tokenizers/pr_2003/en/api/models.md

# Models

## BPE[[tokenizers.models.BPE]]

#### tokenizers.models.BPE[[tokenizers.models.BPE]]

An implementation of the BPE (Byte-Pair Encoding) algorithm

Example:

```python
>>> from tokenizers.models import BPE
>>> # Build an empty model (to be trained)
>>> model = BPE(unk_token="<unk>")
>>> # Load from vocabulary and merges files
>>> model = BPE.from_file("vocab.json", "merges.txt")
```

from_filetokenizers.models.BPE.from_file[{"name": "vocab", "val": ""}, {"name": "merges", "val": ""}, {"name": "**kwargs", "val": ""}]- **vocab** (`str`) --
        The path to a `vocab.json` file

- **merges** (`str`) --
        The path to a `merges.txt` file0[BPE](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.BPE)An instance of BPE loaded from these files
Instantiate a BPE model from the given files.

This method is roughly equivalent to doing:

```python
vocab, merges = BPE.read_file(vocab_filename, merges_filename)
bpe = BPE(vocab, merges)
```

If you don't need to keep the `vocab, merges` values lying around,
this method is more optimized than manually calling
`read_file()` to initialize a [BPE](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.BPE)

**Parameters:**

vocab (`Dict[str, int]`, *optional*) : A dictionary of string keys and their ids `{"am": 0,...}` 

merges (`List[Tuple[str, str]]`, *optional*) : A list of pairs of tokens (`Tuple[str, str]`) `[("a", "b"),...]` 

cache_capacity (`int`, *optional*) : The number of words that the BPE cache can contain. The cache allows to speed-up the process by keeping the result of the merge operations for a number of words. 

dropout (`float`, *optional*) : A float between 0 and 1 that represents the BPE dropout to use. 

unk_token (`str`, *optional*) : The unknown token to be used by the model. 

continuing_subword_prefix (`str`, *optional*) : The prefix to attach to subword units that don't represent a beginning of word. 

end_of_word_suffix (`str`, *optional*) : The suffix to attach to subword units that represent an end of word. 

fuse_unk (`bool`, *optional*) : Whether to fuse any subsequent unknown tokens into a single one 

byte_fallback (`bool`, *optional*) : Whether to use spm byte-fallback trick (defaults to False) 

ignore_merges (`bool`, *optional*) : Whether or not to match tokens with the vocab before using merges.

**Returns:**

`[BPE](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.BPE)`

An instance of BPE loaded from these files
#### read_file[[tokenizers.models.BPE.read_file]]

Read a `vocab.json` and a `merges.txt` files

This method provides a way to read and parse the content of these files,
returning the relevant data structures. If you want to instantiate some BPE models
from memory, this method gives you the expected input from the standard files.

**Parameters:**

vocab (`str`) : The path to a `vocab.json` file 

merges (`str`) : The path to a `merges.txt` file

**Returns:**

`A `Tuple` with the vocab and the merges`

The vocabulary and merges loaded into memory

## Model[[tokenizers.models.Model]]

#### tokenizers.models.Model[[tokenizers.models.Model]]

Base class for all models

The model represents the actual tokenization algorithm. This is the part that
will contain and manage the learned vocabulary.

This class cannot be constructed directly. Please use one of the concrete models.

get_trainertokenizers.models.Model.get_trainer[]`Trainer`The Trainer used to train this model
Get the associated `Trainer`

Retrieve the `Trainer` associated to this
[Model](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.Model).

**Returns:**

``Trainer``

The Trainer used to train this model
#### id_to_token[[tokenizers.models.Model.id_to_token]]

Get the token associated to an ID

**Parameters:**

id (`int`) : An ID to convert to a token

**Returns:**

``str``

The token associated to the ID
#### save[[tokenizers.models.Model.save]]

Save the current model

Save the current model in the given folder, using the given prefix for the various
files that will get created.
Any file with the same name that already exists in this folder will be overwritten.

**Parameters:**

folder (`str`) : The path to the target folder in which to save the various files 

prefix (`str`, *optional*) : An optional prefix, used to prefix each file name

**Returns:**

``List[str]``

The list of saved files
#### token_to_id[[tokenizers.models.Model.token_to_id]]

Get the ID associated to a token

**Parameters:**

token (`str`) : A token to convert to an ID

**Returns:**

``int``

The ID associated to the token
#### tokenize[[tokenizers.models.Model.tokenize]]

Tokenize a sequence

**Parameters:**

sequence (`str`) : A sequence to tokenize

**Returns:**

`A `List` of `Token``

The generated tokens

## Unigram[[tokenizers.models.Unigram]]

#### tokenizers.models.Unigram[[tokenizers.models.Unigram]]

An implementation of the Unigram algorithm

The Unigram algorithm is a subword tokenization algorithm based on unigram language
models, as used in SentencePiece. It learns a vocabulary by starting with a large
initial vocabulary and iteratively pruning it using the EM algorithm.

Example:

```python
>>> from tokenizers.models import Unigram
>>> # Build an empty model (to be trained)
>>> model = Unigram()
>>> # Build from a vocabulary list
>>> vocab = [("<unk>", 0.0), ("hello", -1.0), ("world", -1.5)]
>>> model = Unigram(vocab=vocab, unk_id=0)
```

**Parameters:**

vocab (`List[Tuple[str, float]]`, *optional*) : A list of vocabulary items and their log-probability scores, e.g. `[("am", -0.2442), ...]`. If not provided, an empty model is created. 

unk_id (`int`, *optional*) : The index of the unknown token in the vocabulary list. 

byte_fallback (`bool`, *optional*, defaults to `False`) : Whether to use SentencePiece byte fallback for characters not in the vocabulary.

## WordLevel[[tokenizers.models.WordLevel]]

#### tokenizers.models.WordLevel[[tokenizers.models.WordLevel]]

An implementation of the WordLevel algorithm

Most simple tokenizer model based on mapping tokens to their corresponding id.

Example:

```python
>>> from tokenizers.models import WordLevel
>>> # Build from a vocabulary dictionary
>>> vocab = {"hello": 0, "world": 1, "<unk>": 2}
>>> model = WordLevel(vocab=vocab, unk_token="<unk>")
>>> # Load from file
>>> model = WordLevel.from_file("vocab.json", unk_token="<unk>")
```

from_filetokenizers.models.WordLevel.from_file[{"name": "vocab", "val": ""}, {"name": "unk_token", "val": " = None"}]- **vocab** (`str`) --
        The path to a `vocab.json` file0[WordLevel](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordLevel)An instance of WordLevel loaded from file
Instantiate a WordLevel model from the given file

This method is roughly equivalent to doing:

```python
vocab = WordLevel.read_file(vocab_filename)
wordlevel = WordLevel(vocab)
```

If you don't need to keep the `vocab` values lying around, this method is
more optimized than manually calling `read_file()` to
initialize a [WordLevel](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordLevel)

**Parameters:**

vocab (`str`, *optional*) : A dictionary of string keys and their ids `{"am": 0,...}` 

unk_token (`str`, *optional*) : The unknown token to be used by the model.

**Returns:**

`[WordLevel](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordLevel)`

An instance of WordLevel loaded from file
#### read_file[[tokenizers.models.WordLevel.read_file]]

Read a `vocab.json`

This method provides a way to read and parse the content of a vocabulary file,
returning the relevant data structures. If you want to instantiate some WordLevel models
from memory, this method gives you the expected input from the standard files.

**Parameters:**

vocab (`str`) : The path to a `vocab.json` file

**Returns:**

``Dict[str, int]``

The vocabulary as a `dict`

## WordPiece[[tokenizers.models.WordPiece]]

#### tokenizers.models.WordPiece[[tokenizers.models.WordPiece]]

An implementation of the WordPiece algorithm

Example:

```python
>>> from tokenizers.models import WordPiece
>>> # Build an empty model (to be trained)
>>> model = WordPiece(unk_token="[UNK]")
>>> # Load from a vocabulary file
>>> model = WordPiece.from_file("vocab.txt")
```

from_filetokenizers.models.WordPiece.from_file[{"name": "vocab", "val": ""}, {"name": "**kwargs", "val": ""}]- **vocab** (`str`) --
        The path to a `vocab.txt` file0[WordPiece](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordPiece)An instance of WordPiece loaded from file
Instantiate a WordPiece model from the given file

This method is roughly equivalent to doing:

```python
vocab = WordPiece.read_file(vocab_filename)
wordpiece = WordPiece(vocab)
```

If you don't need to keep the `vocab` values lying around, this method is
more optimized than manually calling `read_file()` to
initialize a [WordPiece](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordPiece)

**Parameters:**

vocab (`Dict[str, int]`, *optional*) : A dictionary of string keys and their ids `{"am": 0,...}` 

unk_token (`str`, *optional*) : The unknown token to be used by the model. 

max_input_chars_per_word (`int`, *optional*) : The maximum number of characters to authorize in a single word.

**Returns:**

`[WordPiece](/docs/tokenizers/pr_2003/en/api/models#tokenizers.models.WordPiece)`

An instance of WordPiece loaded from file
#### read_file[[tokenizers.models.WordPiece.read_file]]

Read a `vocab.txt` file

This method provides a way to read and parse the content of a standard *vocab.txt*
file as used by the WordPiece Model, returning the relevant data structures. If you
want to instantiate some WordPiece models from memory, this method gives you the
expected input from the standard files.

**Parameters:**

vocab (`str`) : The path to a `vocab.txt` file

**Returns:**

``Dict[str, int]``

The vocabulary as a `dict`

The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokenizers/latest/tokenizers/) website.

The node API has not been documented yet.