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103 lines
2.7 KiB
Plaintext
103 lines
2.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "13dc0983",
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"metadata": {},
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"source": [
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"# Hugging Face Length Function\n",
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"Most LLMs are constrained by the number of tokens that you can pass in, which is not the same as the number of characters. In order to get a more accurate estimate, we can use Hugging Face tokenizers to count the text length.\n",
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"\n",
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"1. How the text is split: by character passed in\n",
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"2. How the chunk size is measured: by Hugging Face tokenizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a8ce51d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import GPT2TokenizerFast\n",
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"\n",
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"tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "388369ed",
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is a long document we can split up.\n",
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"with open('../../../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()\n",
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"from langchain.text_splitter import CharacterTextSplitter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ca5e72c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)\n",
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"texts = text_splitter.split_text(state_of_the_union)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "37cdfbeb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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",
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"\n",
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"Last year COVID-19 kept us apart. This year we are finally together again. \n",
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"\n",
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"Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
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"\n",
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"With a duty to one another to the American people to the Constitution.\n"
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]
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}
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],
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"source": [
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"print(texts[0])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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