<commit_before><!-- auto-generated by tfds.scripts.document_datasets -->
# Datasets

## Usage

```python
# See all registered datasets
tfds.list_builders()

# Load a given dataset by name, along with the DatasetInfo
data, info = tfds.load("mnist", with_info=True)
train_data, test_data = data['test'], data['train']
assert isinstance(train_data, tf.data.Dataset)
assert info.features['label'].num_classes == 10
assert info.splits['train'].num_examples == 60000

# You can also access a builder directly
builder = tfds.builder("mnist")
assert builder.info.splits['train'].num_examples == 60000
builder.download_and_prepare()
datasets = builder.as_dataset()

# If you need NumPy arrays
np_datasets = tfds.as_numpy(datasets)
```

---

## All Datasets

*   [`audio`](#audio)
    *   [`"nsynth"`](#nsynth)
*   [`image`](#image)
    *   [`"cats_vs_dogs"`](#cats_vs_dogs)
    *   [`"celeb_a"`](#celeb_a)
    *   [`"celeb_a_hq"`](#celeb_a_hq)
    *   [`"chexpert"`](#chexpert)
    *   [`"cifar10"`](#cifar10)
    *   [`"cifar100"`](#cifar100)
    *   [`"coco2014"`](#coco2014)
    *   [`"colorectal_histology"`](#colorectal_histology)
    *   [`"colorectal_histology_large"`](#colorectal_histology_large)
    *   [`"diabetic_retinopathy_detection"`](#diabetic_retinopathy_detection)
    *   [`"fashion_mnist"`](#fashion_mnist)
    *   [`"horses_or_humans"`](#horses_or_humans)
    *   [`"image_label_folder"`](#image_label_folder)
    *   [`"imagenet2012"`](#imagenet2012)
    *   [`"lsun"`](#lsun)
    *   [`"mnist"`](#mnist)
    *   [`"omniglot"`](#omniglot)
    *   [`"open_images_v4"`](#open_images_v4)
    *   [`"quickdraw_bitmap"`](#quickdraw_bitmap)
    *   [`"rock_paper_scissors"`](#rock_paper_scissors)
    *   [`"svhn_cropped"`](#svhn_cropped)

*   [`structured`](#structured)

    *   [`"titanic"`](#titanic)

*   [`text`](#text)

    *   [`"imdb_reviews"`](#imdb_reviews)
    *   [`"lm1b"`](#lm1b)
    *   [`"squad"`](#squad)

*   [`translate`](#translate)

    *   [`"flores_translate_neen"`](#flores_translate_neen)
    *   [`"flores_translate_sien"`](#flores_translate_sien)
    *   [`"wmt_translate_ende"`](#wmt_translate_ende)
    *   [`"wmt_translate_enfr"`](#wmt_translate_enfr)

*   [`video`](#video)<commit_msg>Add "tf_flowers" to "image" dataset table. (#126)<commit_after><!-- auto-generated by tfds.scripts.document_datasets -->
# Datasets

## Usage

```python
# See all registered datasets
tfds.list_builders()

# Load a given dataset by name, along with the DatasetInfo
data, info = tfds.load("mnist", with_info=True)
train_data, test_data = data['test'], data['train']
assert isinstance(train_data, tf.data.Dataset)
assert info.features['label'].num_classes == 10
assert info.splits['train'].num_examples == 60000

# You can also access a builder directly
builder = tfds.builder("mnist")
assert builder.info.splits['train'].num_examples == 60000
builder.download_and_prepare()
datasets = builder.as_dataset()

# If you need NumPy arrays
np_datasets = tfds.as_numpy(datasets)
```

---

## All Datasets

*   [`audio`](#audio)
    *   [`"nsynth"`](#nsynth)
*   [`image`](#image)
    *   [`"cats_vs_dogs"`](#cats_vs_dogs)
    *   [`"celeb_a"`](#celeb_a)
    *   [`"celeb_a_hq"`](#celeb_a_hq)
    *   [`"chexpert"`](#chexpert)
    *   [`"cifar10"`](#cifar10)
    *   [`"cifar100"`](#cifar100)
    *   [`"coco2014"`](#coco2014)
    *   [`"colorectal_histology"`](#colorectal_histology)
    *   [`"colorectal_histology_large"`](#colorectal_histology_large)
    *   [`"diabetic_retinopathy_detection"`](#diabetic_retinopathy_detection)
    *   [`"fashion_mnist"`](#fashion_mnist)
    *   [`"horses_or_humans"`](#horses_or_humans)
    *   [`"image_label_folder"`](#image_label_folder)
    *   [`"imagenet2012"`](#imagenet2012)
    *   [`"lsun"`](#lsun)
    *   [`"mnist"`](#mnist)
    *   [`"omniglot"`](#omniglot)
    *   [`"open_images_v4"`](#open_images_v4)
    *   [`"quickdraw_bitmap"`](#quickdraw_bitmap)
    *   [`"rock_paper_scissors"`](#rock_paper_scissors)
    *   [`"svhn_cropped"`](#svhn_cropped)
    *   [`"tf_flowers"`](#tf_flowers)

*   [`structured`](#structured)

    *   [`"titanic"`](#titanic)

*   [`text`](#text)

    *   [`"imdb_reviews"`](#imdb_reviews)
    *   [`"lm1b"`](#lm1b)
    *   [`"squad"`](#squad)

*   [`translate`](#translate)

    *   [`"flores_translate_neen"`](#flores_translate_neen)
    *   [`"flores_translate_sien"`](#flores_translate_sien)
    *   [`"wmt_translate_ende"`](#wmt_translate_ende)
    *   [`"wmt_translate_enfr"`](#wmt_translate_enfr)

*   [`video`](#video)
{'fraction_non_alphanumeric': 0.20291777188328913, 'fraction_numerical': 0.019230769230769232, 'mean_word_length': 4.771683673469388, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7272294', 'n_tokens_mistral': 1952, 'n_tokens_neox': 1812, 'n_words': 204}