Padding and truncation

Batched inputs are often different lengths, so they can’t be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special padding token to ensure shorter sequences will have the same length as either the longest sequence in a batch or the maximum length accepted by the model. Truncation works in the other direction by truncating long sequences.

In most cases, padding your batch to the length of the longest sequence and truncating to the maximum length a model can accept works pretty well. However, the API supports more strategies if you need them. The three arguments you need to know are: padding, truncation and max_length.

The padding argument controls padding. It can be a boolean or a string:

• True or 'longest': pad to the longest sequence in the batch (no padding is applied if you only provide a single sequence).
• 'max_length': pad to a length specified by the max_length argument or the maximum length accepted by the model if no max_length is provided (max_length=None). Padding will still be applied if you only provide a single sequence.
• False or 'do_not_pad': no padding is applied. This is the default behavior.

The truncation argument controls truncation. It can be a boolean or a string:

• True or 'longest_first': truncate to a maximum length specified by the max_length argument or the maximum length accepted by the model if no max_length is provided (max_length=None). This will truncate token by token, removing a token from the longest sequence in the pair until the proper length is reached.
• 'only_second': truncate to a maximum length specified by the max_length argument or the maximum length accepted by the model if no max_length is provided (max_length=None). This will only truncate the second sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
• 'only_first': truncate to a maximum length specified by the max_length argument or the maximum length accepted by the model if no max_length is provided (max_length=None). This will only truncate the first sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
• False or 'do_not_truncate': no truncation is applied. This is the default behavior.

The max_length argument controls the length of the padding and truncation. It can be an integer or None, in which case it will default to the maximum length the model can accept. If the model has no specific maximum input length, truncation or padding to max_length is deactivated.

The following table summarizes the recommended way to setup padding and truncation. If you use pairs of input sequences in any of the following examples, you can replace truncation=True by a STRATEGY selected in ['only_first', 'only_second', 'longest_first'], i.e. truncation='only_second' or truncation='longest_first' to control how both sequences in the pair are truncated as detailed before.

Truncation	Padding	Instruction
no truncation	no padding	tokenizer(batch_sentences)
	padding to max sequence in batch	tokenizer(batch_sentences, padding=True) or
		tokenizer(batch_sentences, padding='longest')
	padding to max model input length	tokenizer(batch_sentences, padding='max_length')
	padding to specific length	tokenizer(batch_sentences, padding='max_length', max_length=42)
	padding to a multiple of a value	tokenizer(batch_sentences, padding=True, pad_to_multiple_of=8)
truncation to max model input length	no padding	tokenizer(batch_sentences, truncation=True) or
		tokenizer(batch_sentences, truncation=STRATEGY)
	padding to max sequence in batch	tokenizer(batch_sentences, padding=True, truncation=True) or
		tokenizer(batch_sentences, padding=True, truncation=STRATEGY)
	padding to max model input length	tokenizer(batch_sentences, padding='max_length', truncation=True) or
		tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)
	padding to specific length	Not possible
truncation to specific length	no padding	tokenizer(batch_sentences, truncation=True, max_length=42) or
		tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)
	padding to max sequence in batch	tokenizer(batch_sentences, padding=True, truncation=True, max_length=42) or
		tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)
	padding to max model input length	Not possible
	padding to specific length	tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42) or
		tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)

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