mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-10-31 09:50:23 +00:00
1909 lines
55 KiB
Plaintext
1909 lines
55 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d95f841a-63c9-41d4-aea1-496b3d2024dd",
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"metadata": {},
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"source": [
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"<table style=\"width:100%\">\n",
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"<tr>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<font size=\"2\">\n",
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"Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
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"<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
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"</font>\n",
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"</td>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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"</td>\n",
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"</tr>\n",
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"</table>\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "25aa40e3-5109-433f-9153-f5770531fe94",
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"metadata": {},
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"source": [
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"# Chapter 2: Working with Text"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76d5d2c0-cba8-404e-9bf3-71a218cae3cf",
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"metadata": {},
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"source": [
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"Packages that are being used in this notebook:"
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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": "4d1305cf-12d5-46fe-a2c9-36fb71c5b3d3",
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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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"torch version: 2.4.0\n",
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"tiktoken version: 0.7.0\n"
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]
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}
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],
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"source": [
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"from importlib.metadata import version\n",
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"\n",
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"print(\"torch version:\", version(\"torch\"))\n",
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"print(\"tiktoken version:\", version(\"tiktoken\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5a42fbfd-e3c2-43c2-bc12-f5f870a0b10a",
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"metadata": {},
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"source": [
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"- This chapter covers data preparation and sampling to get input data \"ready\" for the LLM"
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]
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},
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{
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"cell_type": "markdown",
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"id": "628b2922-594d-4ff9-bd82-04f1ebdf41f5",
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"metadata": {},
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"source": [
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"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/01.webp?timestamp=1\" width=\"500px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2417139b-2357-44d2-bd67-23f5d7f52ae7",
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"metadata": {},
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"source": [
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"## 2.1 Understanding word embeddings"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b6816ae-e927-43a9-b4dd-e47a9b0e1cf6",
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"metadata": {},
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"source": [
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"- No code in this section"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f69dab7-a433-427a-9e5b-b981062d6296",
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"metadata": {},
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"source": [
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"- There are many forms of embeddings; we focus on text embeddings in this book"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ba08d16f-f237-4166-bf89-0e9fe703e7b4",
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"metadata": {},
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"source": [
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"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/02.webp\" width=\"500px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "288c4faf-b93a-4616-9276-7a4aa4b5e9ba",
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"metadata": {},
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"source": [
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"- LLMs work with embeddings in high-dimensional spaces (i.e., thousands of dimensions)\n",
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"- Since we can't visualize such high-dimensional spaces (we humans think in 1, 2, or 3 dimensions), the figure below illustrates a 2-dimensional embedding space"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d6b80160-1f10-4aad-a85e-9c79444de9e6",
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"metadata": {},
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"source": [
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"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/03.webp\" width=\"300px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eddbb984-8d23-40c5-bbfa-c3c379e7eec3",
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"metadata": {},
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"source": [
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"## 2.2 Tokenizing text"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f9c90731-7dc9-4cd3-8c4a-488e33b48e80",
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"metadata": {},
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"source": [
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"- In this section, we tokenize text, which means breaking text into smaller units, such as individual words and punctuation characters"
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]
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},
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{
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"cell_type": "markdown",
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"id": "09872fdb-9d4e-40c4-949d-52a01a43ec4b",
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"metadata": {},
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"source": [
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"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/04.webp\" width=\"300px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8cceaa18-833d-46b6-b211-b20c53902805",
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"metadata": {},
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"source": [
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"- Load raw text we want to work with\n",
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"- [The Verdict by Edith Wharton](https://en.wikisource.org/wiki/The_Verdict) is a public domain short story"
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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": "40f9d9b1-6d32-485a-825a-a95392a86d79",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import urllib.request\n",
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"\n",
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"if not os.path.exists(\"the-verdict.txt\"):\n",
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" url = (\"https://raw.githubusercontent.com/rasbt/\"\n",
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" \"LLMs-from-scratch/main/ch02/01_main-chapter-code/\"\n",
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" \"the-verdict.txt\")\n",
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" file_path = \"the-verdict.txt\"\n",
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" urllib.request.urlretrieve(url, file_path)"
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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": "8a769e87-470a-48b9-8bdb-12841b416198",
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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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"Total number of character: 20479\n",
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"I HAD always thought Jack Gisburn rather a cheap genius--though a good fellow enough--so it was no \n"
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]
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}
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],
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"source": [
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"with open(\"the-verdict.txt\", \"r\", encoding=\"utf-8\") as f:\n",
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" raw_text = f.read()\n",
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" \n",
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"print(\"Total number of character:\", len(raw_text))\n",
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"print(raw_text[:99])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b971a46-ac03-4368-88ae-3f20279e8f4e",
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"metadata": {},
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"source": [
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"- The goal is to tokenize and embed this text for an LLM\n",
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"- Let's develop a simple tokenizer based on some simple sample text that we can then later apply to the text above\n",
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"- The following regular expression will split on whitespaces"
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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": "737dd5b0-9dbb-4a97-9ae4-3482c8c04be7",
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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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"['Hello,', ' ', 'world.', ' ', 'This,', ' ', 'is', ' ', 'a', ' ', 'test.']\n"
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]
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}
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],
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"source": [
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"import re\n",
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"\n",
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"text = \"Hello, world. This, is a test.\"\n",
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"result = re.split(r'(\\s)', text)\n",
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"\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a8c40c18-a9d5-4703-bf71-8261dbcc5ee3",
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"metadata": {},
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"source": [
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"- We don't only want to split on whitespaces but also commas and periods, so let's modify the regular expression to do that as well"
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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": 5,
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"id": "ea02489d-01f9-4247-b7dd-a0d63f62ef07",
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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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"['Hello', ',', '', ' ', 'world', '.', '', ' ', 'This', ',', '', ' ', 'is', ' ', 'a', ' ', 'test', '.', '']\n"
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]
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}
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],
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"source": [
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"result = re.split(r'([,.]|\\s)', text)\n",
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"\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "461d0c86-e3af-4f87-8fae-594a9ca9b6ad",
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"metadata": {},
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"source": [
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"- As we can see, this creates empty strings, let's remove them"
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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": 6,
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"id": "4d8a6fb7-2e62-4a12-ad06-ccb04f25fed7",
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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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"['Hello', ',', 'world', '.', 'This', ',', 'is', 'a', 'test', '.']\n"
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]
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}
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],
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"source": [
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"# Strip whitespace from each item and then filter out any empty strings.\n",
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"result = [item for item in result if item.strip()]\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "250e8694-181e-496f-895d-7cb7d92b5562",
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"metadata": {},
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"source": [
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"- This looks pretty good, but let's also handle other types of punctuation, such as periods, question marks, and so on"
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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": 7,
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"id": "ed3a9467-04b4-49d9-96c5-b8042bcf8374",
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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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"['Hello', ',', 'world', '.', 'Is', 'this', '--', 'a', 'test', '?']\n"
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]
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}
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],
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"source": [
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"text = \"Hello, world. Is this-- a test?\"\n",
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"\n",
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"result = re.split(r'([,.:;?_!\"()\\']|--|\\s)', text)\n",
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"result = [item.strip() for item in result if item.strip()]\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5bbea70b-c030-45d9-b09d-4318164c0bb4",
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"metadata": {},
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"source": [
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"- This is pretty good, and we are now ready to apply this tokenization to the raw text"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6cbe9330-b587-4262-be9f-497a84ec0e8a",
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"metadata": {},
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"source": [
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"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/05.webp\" width=\"350px\">"
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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": 8,
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"id": "8c567caa-8ff5-49a8-a5cc-d365b0a78a99",
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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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"['I', 'HAD', 'always', 'thought', 'Jack', 'Gisburn', 'rather', 'a', 'cheap', 'genius', '--', 'though', 'a', 'good', 'fellow', 'enough', '--', 'so', 'it', 'was', 'no', 'great', 'surprise', 'to', 'me', 'to', 'hear', 'that', ',', 'in']\n"
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]
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}
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],
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"source": [
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"preprocessed = re.split(r'([,.:;?_!\"()\\']|--|\\s)', raw_text)\n",
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"preprocessed = [item.strip() for item in preprocessed if item.strip()]\n",
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"print(preprocessed[:30])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e2a19e1a-5105-4ddb-812a-b7d3117eab95",
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"metadata": {},
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"source": [
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"- Let's calculate the total number of tokens"
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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": 9,
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"id": "35db7b5e-510b-4c45-995f-f5ad64a8e19c",
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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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"4690\n"
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]
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}
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],
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"source": [
|
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"print(len(preprocessed))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b5ce8fe-3a07-4f2a-90f1-a0321ce3a231",
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"metadata": {},
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"source": [
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"## 2.3 Converting tokens into token IDs"
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]
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},
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{
|
|
"cell_type": "markdown",
|
|
"id": "a5204973-f414-4c0d-87b0-cfec1f06e6ff",
|
|
"metadata": {},
|
|
"source": [
|
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"- Next, we convert the text tokens into token IDs that we can process via embedding layers later"
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]
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},
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{
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"cell_type": "markdown",
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"id": "177b041d-f739-43b8-bd81-0443ae3a7f8d",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/06.webp\" width=\"500px\">"
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]
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b5973794-7002-4202-8b12-0900cd779720",
|
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"metadata": {},
|
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"source": [
|
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"- From these tokens, we can now build a vocabulary that consists of all the unique tokens"
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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": 10,
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|
"id": "7fdf0533-5ab6-42a5-83fa-a3b045de6396",
|
|
"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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"1130\n"
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]
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}
|
|
],
|
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"source": [
|
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"all_words = sorted(set(preprocessed))\n",
|
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"vocab_size = len(all_words)\n",
|
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"\n",
|
|
"print(vocab_size)"
|
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]
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},
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|
{
|
|
"cell_type": "code",
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|
"execution_count": 11,
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|
"id": "77d00d96-881f-4691-bb03-84fec2a75a26",
|
|
"metadata": {},
|
|
"outputs": [],
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"source": [
|
|
"vocab = {token:integer for integer,token in enumerate(all_words)}"
|
|
]
|
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},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "75bd1f81-3a8f-4dd9-9dd6-e75f32dacbe3",
|
|
"metadata": {},
|
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"source": [
|
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"- Below are the first 50 entries in this vocabulary:"
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]
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},
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|
{
|
|
"cell_type": "code",
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|
"execution_count": 12,
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|
"id": "e1c5de4a-aa4e-4aec-b532-10bb364039d6",
|
|
"metadata": {},
|
|
"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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"('!', 0)\n",
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"('\"', 1)\n",
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"(\"'\", 2)\n",
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"('(', 3)\n",
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"(')', 4)\n",
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"(',', 5)\n",
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"('--', 6)\n",
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"('.', 7)\n",
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"(':', 8)\n",
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"(';', 9)\n",
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"('?', 10)\n",
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"('A', 11)\n",
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"('Ah', 12)\n",
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"('Among', 13)\n",
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"('And', 14)\n",
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"('Are', 15)\n",
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"('Arrt', 16)\n",
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"('As', 17)\n",
|
|
"('At', 18)\n",
|
|
"('Be', 19)\n",
|
|
"('Begin', 20)\n",
|
|
"('Burlington', 21)\n",
|
|
"('But', 22)\n",
|
|
"('By', 23)\n",
|
|
"('Carlo', 24)\n",
|
|
"('Chicago', 25)\n",
|
|
"('Claude', 26)\n",
|
|
"('Come', 27)\n",
|
|
"('Croft', 28)\n",
|
|
"('Destroyed', 29)\n",
|
|
"('Devonshire', 30)\n",
|
|
"('Don', 31)\n",
|
|
"('Dubarry', 32)\n",
|
|
"('Emperors', 33)\n",
|
|
"('Florence', 34)\n",
|
|
"('For', 35)\n",
|
|
"('Gallery', 36)\n",
|
|
"('Gideon', 37)\n",
|
|
"('Gisburn', 38)\n",
|
|
"('Gisburns', 39)\n",
|
|
"('Grafton', 40)\n",
|
|
"('Greek', 41)\n",
|
|
"('Grindle', 42)\n",
|
|
"('Grindles', 43)\n",
|
|
"('HAD', 44)\n",
|
|
"('Had', 45)\n",
|
|
"('Hang', 46)\n",
|
|
"('Has', 47)\n",
|
|
"('He', 48)\n",
|
|
"('Her', 49)\n",
|
|
"('Hermia', 50)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, item in enumerate(vocab.items()):\n",
|
|
" print(item)\n",
|
|
" if i >= 50:\n",
|
|
" break"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3b1dc314-351b-476a-9459-0ec9ddc29b19",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Below, we illustrate the tokenization of a short sample text using a small vocabulary:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "67407a9f-0202-4e7c-9ed7-1b3154191ebc",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/07.webp?123\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4e569647-2589-4c9d-9a5c-aef1c88a0a9a",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Putting it now all together into a tokenizer class"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "f531bf46-7c25-4ef8-bff8-0d27518676d5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class SimpleTokenizerV1:\n",
|
|
" def __init__(self, vocab):\n",
|
|
" self.str_to_int = vocab\n",
|
|
" self.int_to_str = {i:s for s,i in vocab.items()}\n",
|
|
" \n",
|
|
" def encode(self, text):\n",
|
|
" preprocessed = re.split(r'([,.:;?_!\"()\\']|--|\\s)', text)\n",
|
|
" \n",
|
|
" preprocessed = [\n",
|
|
" item.strip() for item in preprocessed if item.strip()\n",
|
|
" ]\n",
|
|
" ids = [self.str_to_int[s] for s in preprocessed]\n",
|
|
" return ids\n",
|
|
" \n",
|
|
" def decode(self, ids):\n",
|
|
" text = \" \".join([self.int_to_str[i] for i in ids])\n",
|
|
" # Replace spaces before the specified punctuations\n",
|
|
" text = re.sub(r'\\s+([,.?!\"()\\'])', r'\\1', text)\n",
|
|
" return text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dee7a1e5-b54f-4ca1-87ef-3d663c4ee1e7",
|
|
"metadata": {},
|
|
"source": [
|
|
"- The `encode` function turns text into token IDs\n",
|
|
"- The `decode` function turns token IDs back into text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cc21d347-ec03-4823-b3d4-9d686e495617",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/08.webp?123\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c2950a94-6b0d-474e-8ed0-66d0c3c1a95c",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We can use the tokenizer to encode (that is, tokenize) texts into integers\n",
|
|
"- These integers can then be embedded (later) as input of/for the LLM"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "647364ec-7995-4654-9b4a-7607ccf5f1e4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[1, 56, 2, 850, 988, 602, 533, 746, 5, 1126, 596, 5, 1, 67, 7, 38, 851, 1108, 754, 793, 7]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer = SimpleTokenizerV1(vocab)\n",
|
|
"\n",
|
|
"text = \"\"\"\"It's the last he painted, you know,\" \n",
|
|
" Mrs. Gisburn said with pardonable pride.\"\"\"\n",
|
|
"ids = tokenizer.encode(text)\n",
|
|
"print(ids)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3201706e-a487-4b60-b99d-5765865f29a0",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We can decode the integers back into text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "01d8c8fb-432d-4a49-b332-99f23b233746",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'\" It\\' s the last he painted, you know,\" Mrs. Gisburn said with pardonable pride.'"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer.decode(ids)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "54f6aa8b-9827-412e-9035-e827296ab0fe",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'\" It\\' s the last he painted, you know,\" Mrs. Gisburn said with pardonable pride.'"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer.decode(tokenizer.encode(text))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4b821ef8-4d53-43b6-a2b2-aef808c343c7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2.4 Adding special context tokens"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "863d6d15-a3e2-44e0-b384-bb37f17cf443",
|
|
"metadata": {},
|
|
"source": [
|
|
"- It's useful to add some \"special\" tokens for unknown words and to denote the end of a text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "aa7fc96c-e1fd-44fb-b7f5-229d7c7922a4",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/09.webp?123\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9d709d57-2486-4152-b7f9-d3e4bd8634cd",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Some tokenizers use special tokens to help the LLM with additional context\n",
|
|
"- Some of these special tokens are\n",
|
|
" - `[BOS]` (beginning of sequence) marks the beginning of text\n",
|
|
" - `[EOS]` (end of sequence) marks where the text ends (this is usually used to concatenate multiple unrelated texts, e.g., two different Wikipedia articles or two different books, and so on)\n",
|
|
" - `[PAD]` (padding) if we train LLMs with a batch size greater than 1 (we may include multiple texts with different lengths; with the padding token we pad the shorter texts to the longest length so that all texts have an equal length)\n",
|
|
"- `[UNK]` to represent words that are not included in the vocabulary\n",
|
|
"\n",
|
|
"- Note that GPT-2 does not need any of these tokens mentioned above but only uses an `<|endoftext|>` token to reduce complexity\n",
|
|
"- The `<|endoftext|>` is analogous to the `[EOS]` token mentioned above\n",
|
|
"- GPT also uses the `<|endoftext|>` for padding (since we typically use a mask when training on batched inputs, we would not attend padded tokens anyways, so it does not matter what these tokens are)\n",
|
|
"- GPT-2 does not use an `<UNK>` token for out-of-vocabulary words; instead, GPT-2 uses a byte-pair encoding (BPE) tokenizer, which breaks down words into subword units which we will discuss in a later section\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a336b43b-7173-49e7-bd80-527ad4efb271",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We use the `<|endoftext|>` tokens between two independent sources of text:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "52442951-752c-4855-9752-b121a17fef55",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/10.webp\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c661a397-da06-4a86-ac27-072dbe7cb172",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Let's see what happens if we tokenize the following text:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "d5767eff-440c-4de1-9289-f789349d6b85",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "KeyError",
|
|
"evalue": "'Hello'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[17], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m SimpleTokenizerV1(vocab)\n\u001b[1;32m 3\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, do you like tea. Is this-- a test?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mencode(text)\n",
|
|
"Cell \u001b[0;32mIn[13], line 12\u001b[0m, in \u001b[0;36mSimpleTokenizerV1.encode\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.:;?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m 9\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 10\u001b[0m item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()\n\u001b[1;32m 11\u001b[0m ]\n\u001b[0;32m---> 12\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstr_to_int[s] \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n",
|
|
"Cell \u001b[0;32mIn[13], line 12\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.:;?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m 9\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 10\u001b[0m item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()\n\u001b[1;32m 11\u001b[0m ]\n\u001b[0;32m---> 12\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstr_to_int[s] \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n",
|
|
"\u001b[0;31mKeyError\u001b[0m: 'Hello'"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer = SimpleTokenizerV1(vocab)\n",
|
|
"\n",
|
|
"text = \"Hello, do you like tea. Is this-- a test?\"\n",
|
|
"\n",
|
|
"tokenizer.encode(text)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dc53ee0c-fe2b-4cd8-a946-5471f7651acf",
|
|
"metadata": {},
|
|
"source": [
|
|
"- The above produces an error because the word \"Hello\" is not contained in the vocabulary\n",
|
|
"- To deal with such cases, we can add special tokens like `\"<|unk|>\"` to the vocabulary to represent unknown words\n",
|
|
"- Since we are already extending the vocabulary, let's add another token called `\"<|endoftext|>\"` which is used in GPT-2 training to denote the end of a text (and it's also used between concatenated text, like if our training datasets consists of multiple articles, books, etc.)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "ce9df29c-6c5b-43f1-8c1a-c7f7b79db78f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_tokens = sorted(list(set(preprocessed)))\n",
|
|
"all_tokens.extend([\"<|endoftext|>\", \"<|unk|>\"])\n",
|
|
"\n",
|
|
"vocab = {token:integer for integer,token in enumerate(all_tokens)}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "57c3143b-e860-4d3b-a22a-de22b547a6a9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1132"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(vocab.items())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "50e51bb1-ae05-4aa8-a9ff-455b65ed1959",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"('younger', 1127)\n",
|
|
"('your', 1128)\n",
|
|
"('yourself', 1129)\n",
|
|
"('<|endoftext|>', 1130)\n",
|
|
"('<|unk|>', 1131)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, item in enumerate(list(vocab.items())[-5:]):\n",
|
|
" print(item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a1daa2b0-6e75-412b-ab53-1f6fb7b4d453",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We also need to adjust the tokenizer accordingly so that it knows when and how to use the new `<unk>` token"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "948861c5-3f30-4712-a234-725f20d26f68",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class SimpleTokenizerV2:\n",
|
|
" def __init__(self, vocab):\n",
|
|
" self.str_to_int = vocab\n",
|
|
" self.int_to_str = { i:s for s,i in vocab.items()}\n",
|
|
" \n",
|
|
" def encode(self, text):\n",
|
|
" preprocessed = re.split(r'([,.:;?_!\"()\\']|--|\\s)', text)\n",
|
|
" preprocessed = [item.strip() for item in preprocessed if item.strip()]\n",
|
|
" preprocessed = [\n",
|
|
" item if item in self.str_to_int \n",
|
|
" else \"<|unk|>\" for item in preprocessed\n",
|
|
" ]\n",
|
|
"\n",
|
|
" ids = [self.str_to_int[s] for s in preprocessed]\n",
|
|
" return ids\n",
|
|
" \n",
|
|
" def decode(self, ids):\n",
|
|
" text = \" \".join([self.int_to_str[i] for i in ids])\n",
|
|
" # Replace spaces before the specified punctuations\n",
|
|
" text = re.sub(r'\\s+([,.:;?!\"()\\'])', r'\\1', text)\n",
|
|
" return text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "aa728dd1-9d35-4ac7-938f-d411d73083f6",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's try to tokenize text with the modified tokenizer:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "4133c502-18ac-4412-9f43-01caf4efa3dc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Hello, do you like tea? <|endoftext|> In the sunlit terraces of the palace.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer = SimpleTokenizerV2(vocab)\n",
|
|
"\n",
|
|
"text1 = \"Hello, do you like tea?\"\n",
|
|
"text2 = \"In the sunlit terraces of the palace.\"\n",
|
|
"\n",
|
|
"text = \" <|endoftext|> \".join((text1, text2))\n",
|
|
"\n",
|
|
"print(text)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "7ed395fe-dc1b-4ed2-b85b-457cc35aab60",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[1131, 5, 355, 1126, 628, 975, 10, 1130, 55, 988, 956, 984, 722, 988, 1131, 7]"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer.encode(text)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "059367f9-7a60-4c0d-8a00-7c4c766d0ebc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'<|unk|>, do you like tea? <|endoftext|> In the sunlit terraces of the <|unk|>.'"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tokenizer.decode(tokenizer.encode(text))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5c4ba34b-170f-4e71-939b-77aabb776f14",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2.5 BytePair encoding"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2309494c-79cf-4a2d-bc28-a94d602f050e",
|
|
"metadata": {},
|
|
"source": [
|
|
"- GPT-2 used BytePair encoding (BPE) as its tokenizer\n",
|
|
"- it allows the model to break down words that aren't in its predefined vocabulary into smaller subword units or even individual characters, enabling it to handle out-of-vocabulary words\n",
|
|
"- For instance, if GPT-2's vocabulary doesn't have the word \"unfamiliarword,\" it might tokenize it as [\"unfam\", \"iliar\", \"word\"] or some other subword breakdown, depending on its trained BPE merges\n",
|
|
"- The original BPE tokenizer can be found here: [https://github.com/openai/gpt-2/blob/master/src/encoder.py](https://github.com/openai/gpt-2/blob/master/src/encoder.py)\n",
|
|
"- In this chapter, we are using the BPE tokenizer from OpenAI's open-source [tiktoken](https://github.com/openai/tiktoken) library, which implements its core algorithms in Rust to improve computational performance\n",
|
|
"- I created a notebook in the [./bytepair_encoder](../02_bonus_bytepair-encoder) that compares these two implementations side-by-side (tiktoken was about 5x faster on the sample text)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "ede1d41f-934b-4bf4-8184-54394a257a94",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# pip install tiktoken"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "48967a77-7d17-42bf-9e92-fc619d63a59e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"tiktoken version: 0.7.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import importlib\n",
|
|
"import tiktoken\n",
|
|
"\n",
|
|
"print(\"tiktoken version:\", importlib.metadata.version(\"tiktoken\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "6ad3312f-a5f7-4efc-9d7d-8ea09d7b5128",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tokenizer = tiktoken.get_encoding(\"gpt2\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "5ff2cd85-7cfb-4325-b390-219938589428",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[15496, 11, 466, 345, 588, 8887, 30, 220, 50256, 554, 262, 4252, 18250, 8812, 2114, 1659, 617, 34680, 27271, 13]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"text = (\n",
|
|
" \"Hello, do you like tea? <|endoftext|> In the sunlit terraces\"\n",
|
|
" \"of someunknownPlace.\"\n",
|
|
")\n",
|
|
"\n",
|
|
"integers = tokenizer.encode(text, allowed_special={\"<|endoftext|>\"})\n",
|
|
"\n",
|
|
"print(integers)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "d26a48bb-f82e-41a8-a955-a1c9cf9d50ab",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Hello, do you like tea? <|endoftext|> In the sunlit terracesof someunknownPlace.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"strings = tokenizer.decode(integers)\n",
|
|
"\n",
|
|
"print(strings)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e8c2e7b4-6a22-42aa-8e4d-901f06378d4a",
|
|
"metadata": {},
|
|
"source": [
|
|
"- BPE tokenizers break down unknown words into subwords and individual characters:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c082d41f-33d7-4827-97d8-993d5a84bb3c",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/11.webp\" width=\"300px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "abbd7c0d-70f8-4386-a114-907e96c950b0",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2.6 Data sampling with a sliding window"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "509d9826-6384-462e-aa8a-a7c73cd6aad0",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We train LLMs to generate one word at a time, so we want to prepare the training data accordingly where the next word in a sequence represents the target to predict:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "39fb44f4-0c43-4a6a-9c2f-9cf31452354c",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/12.webp\" width=\"400px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "848d5ade-fd1f-46c3-9e31-1426e315c71b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"5145\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with open(\"the-verdict.txt\", \"r\", encoding=\"utf-8\") as f:\n",
|
|
" raw_text = f.read()\n",
|
|
"\n",
|
|
"enc_text = tokenizer.encode(raw_text)\n",
|
|
"print(len(enc_text))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cebd0657-5543-43ca-8011-2ae6bd0a5810",
|
|
"metadata": {},
|
|
"source": [
|
|
"- For each text chunk, we want the inputs and targets\n",
|
|
"- Since we want the model to predict the next word, the targets are the inputs shifted by one position to the right"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "e84424a7-646d-45b6-99e3-80d15fb761f2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"enc_sample = enc_text[50:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "dfbff852-a92f-48c8-a46d-143a0f109f40",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"x: [290, 4920, 2241, 287]\n",
|
|
"y: [4920, 2241, 287, 257]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"context_size = 4\n",
|
|
"\n",
|
|
"x = enc_sample[:context_size]\n",
|
|
"y = enc_sample[1:context_size+1]\n",
|
|
"\n",
|
|
"print(f\"x: {x}\")\n",
|
|
"print(f\"y: {y}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "815014ef-62f7-4476-a6ad-66e20e42b7c3",
|
|
"metadata": {},
|
|
"source": [
|
|
"- One by one, the prediction would look like as follows:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "d97b031e-ed55-409d-95f2-aeb38c6fe366",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[290] ----> 4920\n",
|
|
"[290, 4920] ----> 2241\n",
|
|
"[290, 4920, 2241] ----> 287\n",
|
|
"[290, 4920, 2241, 287] ----> 257\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in range(1, context_size+1):\n",
|
|
" context = enc_sample[:i]\n",
|
|
" desired = enc_sample[i]\n",
|
|
"\n",
|
|
" print(context, \"---->\", desired)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "f57bd746-dcbf-4433-8e24-ee213a8c34a1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" and ----> established\n",
|
|
" and established ----> himself\n",
|
|
" and established himself ----> in\n",
|
|
" and established himself in ----> a\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in range(1, context_size+1):\n",
|
|
" context = enc_sample[:i]\n",
|
|
" desired = enc_sample[i]\n",
|
|
"\n",
|
|
" print(tokenizer.decode(context), \"---->\", tokenizer.decode([desired]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "210d2dd9-fc20-4927-8d3d-1466cf41aae1",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We will take care of the next-word prediction in a later chapter after we covered the attention mechanism\n",
|
|
"- For now, we implement a simple data loader that iterates over the input dataset and returns the inputs and targets shifted by one"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a1a1b47a-f646-49d1-bc70-fddf2c840796",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Install and import PyTorch (see Appendix A for installation tips)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "e1770134-e7f3-4725-a679-e04c3be48cac",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"PyTorch version: 2.4.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import torch\n",
|
|
"print(\"PyTorch version:\", torch.__version__)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0c9a3d50-885b-49bc-b791-9f5cc8bc7b7c",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We use a sliding window approach, changing the position by +1:\n",
|
|
"\n",
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/13.webp?123\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "92ac652d-7b38-4843-9fbd-494cdc8ec12c",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Create dataset and dataloader that extract chunks from the input text dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "74b41073-4c9f-46e2-a1bd-d38e4122b375",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from torch.utils.data import Dataset, DataLoader\n",
|
|
"\n",
|
|
"\n",
|
|
"class GPTDatasetV1(Dataset):\n",
|
|
" def __init__(self, txt, tokenizer, max_length, stride):\n",
|
|
" self.input_ids = []\n",
|
|
" self.target_ids = []\n",
|
|
"\n",
|
|
" # Tokenize the entire text\n",
|
|
" token_ids = tokenizer.encode(txt, allowed_special={\"<|endoftext|>\"})\n",
|
|
"\n",
|
|
" # Use a sliding window to chunk the book into overlapping sequences of max_length\n",
|
|
" for i in range(0, len(token_ids) - max_length, stride):\n",
|
|
" input_chunk = token_ids[i:i + max_length]\n",
|
|
" target_chunk = token_ids[i + 1: i + max_length + 1]\n",
|
|
" self.input_ids.append(torch.tensor(input_chunk))\n",
|
|
" self.target_ids.append(torch.tensor(target_chunk))\n",
|
|
"\n",
|
|
" def __len__(self):\n",
|
|
" return len(self.input_ids)\n",
|
|
"\n",
|
|
" def __getitem__(self, idx):\n",
|
|
" return self.input_ids[idx], self.target_ids[idx]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "5eb30ebe-97b3-43c5-9ff1-a97d621b3c4e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def create_dataloader_v1(txt, batch_size=4, max_length=256, \n",
|
|
" stride=128, shuffle=True, drop_last=True,\n",
|
|
" num_workers=0):\n",
|
|
"\n",
|
|
" # Initialize the tokenizer\n",
|
|
" tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
|
|
"\n",
|
|
" # Create dataset\n",
|
|
" dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)\n",
|
|
"\n",
|
|
" # Create dataloader\n",
|
|
" dataloader = DataLoader(\n",
|
|
" dataset,\n",
|
|
" batch_size=batch_size,\n",
|
|
" shuffle=shuffle,\n",
|
|
" drop_last=drop_last,\n",
|
|
" num_workers=num_workers\n",
|
|
" )\n",
|
|
"\n",
|
|
" return dataloader"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "42dd68ef-59f7-45ff-ba44-e311c899ddcd",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Let's test the dataloader with a batch size of 1 for an LLM with a context size of 4:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "df31d96c-6bfd-4564-a956-6192242d7579",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"with open(\"the-verdict.txt\", \"r\", encoding=\"utf-8\") as f:\n",
|
|
" raw_text = f.read()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "9226d00c-ad9a-4949-a6e4-9afccfc7214f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[tensor([[ 40, 367, 2885, 1464]]), tensor([[ 367, 2885, 1464, 1807]])]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"dataloader = create_dataloader_v1(\n",
|
|
" raw_text, batch_size=1, max_length=4, stride=1, shuffle=False\n",
|
|
")\n",
|
|
"\n",
|
|
"data_iter = iter(dataloader)\n",
|
|
"first_batch = next(data_iter)\n",
|
|
"print(first_batch)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "10deb4bc-4de1-4d20-921e-4b1c7a0e1a6d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[tensor([[ 367, 2885, 1464, 1807]]), tensor([[2885, 1464, 1807, 3619]])]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"second_batch = next(data_iter)\n",
|
|
"print(second_batch)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b006212f-de45-468d-bdee-5806216d1679",
|
|
"metadata": {},
|
|
"source": [
|
|
"- An example using stride equal to the context length (here: 4) as shown below:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9cb467e0-bdcd-4dda-b9b0-a738c5d33ac3",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/14.webp\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b1ae6d45-f26e-4b83-9c7b-cff55ffa7d16",
|
|
"metadata": {},
|
|
"source": [
|
|
"- We can also create batched outputs\n",
|
|
"- Note that we increase the stride here so that we don't have overlaps between the batches, since more overlap could lead to increased overfitting"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "1916e7a6-f03d-4f09-91a6-d0bdbac5a58c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Inputs:\n",
|
|
" tensor([[ 40, 367, 2885, 1464],\n",
|
|
" [ 1807, 3619, 402, 271],\n",
|
|
" [10899, 2138, 257, 7026],\n",
|
|
" [15632, 438, 2016, 257],\n",
|
|
" [ 922, 5891, 1576, 438],\n",
|
|
" [ 568, 340, 373, 645],\n",
|
|
" [ 1049, 5975, 284, 502],\n",
|
|
" [ 284, 3285, 326, 11]])\n",
|
|
"\n",
|
|
"Targets:\n",
|
|
" tensor([[ 367, 2885, 1464, 1807],\n",
|
|
" [ 3619, 402, 271, 10899],\n",
|
|
" [ 2138, 257, 7026, 15632],\n",
|
|
" [ 438, 2016, 257, 922],\n",
|
|
" [ 5891, 1576, 438, 568],\n",
|
|
" [ 340, 373, 645, 1049],\n",
|
|
" [ 5975, 284, 502, 284],\n",
|
|
" [ 3285, 326, 11, 287]])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"dataloader = create_dataloader_v1(raw_text, batch_size=8, max_length=4, stride=4, shuffle=False)\n",
|
|
"\n",
|
|
"data_iter = iter(dataloader)\n",
|
|
"inputs, targets = next(data_iter)\n",
|
|
"print(\"Inputs:\\n\", inputs)\n",
|
|
"print(\"\\nTargets:\\n\", targets)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2cd2fcda-2fda-4aa8-8bc8-de1e496f9db1",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2.7 Creating token embeddings"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1a301068-6ab2-44ff-a915-1ba11688274f",
|
|
"metadata": {},
|
|
"source": [
|
|
"- The data is already almost ready for an LLM\n",
|
|
"- But lastly let us embed the tokens in a continuous vector representation using an embedding layer\n",
|
|
"- Usually, these embedding layers are part of the LLM itself and are updated (trained) during model training"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e85089aa-8671-4e5f-a2b3-ef252004ee4c",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/15.webp\" width=\"400px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44e014ca-1fc5-4b90-b6fa-c2097bb92c0b",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Suppose we have the following four input examples with input ids 2, 3, 5, and 1 (after tokenization):"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "15a6304c-9474-4470-b85d-3991a49fa653",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"input_ids = torch.tensor([2, 3, 5, 1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "14da6344-2c71-4837-858d-dd120005ba05",
|
|
"metadata": {},
|
|
"source": [
|
|
"- For the sake of simplicity, suppose we have a small vocabulary of only 6 words and we want to create embeddings of size 3:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "93cb2cee-9aa6-4bb8-8977-c65661d16eda",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vocab_size = 6\n",
|
|
"output_dim = 3\n",
|
|
"\n",
|
|
"torch.manual_seed(123)\n",
|
|
"embedding_layer = torch.nn.Embedding(vocab_size, output_dim)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4ff241f6-78eb-4e4a-a55f-5b2b6196d5b0",
|
|
"metadata": {},
|
|
"source": [
|
|
"- This would result in a 6x3 weight matrix:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "a686eb61-e737-4351-8f1c-222913d47468",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameter containing:\n",
|
|
"tensor([[ 0.3374, -0.1778, -0.1690],\n",
|
|
" [ 0.9178, 1.5810, 1.3010],\n",
|
|
" [ 1.2753, -0.2010, -0.1606],\n",
|
|
" [-0.4015, 0.9666, -1.1481],\n",
|
|
" [-1.1589, 0.3255, -0.6315],\n",
|
|
" [-2.8400, -0.7849, -1.4096]], requires_grad=True)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(embedding_layer.weight)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "26fcf4f5-0801-4eb4-bb90-acce87935ac7",
|
|
"metadata": {},
|
|
"source": [
|
|
"- For those who are familiar with one-hot encoding, the embedding layer approach above is essentially just a more efficient way of implementing one-hot encoding followed by matrix multiplication in a fully-connected layer, which is described in the supplementary code in [./embedding_vs_matmul](../03_bonus_embedding-vs-matmul)\n",
|
|
"- Because the embedding layer is just a more efficient implementation that is equivalent to the one-hot encoding and matrix-multiplication approach it can be seen as a neural network layer that can be optimized via backpropagation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4b0d58c3-83c0-4205-aca2-9c48b19fd4a7",
|
|
"metadata": {},
|
|
"source": [
|
|
"- To convert a token with id 3 into a 3-dimensional vector, we do the following:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "e43600ba-f287-4746-8ddf-d0f71a9023ca",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"tensor([[-0.4015, 0.9666, -1.1481]], grad_fn=<EmbeddingBackward0>)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(embedding_layer(torch.tensor([3])))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a7bbf625-4f36-491d-87b4-3969efb784b0",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Note that the above is the 4th row in the `embedding_layer` weight matrix\n",
|
|
"- To embed all four `input_ids` values above, we do"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "50280ead-0363-44c8-8c35-bb885d92c8b7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"tensor([[ 1.2753, -0.2010, -0.1606],\n",
|
|
" [-0.4015, 0.9666, -1.1481],\n",
|
|
" [-2.8400, -0.7849, -1.4096],\n",
|
|
" [ 0.9178, 1.5810, 1.3010]], grad_fn=<EmbeddingBackward0>)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(embedding_layer(input_ids))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "be97ced4-bd13-42b7-866a-4d699a17e155",
|
|
"metadata": {},
|
|
"source": [
|
|
"- An embedding layer is essentially a look-up operation:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f33c2741-bf1b-4c60-b7fd-61409d556646",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/16.webp?123\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "08218d9f-aa1a-4afb-a105-72ff96a54e73",
|
|
"metadata": {},
|
|
"source": [
|
|
"- **You may be interested in the bonus content comparing embedding layers with regular linear layers: [../03_bonus_embedding-vs-matmul](../03_bonus_embedding-vs-matmul)**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c393d270-b950-4bc8-99ea-97d74f2ea0f6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2.8 Encoding word positions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "24940068-1099-4698-bdc0-e798515e2902",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Embedding layer convert IDs into identical vector representations regardless of where they are located in the input sequence:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9e0b14a2-f3f3-490e-b513-f262dbcf94fa",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/17.webp\" width=\"400px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "92a7d7fe-38a5-46e6-8db6-b688887b0430",
|
|
"metadata": {},
|
|
"source": [
|
|
"- Positional embeddings are combined with the token embedding vector to form the input embeddings for a large language model:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "48de37db-d54d-45c4-ab3e-88c0783ad2e4",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/18.webp\" width=\"500px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7f187f87-c1f8-4c2e-8050-350bbb972f55",
|
|
"metadata": {},
|
|
"source": [
|
|
"- The BytePair encoder has a vocabulary size of 50,257:\n",
|
|
"- Suppose we want to encode the input tokens into a 256-dimensional vector representation:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "0b9e344d-03a6-4f2c-b723-67b6a20c5041",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vocab_size = 50257\n",
|
|
"output_dim = 256\n",
|
|
"\n",
|
|
"token_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a2654722-24e4-4b0d-a43c-436a461eb70b",
|
|
"metadata": {},
|
|
"source": [
|
|
"- If we sample data from the dataloader, we embed the tokens in each batch into a 256-dimensional vector\n",
|
|
"- If we have a batch size of 8 with 4 tokens each, this results in a 8 x 4 x 256 tensor:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "ad56a263-3d2e-4d91-98bf-d0b68d3c7fc3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"max_length = 4\n",
|
|
"dataloader = create_dataloader_v1(\n",
|
|
" raw_text, batch_size=8, max_length=max_length,\n",
|
|
" stride=max_length, shuffle=False\n",
|
|
")\n",
|
|
"data_iter = iter(dataloader)\n",
|
|
"inputs, targets = next(data_iter)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "84416b60-3707-4370-bcbc-da0b62f2b64d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Token IDs:\n",
|
|
" tensor([[ 40, 367, 2885, 1464],\n",
|
|
" [ 1807, 3619, 402, 271],\n",
|
|
" [10899, 2138, 257, 7026],\n",
|
|
" [15632, 438, 2016, 257],\n",
|
|
" [ 922, 5891, 1576, 438],\n",
|
|
" [ 568, 340, 373, 645],\n",
|
|
" [ 1049, 5975, 284, 502],\n",
|
|
" [ 284, 3285, 326, 11]])\n",
|
|
"\n",
|
|
"Inputs shape:\n",
|
|
" torch.Size([8, 4])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"Token IDs:\\n\", inputs)\n",
|
|
"print(\"\\nInputs shape:\\n\", inputs.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "7766ec38-30d0-4128-8c31-f49f063c43d1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch.Size([8, 4, 256])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"token_embeddings = token_embedding_layer(inputs)\n",
|
|
"print(token_embeddings.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fe2ae164-6f19-4e32-b9e5-76950fcf1c9f",
|
|
"metadata": {},
|
|
"source": [
|
|
"- GPT-2 uses absolute position embeddings, so we just create another embedding layer:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "cc048e20-7ac8-417e-81f5-8fe6f9a4fe07",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"context_length = max_length\n",
|
|
"pos_embedding_layer = torch.nn.Embedding(context_length, output_dim)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"id": "c369a1e7-d566-4b53-b398-d6adafb44105",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch.Size([4, 256])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"pos_embeddings = pos_embedding_layer(torch.arange(max_length))\n",
|
|
"print(pos_embeddings.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "870e9d9f-2935-461a-9518-6d1386b976d6",
|
|
"metadata": {},
|
|
"source": [
|
|
"- To create the input embeddings used in an LLM, we simply add the token and the positional embeddings:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"id": "b22fab89-526e-43c8-9035-5b7018e34288",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch.Size([8, 4, 256])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"input_embeddings = token_embeddings + pos_embeddings\n",
|
|
"print(input_embeddings.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1fbda581-6f9b-476f-8ea7-d244e6a4eaec",
|
|
"metadata": {},
|
|
"source": [
|
|
"- In the initial phase of the input processing workflow, the input text is segmented into separate tokens\n",
|
|
"- Following this segmentation, these tokens are transformed into token IDs based on a predefined vocabulary:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d1bb0f7e-460d-44db-b366-096adcd84fff",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch02_compressed/19.webp\" width=\"400px\">"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "63230f2e-258f-4497-9e2e-8deee4530364",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Summary and takeaways"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8b3293a6-45a5-47cd-aa00-b23e3ca0a73f",
|
|
"metadata": {},
|
|
"source": [
|
|
"See the [./dataloader.ipynb](./dataloader.ipynb) code notebook, which is a concise version of the data loader that we implemented in this chapter and will need for training the GPT model in upcoming chapters.\n",
|
|
"\n",
|
|
"See [./exercise-solutions.ipynb](./exercise-solutions.ipynb) for the exercise solutions."
|
|
]
|
|
}
|
|
],
|
|
"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": "3.11.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|