taskTokenizer.md

To create a character-level tokenizer for a small GPT-style transformer model, you can utilize the Hugging Face Tokenizers library to build a custom tokenizer and then integrate it with your model. Here's a step-by-step guide:

1. Install the Required Libraries

Ensure you have the necessary libraries installed:

pip install tokenizers transformers

2. Build a Character-Level Tokenizer

Use the tokenizers library to create a tokenizer that processes text at the character level:

from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import CharDelimiterSplit

# Initialize a tokenizer with a Byte-Pair Encoding (BPE) model
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))

# Set the pre-tokenizer to split input into characters
tokenizer.pre_tokenizer = CharDelimiterSplit('')

# Define a trainer with desired vocabulary size
trainer = BpeTrainer(vocab_size=1000, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])

# Train the tokenizer on your dataset
files = ["path_to_your_text_file.txt"]
tokenizer.train(files, trainer)

3. Save the Tokenizer

After training, save the tokenizer to a directory:

tokenizer.save("path_to_save_directory/character_tokenizer.json")

4. Integrate with Hugging Face Transformers

To use this tokenizer with the Hugging Face Transformers library, wrap it with PreTrainedTokenizerFast:

from transformers import PreTrainedTokenizerFast

# Load the custom tokenizer
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="path_to_save_directory/character_tokenizer.json", 
                                         unk_token="[UNK]", cls_token="[CLS]", sep_token="[SEP]", 
                                         pad_token="[PAD]", mask_token="[MASK]")

# Save the tokenizer in the Hugging Face format
fast_tokenizer.save_pretrained("path_to_save_directory")

5. Bundle the Tokenizer with Your Model

When saving your model, ensure the tokenizer is saved in the same directory:

from transformers import GPT2LMHeadModel

# Initialize or load your GPT-2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Save both the model and tokenizer
model.save_pretrained("path_to_save_directory")
fast_tokenizer.save_pretrained("path_to_save_directory")

By saving both the model and tokenizer in the same directory, you can later load them together:

from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast

# Load the model and tokenizer
model = GPT2LMHeadModel.from_pretrained("path_to_save_directory")
tokenizer = PreTrainedTokenizerFast.from_pretrained("path_to_save_directory")

This approach ensures that your custom character-level tokenizer is properly integrated with your GPT-style model, facilitating seamless tokenization and model inference.

For more detailed information on building custom tokenizers, refer to the Hugging Face Tokenizers documentation.