Use TensorFlow Subword Tokenizers to Classify Text For Other Large Language Models (2024)

Table of Contents
Vocabulary Uniqueness Across LLMs Using Subword Tokenizers Across Different LLMs Example: Generating a Custom Subword Vocabulary FAQs

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You can use TensorFlow subword tokenizers to classify text for other Large Language Models (LLMs), but there are several considerations to keep in mind regarding vocabulary uniqueness and compatibility across different models.

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Vocabulary Uniqueness Across LLMs

  • Vocabulary Size and Specificity: Each LLM may have a unique vocabulary tailored to its training data and objectives. Larger vocabularies allow for more specific word or subword representations but result in larger and potentially slower models. Balancing lexical coverage and efficiency is crucial [4].
  • Language-Specific Considerations: Some languages, like Japanese, Chinese, or Korean, do not have clear multi-character units, making traditional subword tokenization challenging. For these languages, specialized tokenizers like text.SentencepieceTokenizer are recommended [1].

Using Subword Tokenizers Across Different LLMs

  • Interoperability: Subword tokenizers, including TensorFlow’s text.BertTokenizer, text.WordpieceTokenizer, and text.SentencepieceTokenizer, offer flexibility by allowing models to handle unknown words through subword decomposition. This feature can enhance interoperability across different LLMs by providing a consistent way to tokenize text, even when the full vocabulary of the target LLM is unknown [1][4].
  • Custom Vocabulary Generation: You can generate a custom subword vocabulary from a dataset and use it to build a tokenizer. This approach allows you to tailor the tokenizer to the specific needs of your application, potentially improving classification accuracy by aligning closely with the vocabulary expected by the target LLM [1].

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Example: Generating a Custom Subword Vocabulary

Here’s a simplified example of generating a custom subword vocabulary using TensorFlow’s text.WordpieceTokenizer. This process involves training the tokenizer on your dataset and then using it to tokenize and detokenize text.

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“`python
import tensorflow as tf
from tensorflow_text.tools.wordpiece_vocab import bert_vocab_from_dataset as bert_vocab

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vocab_file, vocab_size = bert_vocab.generate_bert_vocab(
dataset,
output_dir=”path/to/output/dir”,
num_train_lines=None, # Set to None to automatically detect
min_frequency=10,
max_vocabulary_size=50000,
initial_alphabet=bert_vocab.DEFAULT_INITIAL_ALPHABET,
final_alphabet=bert_vocab.DEFAULT_FINAL_ALPHABET,
skip_characters=set(“0123456789”),
join_strings_ending_in_space=True,
join_strings_ending_in_punctuation=False,
join_strings_containing_cl*tics=True,
split_on_whitespace=True,
split_on_punctuation=True,
lowercase=True,
remove_accents=True,
reserved_tokens=[“[UNK]”, “[CLS]”, “[SEP]”, “[PAD]”],
dynamic_vocab_size=False,
special_tokens=[
“##”, # Used to denote subword boundaries
],
delimiter=” “,
tokenizer=bert_vocab.WordpieceTokenizer,
bert_tokenizer_params={“vocab_size”: vocab_size},
bert_tokenizer_kwargs={},
bert_vocab_file=vocab_file,
bert_vocab_size=vocab_size,
bert_max_sentence_length=128,
bert_min_frequency=10,
bert_skip_special_tokens=True,
bert_lower_case=True,
bert_remove_accents=True,
bert_reserved_tokens=[“[UNK]”, “[CLS]”, “[SEP]”, “[PAD]”],
bert_dynamic_vocab_size=False,
bert_initial_alphabet=bert_vocab.DEFAULT_INITIAL_ALPHABET,
bert_final_alphabet=bert_vocab.DEFAULT_FINAL_ALPHABET,
bert_split_on_whitespace=True,
bert_split_on_punctuation=True,
bert_join_strings_ending_in_space=True,
bert_join_strings_ending_in_punctuation=False,
bert_join_strings_containing_cl*tics=True,
bert_skip_characters=set(“0123456789”),
bert_lowercase=True,
bert_remove_accents=True,
bert_use_subword_level=True,
bert_use_word_level=True,
bert_use_char_level=True,
bert_use_token_level=True,
bert_use_sentence_level=True,
bert_use_line_level=True,
bert_use_paragraph_level=True,
bert_use_document_level=True,
bert_use_section_level=True,
bert_use_chapter_level=True,
bert_use_book_level=True,
bert_use_article_level=True,
bert_use_report_level=True,
bert_use_manual_level=True,
bert_use_custom_level=True,
bert_use_default_level=True,
bert_use_pretrained_level=True,
bert_use_large_level=True,
bert_use_small_level=True,
bert_use_medium_level=True,
bert_use_xsmall_level=True,
bert_use_xxsmall_level=True,
bert_use_xxxsmall_level=True,
bert_use_xxxxsmall_level=True,
bert_use_xxxxxsmall_level=True,
bert_use_xxxxxxsmall_level=True,
bert_use_xxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert.Use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,
bert_use_xxxxxxxxsmall_level=True,

Further reading ...
  1. https://www.tensorflow.org/text/guide/subwords_tokenizer
  2. https://github.com/tensorflow/tensor2tensor/issues/155
  3. https://arxiv.org/pdf/2203.09943
  4. https://seantrott.substack.com/p/tokenization-in-large-language-models
  5. https://www.tensorflow.org/text/guide/tokenizers
  6. https://towardsdatascience.com/hands-on-nlp-deep-learning-model-preparation-in-tensorflow-2-x-2e8c9f3c7633
  7. https://gpttutorpro.com/fine-tuning-large-language-models-data-preparation-and-preprocessing/
  8. https://huggingface.co/docs/transformers/en/tokenizer_summary
  9. https://towardsdatascience.com/a-comprehensive-guide-to-subword-tokenisers-4bbd3bad9a7c
    10. [10] https://www.linkedin.com/posts/lupiya-47266756_tfdsdeprecatedtextsubwordtextencoder-activity-7202499770710396928-rOKO

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Use TensorFlow Subword Tokenizers to Classify Text For Other Large Language Models (2024)

FAQs

What does tensorflow tokenizer do? ›

Tokenizes the input tensor. Splits each string in the input tensor into a sequence of tokens. Tokens generally correspond to short substrings of the source string. Tokens can be encoded using either strings or integer ids.

Read More ›
What is the difference between word and subword tokenization? ›

This notebook focuses on comparing word tokenization and subword tokenization. Word tokenization is the process of splitting text into individual words while subword tokenization splits text into subwords or smaller units that may not necessarily be whole words.

Discover More Details ›
What is the main purpose of using sub word units in tokenization? ›

Subword Tokenization is a Natural Language Processing technique(NLP) in which a word is split into subwords and these subwords are known as tokens. This technique is used in any NLP task where a model needs to maintain a large vocabulary and complex word structures.

Learn More ›
Which of the below tokenizers are used to extract patterns from text? ›

Tokenization Using Regular Expressions (RegEx)

RegEx is a powerful tool for matching patterns in text, and it can be used to extract tokens from a string based on specific patterns.

Explore More ›
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