>>> from datasets import load_dataset

>>> dataset = load_dataset("cornell-movie-review-data/rotten_tomatoes", split="train")

# Get the first row in the dataset
>>> dataset[0]
{'label': 1,
 'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'}

# Get the last row in the dataset
>>> dataset[-1]
{'label': 0,
 'text': 'things really get weird , though not particularly scary : the movie is all portent and no content .'}

>>> dataset["text"]
['the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .',
 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .',
 'effective but too-tepid biopic',
 ...,
 'things really get weird , though not particularly scary : the movie is all portent and no content .']

>>> dataset[0]["text"]
'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'

>>> import time

>>> start_time = time.time()
>>> text = dataset[0]["text"]
>>> end_time = time.time()
>>> print(f"Elapsed time: {end_time - start_time:.4f} seconds")
Elapsed time: 0.0031 seconds

>>> start_time = time.time()
>>> text = dataset["text"][0]
>>> end_time = time.time()
>>> print(f"Elapsed time: {end_time - start_time:.4f} seconds")
Elapsed time: 0.0042 seconds

# Get the first three rows
>>> dataset[:3]
{'label': [1, 1, 1],
 'text': ['the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .',
  'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .',
  'effective but too-tepid biopic']}

# Get rows between three and six
>>> dataset[3:6]
{'label': [1, 1, 1],
 'text': ['if you sometimes like to go to the movies to have fun , wasabi is a good place to start .',
  "emerges as something rare , an issue movie that's so honest and keenly observed that it doesn't feel like one .",
  'the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game .']}

>>> from datasets import load_dataset

>>> iterable_dataset = load_dataset("ethz/food101", split="train", streaming=True)
>>> for example in iterable_dataset:
...     print(example)
...     break
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F0681F5C520>, 'label': 6}

>>> from datasets import load_dataset

>>> dataset = load_dataset("cornell-movie-review-data/rotten_tomatoes", split="train")
>>> iterable_dataset = dataset.to_iterable_dataset()

>>> next(iter(iterable_dataset))
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F0681F59B50>,
 'label': 6}

>>> for example in iterable_dataset:
...     print(example)
...     break
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F7479DE82B0>, 'label': 6}

>>> next(iter(iterable_dataset["label"]))
6

# Get first three examples
>>> list(iterable_dataset.take(3))
[{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F7479DEE9D0>,
  'label': 6},
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F7479DE8190>,
  'label': 6},
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x383 at 0x7F7479DE8310>,
  'label': 6}]