mirror of
https://github.com/rasbt/LLMs-from-scratch.git
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70cfced899
* fix typos, add codespell pre-commit hook * Update .pre-commit-config.yaml --------- Co-authored-by: Sebastian Raschka <mail@sebastianraschka.com>
427 lines
12 KiB
Plaintext
427 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "136a4efe-fb99-4311-8679-e0a5b6282755",
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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>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b1910a06-e8a3-40ac-8201-ff70615b1ba4",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Create \"Passive Voice\" Entries for an Instruction Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a128651b-f326-4232-a994-42f38b7ed520",
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"metadata": {},
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"source": [
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"- This notebook uses OpenAI's GPT-4 to create \"passive voice\" entries for an instruction dataset, as shown in the example below\n",
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"\n",
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"```python\n",
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"{ \n",
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" 'instruction': 'Identify the verb in the following sentence',\n",
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" 'input': 'The cat sleeps on the couch.',\n",
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" 'output': 'The verb in the sentence is \"sleeps.\"',\n",
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" 'output_2': 'The sentence is \"sleeps.\"' # <---- Newly created entry\n",
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"} \n",
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"```"
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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": "267ba0d1-b884-42df-85bd-0be746fd47a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install -r requirements-extra.txt"
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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": "63610acc-db94-437f-8d38-e99dca0299cb",
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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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"openai version: 1.30.3\n",
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"tqdm version: 4.65.0\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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"pkgs = [\"openai\", # OpenAI API\n",
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" \"tqdm\", # Progress bar\n",
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" ]\n",
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"\n",
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"for p in pkgs:\n",
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" print(f\"{p} version: {version(p)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8bcdcb34-ac75-4f4f-9505-3ce0666c42d5",
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"metadata": {},
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"source": [
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"## Test OpenAI API"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9558a522-650d-401a-84fc-9fd7b1f39da7",
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"metadata": {},
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"source": [
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"- First, let's test if the OpenAI API is correctly set up\n",
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"- If you don't have an account yet, you need to create one at https://platform.openai.com/\n",
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"- Note that you will also have to transfer some funds to your account as the GPT-4 API is not free (see https://platform.openai.com/settings/organization/billing/overview)\n",
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"- Creating the ~200 passive voice entries using the code in this notebook costs about $0.13 (13 cents)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "89343a84-0ddc-42fc-bf50-298a342b93c0",
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"metadata": {},
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"source": [
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"- First, we need to provide our OpenAI API secret key, which can be found at https://platform.openai.com/api-keys\n",
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"- Make sure not to share this key with anyone\n",
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"- Add this secret key (`\"sk-...\"`) to the `config.json` file in this folder"
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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": "26900564-aba7-48ba-8ee8-6cc9a505a25c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"from openai import OpenAI\n",
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"\n",
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"# Load API key from a JSON file. \n",
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"# Make sure to replace \"sk-...\" with your actual API key from https://platform.openai.com/api-keys\n",
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"with open(\"config.json\", \"r\") as config_file:\n",
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" config = json.load(config_file)\n",
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" api_key = config[\"OPENAI_API_KEY\"]\n",
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"\n",
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"client = OpenAI(api_key=api_key)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16642a48-1cab-40d2-af08-ab8c2fbf5876",
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"metadata": {},
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"source": [
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"- First, let's try the API with a simple example to make sure it works as intended:"
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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": "08e9ef2e-e816-4283-840e-43625791ad33",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Breakfast was eaten by me.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def run_chatgpt(prompt, client, model=\"gpt-4-turbo\"):\n",
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" response = client.chat.completions.create(\n",
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" model=model,\n",
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" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
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" temperature=0.0,\n",
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" )\n",
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" return response.choices[0].message.content\n",
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"\n",
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"\n",
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"# Prepare input\n",
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"sentence = \"I ate breakfast\"\n",
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"prompt = f\"Convert the following sentence to passive voice: '{sentence}'\"\n",
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"run_chatgpt(prompt, client)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "162a4739-6f03-4092-a5c2-f57a0b6a4c4d",
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"metadata": {},
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"source": [
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"## Create JSON Entries"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ca011a8b-20c5-4101-979e-9b5fccf62f8a",
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"metadata": {},
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"source": [
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"- Next, we load the file we want to modify:"
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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": "8b2d393a-aa92-4190-9d44-44326a6f699b",
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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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"Number of entries: 200\n"
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]
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}
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],
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"source": [
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"import json\n",
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"\n",
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"json_file = \"instruction-examples.json\"\n",
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"\n",
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"with open(json_file, \"r\") as file:\n",
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" json_data = json.load(file)\n",
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" \n",
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"print(\"Number of entries:\", len(json_data))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "39a55283-7d51-4136-ba60-f799d49f4098",
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"metadata": {},
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"source": [
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"- And we try the OpenAI chat API on a small sample first to ensure that it works correctly:"
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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": "735cc089-d127-480a-b39d-0782581f0c41",
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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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"\n",
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"Input:\n",
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">> The verb in the sentence is \"sleeps.\"\n",
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"\n",
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"Output:\n",
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">> The sentence is \"sleeps.\"\n",
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"\n",
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"-------------------------\n",
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"\n",
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"Input:\n",
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">> The plural form of \"goose\" is \"geese.\"\n",
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"\n",
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"Output:\n",
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">> The plural form of \"goose\" is referred to as \"geese.\"\n",
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"\n",
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"-------------------------\n",
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"\n",
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"Input:\n",
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">> The three primary colors are red, blue, and yellow.\n",
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"\n",
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"Output:\n",
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">> Red, blue, and yellow are considered the three primary colors.\n",
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"\n",
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"-------------------------\n",
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"\n",
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"Input:\n",
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">> They had finished the game.\n",
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"\n",
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"Output:\n",
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">> The game had been finished by them.\n",
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"\n",
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"-------------------------\n",
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"\n",
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"Input:\n",
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">> The abbreviation for \"Doctor of Philosophy\" is Ph.D.\n",
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"\n",
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"Output:\n",
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">> The abbreviation \"Ph.D.\" is used for \"Doctor of Philosophy\".\n",
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"\n",
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"-------------------------\n"
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]
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}
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],
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"source": [
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"for entry in json_data[:5]:\n",
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" text = entry[\"output\"]\n",
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" prompt = f\"Without adding any response or explanation, convert the following text to passive voice: {text}\"\n",
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" \n",
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" print(\"\\nInput:\")\n",
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" print(\">>\", text)\n",
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" print(\"\\nOutput:\")\n",
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" print(\">>\", run_chatgpt(prompt, client))\n",
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" print(\"\\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": "142dfaa7-429f-4eb0-b74d-ff327f79547a",
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"metadata": {},
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"source": [
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"- Let's now extend the code to add the generated entries to the `json_data` and add a progress bar:"
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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": "4f700d4b-19e5-4404-afa7-b0f093024232",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████████████████████████████████████████████████████████████████| 5/5 [00:04<00:00, 1.23it/s]\n"
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]
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}
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],
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"source": [
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"from tqdm import tqdm # a progress bar tool\n",
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"\n",
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"\n",
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"for i, entry in tqdm(enumerate(json_data[:5]), total=len(json_data[:5])):\n",
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" text = entry[\"output\"]\n",
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" prompt = f\"Without adding any response or explanation, convert the following text to passive voice: {text}\"\n",
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" json_data[i][\"output_2\"] = run_chatgpt(prompt, client)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cd144282-0596-4e9b-9815-322cff34b400",
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"metadata": {},
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"source": [
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"- One more time, let's make sure that the new entries (`\"output_2\"`) look ok"
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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": "5b6eaa87-a86d-42a1-a20a-b764b0d559d4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'instruction': 'Identify the verb in the following sentence: The cat sleeps on the couch.',\n",
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" 'input': '',\n",
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" 'output': 'The verb in the sentence is \"sleeps.\"',\n",
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" 'output_2': 'The sentence is \"sleeps.\"'}"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"json_data[0]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6970e8cf-2b18-4e3d-9f25-e6a4489c39a7",
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"metadata": {},
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"source": [
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"- Finally, if everything above looks ok, let's run the conversion to passive voice on our entire json dataset (this takes about 3 minutes):"
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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": "eef99407-8ffd-4a63-b7ab-ffe30c0f0677",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████████████████████████████████████████████████████████████| 200/200 [03:43<00:00, 1.12s/it]\n"
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]
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}
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],
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"source": [
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"for i, entry in tqdm(enumerate(json_data), total=len(json_data)):\n",
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" text = entry[\"output\"]\n",
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" prompt = f\"Without adding any response or explanation, convert the following text to passive voice: {text}\"\n",
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" json_data[i][\"output_2\"] = run_chatgpt(prompt, client)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac91ae85-2f0e-456a-be1d-56e1958f30d8",
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"metadata": {},
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"source": [
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"- After the conversion is completed, we save the file:"
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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": "330cc30a-b08e-4bf0-bee2-bec0da4208de",
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"metadata": {},
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"outputs": [],
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"source": [
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"new_json_file = json_file.replace(\".json\", \"-modified.json\")\n",
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"\n",
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"\n",
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"with open(new_json_file, \"w\") as file:\n",
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" json.dump(json_data, file, indent=4) # \"indent\" for pretty-printing"
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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": "3.10.6"
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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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