md

@@ -376,9 +376,9 @@ 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. 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 `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 ensures 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:
+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. For example, to download the Hindi split, simply specify the corresponding language config name (i.e., "hi" for Hindi):
 ```python
 from datasets import load_dataset
 
@@ -394,7 +394,10 @@ 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) directly with your local/ streaming datasets.
+*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
+
+Local:
+
 ```python
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler
@@ -404,7 +407,8 @@ batch_sampler = BatchSampler(RandomSampler(cv_11), batch_size=32, drop_last=Fals
 dataloader = DataLoader(cv_11, batch_sampler=batch_sampler)
 ```
 
-ofcourse, you can also create a dataloader with streaming datasets as well.
+Streaming:
+
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader