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Process

🤗 Datasets provides many tools for modifying the structure and content of a dataset. You can rearrange the order of rows or extract nested fields into their own columns. For more powerful processing applications, you can even alter the contents of a dataset by applying a function to the entire dataset to generate new rows and columns. These processing methods provide a lot of control and flexibility to mold your dataset into the desired shape and size with the appropriate features.

This guide will show you how to:

  • Reorder rows and split the dataset.
  • Rename and remove columns, and other common column operations.
  • Apply processing functions to each example in a dataset.
  • Concatenate datasets.
  • Apply a custom formatting transform.
  • Save and export processed datasets.

Load the MRPC dataset from the GLUE benchmark to follow along with our examples:

>>> from datasets import load_dataset
>>> dataset = load_dataset('glue', 'mrpc', split='train')

All the processing methods in this guide return a new Dataset. Modification is not done in-place. Be careful about overriding your previous dataset!

Sort, shuffle, select, split, and shard

There are several methods for rearranging the structure of a dataset. These methods are useful for selecting only the rows you want, creating train and test splits, and sharding very large datasets into smaller chunks.

Sort

Use Dataset.sort() to sort a columns values according to their numerical values. The provided column must be NumPy compatible.

>>> dataset['label'][:10]
[1, 0, 1, 0, 1, 1, 0, 1, 0, 0]
>>> sorted_dataset = dataset.sort('label')
>>> sorted_dataset['label'][:10]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
>>> sorted_dataset['label'][-10:]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

Shuffle

The Dataset.shuffle() method randomly rearranges the values of a column. You can specify the generator argument in this method to use a different numpy.random.Generator if you want more control over the algorithm used to shuffle the dataset.

>>> shuffled_dataset = sorted_dataset.shuffle(seed=42)
>>> shuffled_dataset['label'][:10]
[1, 1, 1, 0, 1, 1, 1, 1, 1, 0]

Select and Filter

There are two options for filtering rows in a dataset: Dataset.select() and Dataset.filter().

>>> small_dataset = dataset.select([0, 10, 20, 30, 40, 50])
>>> len(small_dataset)
6
>>> start_with_ar = dataset.filter(lambda example: example['sentence1'].startswith('Ar'))
>>> len(start_with_ar)
6
>>> start_with_ar['sentence1']
['Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .',
'Arison said Mann may have been one of the pioneers of the world music movement and he had a deep love of Brazilian music .',
'Arts helped coach the youth on an eighth-grade football team at Lombardi Middle School in Green Bay .',
'Around 9 : 00 a.m. EDT ( 1300 GMT ) , the euro was at $ 1.1566 against the dollar , up 0.07 percent on the day .',
"Arguing that the case was an isolated example , Canada has threatened a trade backlash if Tokyo 's ban is not justified on scientific grounds .",
'Artists are worried the plan would harm those who need help most - performers who have a difficult time lining up shows .'
]

Dataset.filter() can also filter by indices if you set with_indices=True:

>>> even_dataset = dataset.filter(lambda example, idx: idx % 2 == 0, with_indices=True)
>>> len(even_dataset)
1834
>>> len(dataset) / 2
1834.0

Split

Dataset.train_test_split() creates train and test splits, if your dataset doesn’t already have them. This allows you to adjust the relative proportions or absolute number of samples in each split. In the example below, use the test_size argument to create a test split that is 10% of the original dataset:

>>> dataset.train_test_split(test_size=0.1)
{'train': Dataset(schema: {'sentence1': 'string', 'sentence2': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 3301),
'test': Dataset(schema: {'sentence1': 'string', 'sentence2': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 367)}
>>> 0.1 * len(dataset)
366.8

The splits are shuffled by default, but you can set shuffle=False to prevent shuffling.

Shard

🤗 Datasets supports sharding to divide a very large dataset into a predefined number of chunks. Specify the num_shards argument in Dataset.shard() to determine the number of shards to split the dataset into. You will also need to provide the shard you want to return with the index argument.

For example, the imdb dataset has 25000 examples:

>>> from datasets import load_dataset
>>> datasets = load_dataset('imdb', split='train')
>>> print(dataset)
Dataset({
    features: ['text', 'label'],
    num_rows: 25000
})

After you shard the dataset into four chunks, the first shard only has 6250 examples:

>>> dataset.shard(num_shards=4, index=0)
Dataset({
    features: ['text', 'label'],
    num_rows: 6250
})
>>> print(25000/4)
6250.0

Rename, remove, cast, and flatten

The following methods allow you to modify the columns of a dataset. These methods are useful for renaming or removing columns, changing columns to a new set of features, and flattening nested column structures.

Rename

Use Dataset.rename_column() when you need to rename a column in your dataset. Features associated with the original column are actually moved under the new column name, instead of just replacing the original column in-place.

Provide Dataset.rename_column() with the name of the original column, and the new column name:

>>> dataset
Dataset({
    features: ['sentence1', 'sentence2', 'label', 'idx'],
    num_rows: 3668
})
>>> dataset = dataset.rename_column("sentence1", "sentenceA")
>>> dataset = dataset.rename_column("sentence2", "sentenceB")
>>> dataset
Dataset({
    features: ['sentenceA', 'sentenceB', 'label', 'idx'],
    num_rows: 3668
})

Remove

When you need to remove one or more columns, give Dataset.remove_columns() the name of the column to remove. Remove more than one column by providing a list of column names:

>>> dataset = dataset.remove_columns("label")
>>> dataset
Dataset({
    features: ['sentence1', 'sentence2', 'idx'],
    num_rows: 3668
})
>>> dataset = dataset.remove_columns(['sentence1', 'sentence2'])
>>> dataset
Dataset({
    features: ['idx'],
    num_rows: 3668
})

Cast

Dataset.cast() changes the feature type of one or more columns. This method takes your new Features as its argument. The following sample code shows how to change the feature types of ClassLabel and Value:

>>> dataset.features
{'sentence1': Value(dtype='string', id=None),
'sentence2': Value(dtype='string', id=None),
'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),
'idx': Value(dtype='int32', id=None)}

>>> from datasets import ClassLabel, Value
>>> new_features = dataset.features.copy()
>>> new_features["label"] = ClassLabel(names=['negative', 'positive'])
>>> new_features["idx"] = Value('int64')
>>> dataset = dataset.cast(new_features)
>>> dataset.features
{'sentence1': Value(dtype='string', id=None),
'sentence2': Value(dtype='string', id=None),
'label': ClassLabel(num_classes=2, names=['negative', 'positive'], names_file=None, id=None),
'idx': Value(dtype='int64', id=None)}

Casting only works if the original feature type and new feature type are compatible. For example, you can cast a column with the feature type Value('int32') to Value('bool') if the original column only contains ones and zeros.

Use Dataset.cast_column() to change the feature type of just one column. Pass the column name and its new feature type as arguments:

>>> dataset.features
{'audio': Audio(sampling_rate=44100, mono=True, id=None)}

>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> dataset.features
{'audio': Audio(sampling_rate=16000, mono=True, id=None)}

Flatten

Sometimes a column can be a nested structure of several types. Use Dataset.flatten() to extract the subfields into their own separate columns. Take a look at the nested structure below from the SQuAD dataset:

>>> from datasets import load_dataset
>>> dataset = load_dataset('squad', split='train')
>>> dataset.features
{'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None),
'context': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None),
'question': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None)}

The answers field contains two subfields: text and answer_start. Flatten them with Dataset.flatten():

>>> flat_dataset = dataset.flatten()
>>> flat_dataset
Dataset({
    features: ['id', 'title', 'context', 'question', 'answers.text', 'answers.answer_start'],
 num_rows: 87599
})

Notice how the subfields are now their own independent columns: answers.text and answers.answer_start.

Align

The Dataset.align_labels_with_mapping() function aligns a dataset label id with the label name. Not all 🤗 Transformers models follow the prescribed label mapping of the original dataset, especially for NLI dataset labels. For example, the MNLI dataset uses the following label mapping:

>>> label2id = {'entailment': 0, 'neutral': 1, 'contradiction': 2}

To align the dataset label mapping with the mapping used by a model, create a dictionary of the label name and id to align on:

>>> label2id = {"contradiction": 0, "neutral": 1, "entailment": 2}

Pass the dictionary of the label mappings to the Dataset.align_labels_with_mapping() function, and the column to align on:

>>> from datasets import load_dataset

>>> mnli = load_dataset("glue", "mnli", split="train")
>>> mnli_aligned = mnli.align_labels_with_mapping(label2id, "label")

You can also use this function to assign a custom mapping of labels to ids.

Map

Some of the more powerful applications of 🤗 Datasets come from using Dataset.map(). The primary purpose of Dataset.map() is to speed up processing functions. It allows you to apply a processing function to each example in a dataset, independently or in batches. This function can even create new rows and columns.

In the following example, you will prefix each sentence1 value in the dataset with 'My sentence: '. First, create a function that adds 'My sentence: ' to the beginning of each sentence. The function needs to accept and output a dict:

>>> def add_prefix(example):
...     example['sentence1'] = 'My sentence: ' + example['sentence1']
...     return example

Next, apply this function to the dataset with Dataset.map():

>>> updated_dataset = small_dataset.map(add_prefix)
>>> updated_dataset['sentence1'][:5]
['My sentence: Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
"My sentence: Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .",
'My sentence: They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .',
'My sentence: Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .',
]

Let’s take a look at another example, except this time, you will remove a column with Dataset.map(). When you remove a column, it is only removed after the example has been provided to the mapped function. This allows the mapped function to use the content of the columns before they are removed.

Specify the column to remove with the remove_columns argument in Dataset.map():

>>> updated_dataset = dataset.map(lambda example: {'new_sentence': example['sentence1']}, remove_columns=['sentence1'])
>>> updated_dataset.column_names
['sentence2', 'label', 'idx', 'new_sentence']

🤗 Datasets also has a Dataset.remove_columns() method that is functionally identical, but faster, because it doesn’t copy the data of the remaining columns.

You can also use Dataset.map() with indices if you set with_indices=True. The example below adds the index to the beginning of each sentence:

>>> updated_dataset = dataset.map(lambda example, idx: {'sentence2': f'{idx}: ' + example['sentence2']}, with_indices=True)
>>> updated_dataset['sentence2'][:5]
['0: Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
 "1: Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .",
 "2: On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .",
 '3: Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .',
 '4: PG & E Corp. shares jumped $ 1.63 or 8 percent to $ 21.03 on the New York Stock Exchange on Friday .'
]

You can also use Dataset.map() with the rank of the process if you set with_rank=True. This is analogous to with_indices. The rank argument in the mapped function goes after the index one if it is already present. The main use-case for it is to parallelize your computation across several GPUs. This requires setting multiprocess.set_start_method("spawn"), without which you will receive a CUDA error: RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method.

>>> from multiprocess import set_start_method
>>> import torch
>>> import os
>>>
>>> set_start_method("spawn")
>>>
>>> def gpu_computation(example, rank):
>>>     os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())
>>>     # Your big GPU call goes here
>>>     return examples
>>>
>>> updated_dataset = dataset.map(gpu_computation, with_rank=True)

Multiprocessing

Multiprocessing can significantly speed up processing by parallelizing the processes on your CPU. Set the num_proc argument in Dataset.map() to set the number of processes to use:

>>> updated_dataset = dataset.map(lambda example, idx: {'sentence2': f'{idx}: ' + example['sentence2']}, num_proc=4)

Batch processing

Dataset.map() also supports working with batches of examples. Operate on batches by setting batched=True. The default batch size is 1000, but you can adjust it with the batch_size argument. This opens the door to many interesting applications such as tokenization, splitting long sentences into shorter chunks, and data augmentation.

Tokenization

One of the most obvious use-cases for batch processing is tokenization, which accepts batches of inputs.

First, load the tokenizer from the BERT model:

>>> from transformers import BertTokenizerFast
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')

Apply the tokenizer to batches of the sentence1 field:

>>> encoded_dataset = dataset.map(lambda examples: tokenizer(examples['sentence1']), batched=True)
>>> encoded_dataset.column_names
['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask']
>>> encoded_dataset[0]
{'sentence1': 'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
'sentence2': 'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
'label': 1,
'idx': 0,
'input_ids': [  101,  7277,  2180,  5303,  4806,  1117,  1711,   117,  2292, 1119,  1270,   107,  1103,  7737,   107,   117,  1104,  9938, 4267, 12223, 21811,  1117,  2554,   119,   102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}

Now you have three new columns, input_ids, token_type_ids, attention_mask, that contain the encoded version of the sentence1 field.

Split long examples

When your examples are too long, you may want to split them into several smaller snippets. Begin by creating a function that:

  1. Splits the sentence1 field into snippets of 50 characters.

  2. Stacks all the snippets together to create the new dataset.

>>> def chunk_examples(examples):
...     chunks = []
...     for sentence in examples['sentence1']:
...         chunks += [sentence[i:i + 50] for i in range(0, len(sentence), 50)]
...     return {'chunks': chunks}

Apply the function with Dataset.map():

>>> chunked_dataset = dataset.map(chunk_examples, batched=True, remove_columns=dataset.column_names)
>>> chunked_dataset[:10]
{'chunks': ['Amrozi accused his brother , whom he called " the ',
            'witness " , of deliberately distorting his evidenc',
            'e .',
            "Yucaipa owned Dominick 's before selling the chain",
            ' to Safeway in 1998 for $ 2.5 billion .',
            'They had published an advertisement on the Interne',
            't on June 10 , offering the cargo for sale , he ad',
            'ded .',
            'Around 0335 GMT , Tab shares were up 19 cents , or',
            ' 4.4 % , at A $ 4.56 , having earlier set a record']}

Notice how the sentences are split into shorter chunks now, and there are more rows in the dataset.

>>> dataset
Dataset({
 features: ['sentence1', 'sentence2', 'label', 'idx'],
 num_rows: 3668
})
>>> chunked_dataset
Dataset(schema: {'chunks': 'string'}, num_rows: 10470)

Data augmentation

With batch processing, you can even augment your dataset with additional examples. In the following example, you will generate additional words for a masked token in a sentence.

Load the RoBERTA model for use in the 🤗 Transformer FillMaskPipeline:

>>> from random import randint
>>> from transformers import pipeline

>>> fillmask = pipeline('fill-mask', model='roberta-base')
>>> mask_token = fillmask.tokenizer.mask_token
>>> smaller_dataset = dataset.filter(lambda e, i: i<100, with_indices=True)

Create a function to randomly select a word to mask in the sentence. The function should also return the original sentence and the top two replacements generated by RoBERTA.

>>> def augment_data(examples):
...     outputs = []
...     for sentence in examples['sentence1']:
...         words = sentence.split(' ')
...         K = randint(1, len(words)-1)
...         masked_sentence = " ".join(words[:K]  + [mask_token] + words[K+1:])
...         predictions = fillmask(masked_sentence)
...         augmented_sequences = [predictions[i]['sequence'] for i in range(3)]
...         outputs += [sentence] + augmented_sequences
...
...     return {'data': outputs}

Use Dataset.map() to apply the function over the whole dataset:

>>> augmented_dataset = smaller_dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, batch_size=8)
>>> augmented_dataset[:9]['data']
['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
 'Amrozi accused his brother, whom he called " the witness ", of deliberately withholding his evidence.',
 'Amrozi accused his brother, whom he called " the witness ", of deliberately suppressing his evidence.',
 'Amrozi accused his brother, whom he called " the witness ", of deliberately destroying his evidence.',
 "Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .",
 'Yucaipa owned Dominick Stores before selling the chain to Safeway in 1998 for $ 2.5 billion.',
 "Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
 'Yucaipa owned Dominick Pizza before selling the chain to Safeway in 1998 for $ 2.5 billion.'
]

For each original sentence, RoBERTA augmented a random word with three alternatives. In the first sentence, the word distorting is augmented with withholding, suppressing, and destroying.

Process multiple splits

Many datasets have splits that you can process simultaneously with DatasetDict.map(). For example, tokenize the sentence1 field in the train and test split by:

>>> from datasets import load_dataset

# load all the splits
>>> dataset = load_dataset('glue', 'mrpc')
>>> encoded_dataset = dataset.map(lambda examples: tokenizer(examples['sentence1']), batched=True)
>>> encoded_dataset["train"][0]
{'sentence1': 'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
'sentence2': 'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
'label': 1,
'idx': 0,
'input_ids': [  101,  7277,  2180,  5303,  4806,  1117,  1711,   117,  2292, 1119,  1270,   107,  1103,  7737,   107,   117,  1104,  9938, 4267, 12223, 21811,  1117,  2554,   119,   102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}

Distributed usage

When you use Dataset.map() in a distributed setting, you should also use torch.distributed.barrier. This ensures the main process performs the mapping, while the other processes load the results, thereby avoiding duplicate work.

The following example shows how you can use torch.distributed.barrier to synchronize the processes:

>>> from datasets import Dataset
>>> import torch.distributed

>>> dataset1 = Dataset.from_dict({"a": [0, 1, 2]})

>>> if training_args.local_rank > 0:
...     print("Waiting for main process to perform the mapping")
...     torch.distributed.barrier()

>>> dataset2 = dataset1.map(lambda x: {"a": x["a"] + 1})

>>> if training_args.local_rank == 0:
...     print("Loading results from main process")
...     torch.distributed.barrier()

Concatenate

Separate datasets can be concatenated if they share the same column types. Concatenate datasets with concatenate_datasets():

>>> from datasets import concatenate_datasets, load_dataset

>>> bookcorpus = load_dataset("bookcorpus", split="train")
>>> wiki = load_dataset("wikipedia", "20220301.en", split="train")
>>> wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"])  # only keep the 'text' column

>>> assert bookcorpus.features.type == wiki.features.type
>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])

You can also mix several datasets together by taking alternating examples from each one to create a new dataset. This is known as interleaving, and you can use it with interleave_datasets(). Both interleave_datasets() and concatenate_datasets() will work with regular Dataset and IterableDataset objects. Refer to the Stream section for an example of how it’s used.

You can also concatenate two datasets horizontally (axis=1) as long as they have the same number of rows:

>>> from datasets import Dataset
>>> bookcorpus_ids = Dataset.from_dict({"ids": list(range(len(bookcorpus)))})
>>> bookcorpus_with_ids = concatenate_datasets([bookcorpus, bookcorpus_ids], axis=1)

Format

Set a dataset to a TensorFlow compatible format with Dataset.set_format(). Specify type=tensorflow and the columns that should be formatted:

>>> import tensorflow as tf
>>> dataset.set_format(type='tensorflow', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])

Then you can wrap the dataset with tf.data.Dataset. This method gives you more control over how to create a TensorFlow Dataset. In the example below, the dataset is created from_tensor_slices:

>>> tfdataset = tf.data.Dataset.from_tensor_slices((features, dataset["label"])).batch(32)

Dataset.with_format() provides an alternative method to set the format. This method will return a new Dataset object with your specified format:

>>> dataset = dataset.with_format(type='tensorflow', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])

🤗 Datasets also provides support for other common data formats such as NumPy, PyTorch, Pandas, and JAX.

Use Dataset.reset_format() if you need to reset the dataset to the original format:

>>> dataset.format
{'type': 'tensorflow', 'format_kwargs': {}, 'columns': ['label'], 'output_all_columns': False}
>>> dataset.reset_format()
>>> dataset.format
{'type': 'python', 'format_kwargs': {}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}

Format transform

Dataset.set_transform() allows you to apply a custom formatting transform on-the-fly. This will replace any previously specified format. For example, you can use this method to tokenize and pad tokens on-the-fly:

>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
>>> def encode(batch):
...     return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt")
>>> dataset.set_transform(encode)
>>> dataset.format
{'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}
>>> dataset[:2]
{'input_ids': tensor([[  101,  2572,  3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}

In this case, the tokenization is applied only when the examples are accessed.

Save

Once you are done processing your dataset, you can save and reuse it later with Dataset.save_to_disk().

Save your dataset by providing the path to the directory you wish to save it to:

>>> encoded_dataset.save_to_disk("path/of/my/dataset/directory")

When you want to use your dataset again, use load_from_disk() to reload it:

>>> from datasets import load_from_disk
>>> reloaded_encoded_dataset = load_from_disk("path/of/my/dataset/directory")

Want to save your dataset to a cloud storage provider? Read our Cloud Storage guide on how to save your dataset to AWS or Google Cloud Storage!

Export

🤗 Datasets supports exporting as well, so you can work with your dataset in other applications. The following table shows currently supported file formats you can export to:

File type Export method
CSV Dataset.to_csv()
JSON Dataset.to_json()
Parquet Dataset.to_parquet()
In-memory Python object Dataset.to_pandas() or Dataset.to_dict()

For example, export your dataset to a CSV file like this:

>>> encoded_dataset.to_csv("path/of/my/dataset.csv")