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Unsupervised text tokenizer focused on computational efficiency
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README.md

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YouTokenToMe

YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE) [Sennrich et al.]. Our implementation is much faster in training and tokenization than both fastBPE and SentencePiece. In some test cases, it is 90 times faster. Check out our benchmark results.

Key advantages:

  • Multithreading for training and tokenization
  • The algorithm has O(N) complexity, where N is the length of training data
  • Highly efficient implementation in C++
  • Python wrapper and command-line interface

As well as in the algorithm from the original paper, ours does not consider tokens that cross word boundaries. Just like in SentencePiece, all space symbols were replaced by meta symbol "▁" (U+2581). It allows sequences of tokens to be converted back to text and for word boundaries to be restored.

For example, the phrase Blazingly fast tokenization! can be tokenized into

['▁Bl', 'az', 'ingly', '▁fast', '▁token', 'ization', '!']

Installation

pip install youtokentome

Python interface

Example

Let's start with a self-contained example.

import random

import youtokentome as yttm

train_data_path = "train_data.txt"
model_path = "example.model"

# Generating random file with training data
# 10000 lines with 100 characters in each line
n_lines = 10000
n_characters = 100
with open(train_data_path, "w") as fout:
    for _ in range(n_lines):
        print("".join([random.choice("abcd ") for _ in range(n_characters)]), file=fout)

# Generating random text
test_text = "".join([random.choice("abcde ") for _ in range(100)])

# Training model
yttm.BPE.train(data=train_data_path, vocab_size=5000, model=model_path)

# Loading model
bpe = yttm.BPE(model=model_path)

# Two types of tokenization
print(bpe.encode([test_text], output_type=yttm.OutputType.ID))
print(bpe.encode([test_text], output_type=yttm.OutputType.SUBWORD))

 

Training model

youtokentome.BPE.train(data, model, vocab_size, coverage, n_threads=-1, pad_id=0, unk_id=1, bos_id=2, eos_id=3)

Trains BPE model and saves to file.

Args:

  • data: string, path to file with training data
  • model: string, path to where the trained model will be saved
  • vocab_size: int, number of tokens in the final vocabulary
  • coverage: float, fraction of characters covered by the model. Must be in the range [0, 1]. A good value to use is about 0.9999.
  • n_threads: int, number of parallel threads used to run. If equal to -1 then minimum of the number of available threads and 8 will be used (see benchmark).
  • pad_id: int, reserved id for padding
  • unk_id: int, reserved id for unknown symbols
  • bos_id: int, reserved id for begin of sentence token
  • eos_id: int, reserved id for end of sentence token

Returns: Class youtokentome.BPE with the loaded model.

 

Model loading

youtokentome.BPE(model, n_threads=-1)

Class constructor. Loads the trained model.

  • model: string, path to the trained model
  • n_threads: int, number of parallel threads used to run. If equal to -1, then the maximum number of threads available will be used.

 

Methods

Class youtokentome.BPE has the following methods:

encode

encode(self, sentences, output_type=yttm.OutputType.ID, bos=False, eos=False, reverse=False)

Args:

  • sentences: list of strings, sentences for tokenization.
  • output_type: enum, sentence can be tokenized to ids or subwords. Use OutputType.ID for ids and OutputType.SUBWORD for subwords.
  • bos: bool, if True then token “beginning of sentence” will be added
  • eos: bool, if True then token “end of sentence” will be added
  • reverse: bool, if True the output sequence of tokens will be reversed

Returns: If output_type is equal to youtokentome.OutputType.ID or youtokentome.OutputType.SUBWORD then a list of lists of integers or list of lists of strings will be returned respectively.

 

vocab

vocab(self)

Returns: A list vocab_size strings. The i-th string in the list corresponds to i-th subword.

 

vocab_size

vocab_size(self)

Returns: int. Size of vocabulary.

 

subword_to_id

subword_to_id(self, subword)

Args:

  • subword: string.

Returns: Integer from the range [0, vocab_size-1]. Id of subword or, if there is no such subword in the vocabulary, unk_id will be returned.

 

id_to_subword

id_to_subword(self, id)

Args:

  • id: int, must be in the range [0, vocab_size-1]

Returns: string. Subword from vocabulary by id.

 

decode

decode(self, ids)

Convert each id to subword and concatenate with space symbol.

Args:

  • ids: list of lists of integers. All integers must be in the range [0, vocab_size-1]

Returns: List of strings.

Command line interface

Example

$ yttm bpe --data TRAINING_DATA_FILE --model OUTPUT_MODEL_FILE --vocab_size 2000
$ yttm encode --model OUTPUT_MODEL_FILE --output_type subword < TEST_DATA_FILE > ENCODED_DATA 

Supported commands

YouTokenToMe supports the following commands:

$ yttm --help

Usage: yttm [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  bpe     Train BPE model.
  decode  Decode ids to text.
  encode  Encode text to ids or subwords.
  vocab   Print list of learned subwords.

Command bpe allows you to train Byte Pair Encoding model based on a text file.

$ yttm bpe --help

Usage: yttm bpe [OPTIONS]

  Train BPE model.

Options:
  --data PATH           Training data file path.  [required]
  --model PATH          Output model file path.  [required]
  --vocab_size INTEGER  Number of tokens in the final vocabulary.  [required]
  --coverage FLOAT      Fraction of characters covered by the model.  [default: 1.0]
  --n_threads INTEGER   Number of threads.  [default: -1]
  --pad_id INTEGER      Padding token id.  [default: 0]
  --unk_id INTEGER      Unknown token id.  [default: 1]
  --bos_id INTEGER      'Begin of sentence' token id.  [default: 2]
  --eos_id INTEGER      'End of sentence' token id.  [default: 3]
  --help                Show this message and exit.

Apply BPE encoding for a corpus of sentences. Use stdin for input and stdout for output.

By default, encoding works in parallel using n_threads threads. Number of threads is limited by 8 (see benchmark).

With the --stream option, --n_threads will be ignored and all sentences will be processed one by one. Each sentence will be tokenized and written to the stdout before the next sentence is read.

$ yttm encode --help

Usage: yttm encode [OPTIONS]

  Encode text to ids or subwords.

Options:
  --model PATH         Path to file with learned model.  [required]
  --output_type TEXT   'id' or 'subword'.  [required]
  --n_threads INTEGER  Number of threads.  [default: -1]
  --bos                Add tab 'begin of sentence'.
  --eos                Add tab 'end of sentence'.
  --reverse            Reverse output sequence of tokens.
  --stream             Process each line before reading the next one.
  --help               Show this message and exit.

Print vocabulary. This can be useful for understanding the model.

$ yttm vocab --help

Usage: yttm vocab [OPTIONS]

  Print list of learned subwords.

Options:
  --model PATH  Path to file with learned model.  [required]
  --verbose     Add merging rules.
  --help        Show this message and exit.

Convert ids back to text. Use stdin for input and stdout for output.

$ yttm decode --help

Usage: yttm decode [OPTIONS]

  Decode ids to text.

Options:
  --model PATH  Path to file with learned model.  [required]
  --help        Show this message and exit.
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