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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.
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
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- Interoperability: Subword tokenizers, including TensorFlow’s
text.BertTokenizer
,text.WordpieceTokenizer
, andtext.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_clitics=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_clitics=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 ...
- https://www.tensorflow.org/text/guide/subwords_tokenizer
- https://github.com/tensorflow/tensor2tensor/issues/155
- https://arxiv.org/pdf/2203.09943
- https://seantrott.substack.com/p/tokenization-in-large-language-models
- https://www.tensorflow.org/text/guide/tokenizers
- https://towardsdatascience.com/hands-on-nlp-deep-learning-model-preparation-in-tensorflow-2-x-2e8c9f3c7633
- https://gpttutorpro.com/fine-tuning-large-language-models-data-preparation-and-preprocessing/
- https://huggingface.co/docs/transformers/en/tokenizer_summary
- https://towardsdatascience.com/a-comprehensive-guide-to-subword-tokenisers-4bbd3bad9a7c [10] https://www.linkedin.com/posts/lupiya-47266756_tfdsdeprecatedtextsubwordtextencoder-activity-7202499770710396928-rOKO
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