Hugging Face tokenizer#

Hugging Face has many tokenizers.

We use Hugging Face tokenizer, the GPT2TokenizerFast to count the text length in tokens.

  1. How the text is split: by character passed in

  2. How the chunk size is measured: by number of tokens calculated by the Hugging Face tokenizer

from transformers import GPT2TokenizerFast

tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")

# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
    state_of_the_union = f.read()
from langchain.text_splitter import CharacterTextSplitter

text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)

print(texts[0])

Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  

Last year COVID-19 kept us apart. This year we are finally together again. 

Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. 

With a duty to one another to the American people to the Constitution.