Struct PegasusTokenizer 

pub struct PegasusTokenizer { /* private fields */ }

Pegasus tokenizer

Pegasus tokenizer performing:

• text cleaning
• NFKC decomposition
• (optional) lower casing
• SentencePiece decomposition

Implementations

impl PegasusTokenizer

pub fn from_file<P: AsRef<Path>>( path: P, lower_case: bool, ) -> Result<PegasusTokenizer, TokenizerError>

Create a new instance of a PegasusTokenizer Expects a SentencePiece protobuf file as an input.

Parameters

• path (&str): path to the SentencePiece model file
• lower_case (bool): flag indicating if the text should be lower-cased as part of the tokenization

Example

use rust_tokenizers::tokenizer::{PegasusTokenizer, Tokenizer};
let lower_case = false;
let tokenizer = PegasusTokenizer::from_file("path/to/vocab/file", lower_case).unwrap();

pub fn from_file_with_special_token_mapping<P: AsRef<Path>, S: AsRef<Path>>( path: P, lower_case: bool, special_token_mapping_path: S, ) -> Result<PegasusTokenizer, TokenizerError>

Create a new instance of a PegasusTokenizer Expects a SentencePiece protobuf file and special token mapping file as inputs.

Parameters

• path (&str): path to the SentencePiece model file
• lower_case (bool): flag indicating if the text should be lower-cased as part of the tokenization
• special_token_mapping_path (&str): path to a special token mapping file to overwrite default special tokens

Example

use rust_tokenizers::tokenizer::{PegasusTokenizer, Tokenizer};
let lower_case = false;
let tokenizer = PegasusTokenizer::from_file_with_special_token_mapping(
    "path/to/vocab/file",
    lower_case,
    "path/to/special/token/mapping/file",
)
.unwrap();

pub fn from_existing_vocab_and_model( vocab: PegasusVocab, model: SentencePieceModel, lower_case: bool, ) -> PegasusTokenizer

Create a new instance of a PegasusTokenizer from an existing vocabulary and model

Parameters

• vocab (PegasusVocab): vocabulary
• model (SentencePieceModel): SentencePiece model
• lower_case (bool): flag indicating if the text should be lower-cased as part of the tokenization

Example

use rust_tokenizers::tokenizer::{PegasusTokenizer, Tokenizer};
use rust_tokenizers::vocab::{PegasusVocab, SentencePieceModel, Vocab};
let lower_case = false;
let vocab = PegasusVocab::from_file("path/to/vocab/file").unwrap();
let model = SentencePieceModel::from_file("path/to/model/file").unwrap();

let tokenizer = PegasusTokenizer::from_existing_vocab_and_model(vocab, model, lower_case);

Trait Implementations

impl MultiThreadedTokenizer<PegasusVocab> for PegasusTokenizer

fn vocab(&self) -> &T

returns a reference to the tokenizer vocabulary

fn tokenize_list_with_offsets<S>( &self, text_list: &[S], ) -> Vec<TokensWithOffsets>

where S: AsRef<str> + Sync,

Tokenize a list of strings (with multithreading), where each corresponds to for example a sentence, returns a vector of TokensWithOffsets containing the tokens and their offset information. This calls tokenize_with_offsets on the list provided.

fn tokenize_list<S>(&self, text_list: &[S]) -> Vec<Vec<String>>

where S: AsRef<str> + Sync,

Multithreaded tokenization of a list of strings, returning tokens with offset information

fn encode_list<S>( &self, text_list: &[S], max_len: usize, truncation_strategy: &TruncationStrategy, stride: usize, ) -> Vec<TokenizedInput>

where S: AsRef<str> + Sync,

Multithreaded encoding of a sequence of string-like texts (tokenization followed by encoding). Not that in contrast with encode optional second text, each text provided is encoded independently.

fn encode_pair_list<S>( &self, text_list: &[(S, S)], max_len: usize, truncation_strategy: &TruncationStrategy, stride: usize, ) -> Vec<TokenizedInput>

where S: AsRef<str> + Sync,

Multithreaded ncoding of a sequence of string-like text pairs (tokenization followed by encoding). This combines with encode with the list processing of encode_list.

fn decode_list( &self, token_ids_list: &[Vec<i64>], skip_special_tokens: bool, clean_up_tokenization_spaces: bool, ) -> Vec<String>

Multithreaded conversion a list of sequence of ids (integer) into a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. This calls decode for each provided sequence of ids

impl Tokenizer<PegasusVocab> for PegasusTokenizer

fn vocab(&self) -> &PegasusVocab

returns a reference to the tokenizer vocabulary

fn vocab_mut(&mut self) -> &mut PegasusVocab

returns a mutable reference to the tokenizer vocabulary

fn tokenize_to_tokens(&self, text: TokenRef<'_>) -> Vec<Token>

Tokenize a TokenRef, returning a sequence of tokens

fn convert_tokens_to_string(&self, tokens: Vec<String>) -> String

Converts a sequence of strings into a single string. This will clean-up artifacts from tokenization (for example sub ##word) and generate a single output string

fn build_input_with_special_tokens( &self, tokens_ids_with_offsets_1: TokenIdsWithOffsets, tokens_ids_with_offsets_2: Option<TokenIdsWithOffsets>, ) -> TokenIdsWithSpecialTokens

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens.

fn tokenize(&self, text: &str) -> Vec<String>

Tokenize a string, returns a vector of tokens as strings. Use tokenize_with_offsets or tokenize_to_tokens to return offset information.

fn tokenize_with_offsets(&self, text: &str) -> TokensWithOffsets

Tokenize a string, returning tokens with offset information

fn tokenize_list<S>(&self, text_list: &[S]) -> Vec<Vec<String>>

where S: AsRef<str>,

Tokenize a list of strings, returning tokens with offset information

fn tokenize_list_with_offsets<S>( &self, text_list: &[S], ) -> Vec<TokensWithOffsets>

where S: AsRef<str>,

Tokenize a list of strings, where each corresponds to for example a sentence, returns a vector of TokensWithOffsets containing the tokens and their offset information. This calls tokenize_with_offsets on the list provided.

fn convert_tokens_to_ids<S>(&self, tokens: &[S]) -> Vec<i64>

where S: AsRef<str>,

Convert a slice of string-like to a vector ot token indices

fn encode( &self, text_1: &str, text_2: Option<&str>, max_len: usize, truncation_strategy: &TruncationStrategy, stride: usize, ) -> TokenizedInput

Encode a string-like (tokenization followed by encoding)

fn encode_list<S>( &self, text_list: &[S], max_len: usize, truncation_strategy: &TruncationStrategy, stride: usize, ) -> Vec<TokenizedInput>

where S: AsRef<str>,

Encode a sequence of string-like texts (tokenization followed by encoding). Not that in contrast with encode optional second text, each text provided is encoded independently.

fn encode_pair_list<S>( &self, text_list: &[(S, S)], max_len: usize, truncation_strategy: &TruncationStrategy, stride: usize, ) -> Vec<TokenizedInput>

where S: AsRef<str>,

Encode a sequence of string-like text pairs (tokenization followed by encoding). This combines with encode with the list processing of encode_list.

fn decode_to_vec( &self, token_ids: &[i64], skip_special_tokens: bool, ) -> Vec<String>

Decode a sequence of token indices to a sequence of Strings, optionally skipping special indices

fn decode( &self, token_ids: &[i64], skip_special_tokens: bool, clean_up_tokenization_spaces: bool, ) -> String

Converts a sequence of ids (integer) into a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces.

fn clean_up_tokenization(&self, input_string: String) -> String

Cleans-up tokenization artifacts (for example whitespace before punctuation)

fn decode_list( &self, token_ids_list: &[Vec<i64>], skip_special_tokens: bool, clean_up_tokenization_spaces: bool, ) -> Vec<String>

Converts a list of sequence of ids (integer) into a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. This calls decode for each provided sequence of ids

fn add_tokens(&mut self, tokens: &[&str])

Add arbitrary tokens to the vocabulary.

fn add_extra_ids(&mut self, num_extra_ids: i64)

Add arbitrary tokens to the vocabulary.