Encoding

Encoding

PythonRustNode

Encoding

class tokenizers.Encoding

( )

The Encoding represents the output of a Tokenizer.

propertyattention_mask

Returns

List[int]

The attention mask

The attention mask

This indicates to the LM which tokens should be attended to, and which should not. This is especially important when batching sequences, where we need to applying padding.

propertyids

Returns

List[int]

The list of IDs

The generated IDs

The IDs are the main input to a Language Model. They are the token indices, the numerical representations that a LM understands.

propertyn_sequences

Returns

int

The number of sequences in this Encoding

The number of sequences represented

propertyoffsets

Returns

A List of Tuple[int, int]

The list of offsets

The offsets associated to each token

These offsets let’s you slice the input string, and thus retrieve the original part that led to producing the corresponding token.

propertyoverflowing

A List of overflowing Encoding

When using truncation, the Tokenizer takes care of splitting the output into as many pieces as required to match the specified maximum length. This field lets you retrieve all the subsequent pieces.

When you use pairs of sequences, the overflowing pieces will contain enough variations to cover all the possible combinations, while respecting the provided maximum length.

propertysequence_ids

Returns

A List of Optional[int]

A list of optional sequence index.

The generated sequence indices.

They represent the index of the input sequence associated to each token. The sequence id can be None if the token is not related to any input sequence, like for example with special tokens.

propertyspecial_tokens_mask

Returns

List[int]

The special tokens mask

The special token mask

This indicates which tokens are special tokens, and which are not.

propertytokens

Returns

List[str]

The list of tokens

The generated tokens

They are the string representation of the IDs.

propertytype_ids

Returns

List[int]

The list of type ids

The generated type IDs

Generally used for tasks like sequence classification or question answering, these tokens let the LM know which input sequence corresponds to each tokens.

propertyword_ids

Returns

A List of Optional[int]

A list of optional word index.

The generated word indices.

They represent the index of the word associated to each token. When the input is pre-tokenized, they correspond to the ID of the given input label, otherwise they correspond to the words indices as defined by the PreTokenizer that was used.

For special tokens and such (any token that was generated from something that was not part of the input), the output is None

propertywords

Returns

A List of Optional[int]

A list of optional word index.

The generated word indices.

This is deprecated and will be removed in a future version. Please use ~tokenizers.Encoding.word_ids instead.

They represent the index of the word associated to each token. When the input is pre-tokenized, they correspond to the ID of the given input label, otherwise they correspond to the words indices as defined by the PreTokenizer that was used.

For special tokens and such (any token that was generated from something that was not part of the input), the output is None

char_to_token

( char_possequence_index = 0 ) → int

Parameters

• char_pos (int) — The position of a char in the input string

• sequence_index (int, defaults to 0) — The index of the sequence that contains the target char

Returns

int

The index of the token that contains this char in the encoded sequence

Get the token that contains the char at the given position in the input sequence.

char_to_word

( char_possequence_index = 0 ) → int

Parameters

• char_pos (int) — The position of a char in the input string

• sequence_index (int, defaults to 0) — The index of the sequence that contains the target char

Returns

int

The index of the word that contains this char in the input sequence

Get the word that contains the char at the given position in the input sequence.

merge

( encodingsgrowing_offsets = True ) → Encoding

Parameters

• encodings (A List of Encoding) — The list of encodings that should be merged in one

• growing_offsets (bool, defaults to True) — Whether the offsets should accumulate while merging

Returns

Encoding

The resulting Encoding

Merge the list of encodings into one final Encoding

pad

( lengthdirection = 'right'pad_id = 0pad_type_id = 0pad_token = '[PAD]' )

Parameters

• length (int) — The desired length

  direction — (str, defaults to right): The expected padding direction. Can be either right or left

• pad_id (int, defaults to 0) — The ID corresponding to the padding token

• pad_type_id (int, defaults to 0) — The type ID corresponding to the padding token

• pad_token (str, defaults to [PAD]) — The pad token to use

Pad the Encoding at the given length

set_sequence_id

( sequence_id )

Set the given sequence index

Set the given sequence index for the whole range of tokens contained in this Encoding.

token_to_chars

( token_index ) → Tuple[int, int]

Parameters

• token_index (int) — The index of a token in the encoded sequence.

Returns

Tuple[int, int]

The token offsets (first, last + 1)

Get the offsets of the token at the given index.

The returned offsets are related to the input sequence that contains the token. In order to determine in which input sequence it belongs, you must call ~tokenizers.Encoding.token_to_sequence().

token_to_sequence

( token_index ) → int

Parameters

• token_index (int) — The index of a token in the encoded sequence.

Returns

int

The sequence id of the given token

Get the index of the sequence represented by the given token.

In the general use case, this method returns 0 for a single sequence or the first sequence of a pair, and 1 for the second sequence of a pair

token_to_word

( token_index ) → int

Parameters

• token_index (int) — The index of a token in the encoded sequence.

Returns

int

The index of the word in the relevant input sequence.

Get the index of the word that contains the token in one of the input sequences.

The returned word index is related to the input sequence that contains the token. In order to determine in which input sequence it belongs, you must call ~tokenizers.Encoding.token_to_sequence().

truncate

( max_lengthstride = 0direction = 'right' )

Parameters

• max_length (int) — The desired length

• stride (int, defaults to 0) — The length of previous content to be included in each overflowing piece

• direction (str, defaults to right) — Truncate direction

Truncate the Encoding at the given length

If this Encoding represents multiple sequences, when truncating this information is lost. It will be considered as representing a single sequence.

word_to_chars

( word_indexsequence_index = 0 ) → Tuple[int, int]

Parameters

• word_index (int) — The index of a word in one of the input sequences.

• sequence_index (int, defaults to 0) — The index of the sequence that contains the target word

Returns

Tuple[int, int]

The range of characters (span) (first, last + 1)

Get the offsets of the word at the given index in one of the input sequences.

word_to_tokens

( word_indexsequence_index = 0 ) → Tuple[int, int]

Parameters

• word_index (int) — The index of a word in one of the input sequences.

• sequence_index (int, defaults to 0) — The index of the sequence that contains the target word

Returns

Tuple[int, int]

The range of tokens: (first, last + 1)

Get the encoded tokens corresponding to the word at the given index in one of the input sequences.