A simple tool for adapting a pretrained Huggingface model to a new vocabulary with (almost) no training.
This technique can significantly reduce sequence length when a language model is used on data with a specific vocabulary (biology, medicine, law, other languages, etc...).
A slight loss of model quality is likely to be observed, especially when the vocabulary size is greatly reduced. Fine-tuning or additional pretraining during few steps solves the problem in most cases.
Should work for most Huggingface Hub language models (requires further testing).
Everything is run on CPU.
pip install tokenizer-adapter --upgrade
It is recommended to use an existing tokenizer to train the new vocabulary.
Best and easiest way is to use the tokenizer.train_new_from_iterator(...) method.
from tokenizer_adapter import TokenizerAdapter
from transformers import AutoTokenizer, AutoModelForMaskedLM
BASE_MODEL_PATH = "camembert-base"
# A simple corpus
corpus = ["A first sentence", "A second sentence", "blablabla"]
# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained(BASE_MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_PATH)
# Train new vocabulary from the old tokenizer
new_tokenizer = tokenizer.train_new_from_iterator(corpus, vocab_size=300)
# Default params should work in most cases
adapter = TokenizerAdapter()
# Patch the model with the new tokenizer
model = adapter.adapt_from_pretrained(new_tokenizer, model, tokenizer)
# Save the model and the new tokenizer
model.save_pretrained("my_new_model/")
new_tokenizer.save_pretrained("my_new_model/")To rely on a custom tokenizer (experimental), you may need to use the custom_preprocessing argument.
Example using a RoBERTa (similar to Phi-2) style tokenizer for a CamemBERT model:
from tokenizer_adapter import TokenizerAdapter
from transformers import AutoTokenizer, AutoModelForMaskedLM
BASE_MODEL_PATH = "camembert-base"
NEW_CUSTOM_TOKENIZER = "roberta-base"
# A simple corpus
corpus = ["A first sentence", "A second sentence", "blablabla"]
# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained(BASE_MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_PATH)
# Also load this custom tokenizer to train the new one
new_tokenizer = AutoTokenizer.from_pretrained(NEW_CUSTOM_TOKENIZER)
new_tokenizer = new_tokenizer.train_new_from_iterator(corpus, vocab_size=300)
# CamemBERT tokenizer relies on '▁' while the RoBERTa one relies on 'Ġ'
adapter = TokenizerAdapter(custom_preprocessing=lambda x: x.replace('Ġ', '▁'))
# Patch the model with the new tokenizer
model = adapter.adapt_from_pretrained(new_tokenizer, model, tokenizer)
# Save the model and the new tokenizer
model.save_pretrained("my_new_model/")
new_tokenizer.save_pretrained("my_new_model/")