tokenizer: Tokenizer

In tok: Fast Text Tokenization

Tokenizer

Description

A Tokenizer works as a pipeline. It processes some raw text as input and outputs an encoding.

Value

A tokenizer that can be used for encoding character strings or decoding integers.

Public fields

.tokenizer

(unsafe usage) Lower level pointer to tokenizer

Active bindings

pre_tokenizer

instance of the pre-tokenizer

normalizer

Gets the normalizer instance

post_processor

Gets the post processor used by tokenizer

decoder

Gets and sets the decoder

padding

Gets padding configuration

truncation

Gets truncation configuration

Methods

Public methods

• tokenizer$new()

• tokenizer$encode()

• tokenizer$decode()

• tokenizer$encode_batch()

• tokenizer$decode_batch()

• tokenizer$from_file()

• tokenizer$from_pretrained()

• tokenizer$train()

• tokenizer$train_from_memory()

• tokenizer$save()

• tokenizer$enable_padding()

• tokenizer$no_padding()

• tokenizer$enable_truncation()

• tokenizer$no_truncation()

• tokenizer$get_vocab_size()

• tokenizer$clone()

---

Method new()

Initializes a tokenizer

Usage

tokenizer$new(tokenizer)

Arguments

tokenizer

Will be cloned to initialize a new tokenizer

---

Method encode()

Encode the given sequence and pair. This method can process raw text sequences as well as already pre-tokenized sequences.

Usage

tokenizer$encode(
  sequence,
  pair = NULL,
  is_pretokenized = FALSE,
  add_special_tokens = TRUE
)

Arguments

sequence

The main input sequence we want to encode. This sequence can be either raw text or pre-tokenized, according to the is_pretokenized argument

pair

An optional input sequence. The expected format is the same that for sequence.

is_pretokenized

Whether the input is already pre-tokenized

add_special_tokens

Whether to add the special tokens

---

Method decode()

Decode the given list of ids back to a string

Usage

tokenizer$decode(ids, skip_special_tokens = TRUE)

Arguments

ids

The list of ids that we want to decode

skip_special_tokens

Whether the special tokens should be removed from the decoded string

---

Method encode_batch()

Encodes a batch of sequences. Returns a list of encodings.

Usage

tokenizer$encode_batch(
  input,
  is_pretokenized = FALSE,
  add_special_tokens = TRUE
)

Arguments

input

A list of single sequences or pair sequences to encode. Each sequence can be either raw text or pre-tokenized, according to the is_pretokenized argument.

is_pretokenized

Whether the input is already pre-tokenized

add_special_tokens

Whether to add the special tokens

---

Method decode_batch()

Decode a batch of ids back to their corresponding string

Usage

tokenizer$decode_batch(sequences, skip_special_tokens = TRUE)

Arguments

sequences

The batch of sequences we want to decode

skip_special_tokens

Whether the special tokens should be removed from the decoded strings

---

Method from_file()

Creates a tokenizer from the path of a serialized tokenizer. This is a static method and should be called instead of ⁠$new⁠ when initializing the tokenizer.

Usage

tokenizer$from_file(path)

Arguments

path

Path to tokenizer.json file

---

Method from_pretrained()

Instantiate a new Tokenizer from an existing file on the Hugging Face Hub.

Usage

tokenizer$from_pretrained(identifier, revision = "main", auth_token = NULL)

Arguments

identifier

The identifier of a Model on the Hugging Face Hub, that contains a tokenizer.json file

revision

A branch or commit id

auth_token

An optional auth token used to access private repositories on the Hugging Face Hub

---

Method train()

Train the Tokenizer using the given files. Reads the files line by line, while keeping all the whitespace, even new lines.

Usage

tokenizer$train(files, trainer)

Arguments

files

character vector of file paths.

trainer

an instance of a trainer object, specific to that tokenizer type.

---

Method train_from_memory()

Train the tokenizer on a chracter vector of texts

Usage

tokenizer$train_from_memory(texts, trainer)

Arguments

texts

a character vector of texts.

trainer

an instance of a trainer object, specific to that tokenizer type.

---

Method save()

Saves the tokenizer to a json file

Usage

tokenizer$save(path, pretty = TRUE)

Arguments

path

A path to a file in which to save the serialized tokenizer.

pretty

Whether the JSON file should be pretty formatted.

---

Method enable_padding()

Enables padding for the tokenizer

Usage

tokenizer$enable_padding(
  direction = "right",
  pad_id = 0L,
  pad_type_id = 0L,
  pad_token = "[PAD]",
  length = NULL,
  pad_to_multiple_of = NULL
)

Arguments

direction

(str, optional, defaults to right) — The direction in which to pad. Can be either 'right' or 'left'

pad_id

(int, defaults to 0) — The id to be used when padding

pad_type_id

(int, defaults to 0) — The type id to be used when padding

pad_token

(str, defaults to '[PAD]') — The pad token to be used when padding

length

(int, optional) — If specified, the length at which to pad. If not specified we pad using the size of the longest sequence in a batch.

pad_to_multiple_of

(int, optional) — If specified, the padding length should always snap to the next multiple of the given value. For example if we were going to pad with a length of 250 but pad_to_multiple_of=8 then we will pad to 256.

---

Method no_padding()

Disables padding

Usage

tokenizer$no_padding()

---

Method enable_truncation()

Enables truncation on the tokenizer

Usage

tokenizer$enable_truncation(
  max_length,
  stride = 0,
  strategy = "longest_first",
  direction = "right"
)

Arguments

max_length

The maximum length at which to truncate.

stride

The length of the previous first sequence to be included in the overflowing sequence. Default: 0.

strategy

The strategy used for truncation. Can be one of: "longest_first", "only_first", or "only_second". Default: "longest_first".

direction

The truncation direction. Default: "right".

---

Method no_truncation()

Disables truncation

Usage

tokenizer$no_truncation()

---

Method get_vocab_size()

Gets the vocabulary size

Usage

tokenizer$get_vocab_size(with_added_tokens = TRUE)

Arguments

with_added_tokens

Wether to count added tokens

---

Method clone()

The objects of this class are cloneable with this method.

Usage

tokenizer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

withr::with_envvar(c(HUGGINGFACE_HUB_CACHE = tempdir()), {
try({
tok <- tokenizer$from_pretrained("gpt2")
tok$encode("Hello world")$ids
})
})

tok documentation built on Sept. 11, 2024, 5:21 p.m.