{ "cells": [ { "cell_type": "markdown", "id": "13dc0983", "metadata": {}, "source": [ "# Hugging Face tokenizer\n", "\n", ">[Hugging Face](https://huggingface.co/docs/tokenizers/index) has many tokenizers.\n", "\n", "We use Hugging Face tokenizer, the [GPT2TokenizerFast](https://huggingface.co/Ransaka/gpt2-tokenizer-fast) to count the text length in tokens.\n", "\n", "1. How the text is split: by character passed in\n", "2. How the chunk size is measured: by number of tokens calculated by the `Hugging Face` tokenizer\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "a8ce51d5", "metadata": {}, "outputs": [], "source": [ "from transformers import GPT2TokenizerFast\n", "\n", "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "388369ed", "metadata": {}, "outputs": [], "source": [ "# This is a long document we can split up.\n", "with open('../../../state_of_the_union.txt') as f:\n", " state_of_the_union = f.read()\n", "from langchain.text_splitter import CharacterTextSplitter" ] }, { "cell_type": "code", "execution_count": 3, "id": "ca5e72c0", "metadata": {}, "outputs": [], "source": [ "text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)\n", "texts = text_splitter.split_text(state_of_the_union)" ] }, { "cell_type": "code", "execution_count": 4, "id": "37cdfbeb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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. \n", "\n", "Last year COVID-19 kept us apart. This year we are finally together again. \n", "\n", "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n", "\n", "With a duty to one another to the American people to the Constitution.\n" ] } ], "source": [ "print(texts[0])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "vscode": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" } } }, "nbformat": 4, "nbformat_minor": 5 }