Byte Pair Encoding (BPE) tokenizer & Python codes

Byte Pair Encoding (BPE) tokenizer is a widely used technique in natural language processing that helps in tokenizing text by identifying and replacing the most common pairs of consecutive characters with a new single character. This method not only efficiently reduces the vocabulary size but also captures subword information, allowing models to better understand and generate text. By iteratively applying this process, BPE can create tokens that are beneficial for various linguistic tasks, ensuring that rare words can be represented effectively and enhancing the performance of machine learning models, specifically in scenarios involving morphologically rich languages.

To initialize, train, and use a Byte Pair Encoding (BPE) tokenizer, we can use the tokenizers library, which is commonly used in natural language processing for tokenizing and encoding text. One can following steps:

1. Importing Required Modules

   from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers

• This line imports necessary components from the tokenizers library.
  • Tokenizer: Main class for defining the tokenizer.
  • models: Contains different tokenization models, here we use BPE (Byte Pair Encoding).
  • pre_tokenizers: Contains methods to split text into smaller chunks before tokenizing.
  • decoders: Used to convert token IDs back into human-readable text.
  • trainers: Contains various trainers to train the tokenizer on custom data.

2. Initializing the Tokenizer with BPE

   tokenizer = Tokenizer(models.BPE())

• models.BPE(): Initializes a tokenizer model based on Byte Pair Encoding, a common method that iteratively merges frequent pairs of characters in a dataset to create tokens.
• Tokenizer(models.BPE()): Creates a Tokenizer object using the BPE model.

3. Setting Pre-tokenizer and Decoder

   tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel()
   tokenizer.decoder = decoders.ByteLevel()

• pre_tokenizers.ByteLevel(): A pre-tokenizer that splits text at the byte level, meaning it processes each byte individually rather than splitting by whitespace or punctuation. This is useful for handling any kind of text, including emojis and special symbols.
• decoders.ByteLevel(): A decoder that reconstructs the original text from token IDs, reversing the byte-level tokenization.

4. Training the Tokenizer

   trainer = trainers.BpeTrainer(vocab_size=1000, min_frequency=2)
   tokenizer.train(["data/demo.txt"], trainer)

• trainers.BpeTrainer(vocab_size=1000, min_frequency=2): Initializes a trainer for the BPE model.
  • vocab_size=1000: Limits the vocabulary to 1,000 tokens.
  • min_frequency=2: Only tokens that appear at least twice in the data will be included in the vocabulary.
• tokenizer.train(["data/demo.txt"], trainer): Trains the tokenizer on the specified text file data/demo.txt using the BPE trainer. The model learns frequently occurring byte pairs and creates tokens for them.

5. Encoding Text

   encoded = tokenizer.encode("Hello, world!")
   print("Tokens:", encoded.tokens)

• tokenizer.encode("Hello, world!"): Encodes the input text “Hello, world!” into tokens, based on the trained BPE model.
• encoded.tokens: Accesses the list of tokenized words/units.
• print("Tokens:", encoded.tokens): Prints the tokenized version of the input text.

6. Decoding Text

   decoded = tokenizer.decode(encoded.ids)
   print("Decoded text:", decoded)

• tokenizer.decode(encoded.ids): Takes the encoded token IDs and decodes them back into readable text.
• print("Decoded text:", decoded): Prints the decoded text, which should resemble the original input (“Hello, world!”)

Full code (run in Colab):

from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers

# Initialize a tokenizer with BPE
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel()
tokenizer.decoder = decoders.ByteLevel()

# Training the tokenizer
trainer = trainers.BpeTrainer(vocab_size=1000, min_frequency=2)
tokenizer.train(["path/to/your/text/file.txt"], trainer)

# Encoding text
encoded = tokenizer.encode("Hello, world!")
print("Tokens:", encoded.tokens)

# Decoding text
decoded = tokenizer.decode(encoded.ids)
print("Decoded text:", decoded)