hugtokencraft 0.1.0

HugTokenCraft is a user-friendly Python library that simplifies the process of modifying the vocabulary of a PreTrainedTokenizer from HuggingFace Transformers, making it accessible without additional training.

HugTokenCraft

HugTokenCraft is a user-friendly Python library that simplifies the process of modifying the vocabulary of a PreTrainedTokenizer from HuggingFace Transformers, making it accessible without additional training. As of now, this was validated for BertTokenizer, which is word-piece-based vocabulary.

Installation

Install from PyPI

You can install HugTokenCraft using pip:

pip install hugtokencraft

Install from source

git clone [e-mail]/MDFahimAnjum/HugTokenCraft.git
cd HugTokenCraft
python setup.py install

Usage

1. Reduce vocabulary

Let's take a pre-trained BertTokenizer which has 30,000 tokens and modify it to only keep 20 tokens

#import library
from hugtokencraft import editor
from transformers import BertTokenizer
import os

#load BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

#check
initial_vocab_size=len(tokenizer)
print(f"initial vocab size: {initial_vocab_size}")

#Target vocabulary
target_vocab_size=20
selected_words=editor.get_top_tokens(tokenizer,target_vocab_size)

#parameters
current_directory = os.getcwd()
# Define the path where you want to save the tokenizer
tokenizer_path = os.path.join(current_directory,"ModifiedTokenizer")
model_max_length=128

#reduce vocabulary
modified_tokenizer=editor.reduce_vocabulary(tokenizer,selected_words)
tokenizer_path=editor.save_tokenizer(modified_tokenizer,tokenizer_path,model_max_length)
modified_tokenizer=editor.load_tokenizer(type(tokenizer),tokenizer_path)

#check
new_vocab_size=len(modified_tokenizer)
print(f"new vocab size: {new_vocab_size} words")

2. Expand vocabulary

Let's take a pre-trained BertTokenizer and add two new tokens

#import library
from hugtokencraft import editor
from transformers import BertTokenizer
import os

#load BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

#check
initial_vocab_size=len(tokenizer)
print(f"initial vocab size: {initial_vocab_size}")

#Target vocabulary
selected_words_add={'hugtoken','hugtokencraft'}

#parameters
current_directory = os.getcwd()
# Define the path where you want to save the tokenizer
tokenizer_path = os.path.join(current_directory,"ModifiedTokenizer")


#expand vocabulary
modified_tokenizer=editor.expand_vocabulary(tokenizer,selected_words_add)
tokenizer_path=editor.save_tokenizer(modified_tokenizer,tokenizer_path,model_max_length=None,isreduced=False)
modified_tokenizer=editor.load_tokenizer(type(tokenizer),tokenizer_path)

#check
new_vocab_size=len(modified_tokenizer)
print(f"new vocab size: {new_vocab_size}")

Notebook Example

You can also run the Python jupyter notebook examples directly by running example_notebook.ipynb

Documentation

get_top_tokens()

Obtains the k most frequently used tokens from tokenizer vocabulary.

Syntex

token_set=get_top_tokens(tokenizer,k)

Parameters

• tokenizer: BertTokenizer
  • Pre-trained Bert Tokenizer
• k: int
  • Desired number of tokens

Returns

• token_list: set
  • Set of k most frequent tokens

expand_vocabulary()

Adds a set of new tokens to the vocabulary

Syntex

modified_tokenizer=expand_vocabulary(tokenizer,tokens_to_add)

Parameters

• tokenizer: BertTokenizer
  • Pre-trained Bert Tokenizer
• tokens_to_add: set
  • Set of tokens to add

Returns

• modified_tokenizer: BertTokenizer
  • Modified Bert Tokenizer

reduce_vocabulary()

Removes all tokens execpt the given set of tokens from vocabulary

Syntex

modified_tokenizer=reduce_vocabulary(tokenizer,tokens_to_keep)

Parameters

• tokenizer: BertTokenizer
  • Pre-trained Bert Tokenizer
• tokens_to_keep: set
  • Set of tokens to keep

Returns

• modified_tokenizer: BertTokenizer
  • Modified Bert Tokenizer

save_tokenizer()

Saves the modified tokenizer for use

Syntex

tokenizer_path=save_tokenizer(tokenizer,tokenizer_path,model_max_length=None,isreduced=True)

Parameters

• tokenizer: BertTokenizer
  • Pre-trained Bert Tokenizer
• tokenizer_path: str
  • Location path to save the tokenizer
• model_max_length: int
  • New value of maximum token length
  • Defaults to None which means no change
• isreduced: bool
  • Whether the modified tokenizer was reduced
  • True if vocabulary was reduced (Default)
  • False if vocabulary was expanded

Returns

• tokenizer_path: str
  • Location path to save the tokenizer

load_tokenizer()

Loads a tokenizer from a given path

Syntex

tokenizer=load_tokenizer(tokenizer_class,tokenizer_path)

Parameters

• tokenizer_class: type
  • Class type of Tokenizer
• tokenizer_path: str
  • Location path to save the tokenizer

Returns

• tokenizer: tokenizer_class
  • Tokenizer

validate_tokenizer()

Simple sanity check for tokenizer

Syntex

is_pass=validate_tokenizer(tokenizer)

Parameters

• tokenizer: BertTokenizer
  • Pre-trained Bert Tokenizer

Returns

• is_pass: bool
  • Valication result
  • True: validation passed
  • False: Validation failed

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

We welcome contributions!