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188 lines
6.0 KiB
Plaintext
188 lines
6.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Load the dataset\n",
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"\n",
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"The dataset used in this example is [fine-food reviews](https://www.kaggle.com/snap/amazon-fine-food-reviews) from Amazon. The dataset contains a total of 568,454 food reviews Amazon users left up to October 2012. We will use a subset of this dataset, consisting of 1,000 most recent reviews for illustration purposes. The reviews are in English and tend to be positive or negative. Each review has a ProductId, UserId, Score, review title (Summary) and review body (Text).\n",
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"\n",
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"We will combine the review summary and review text into a single combined text. The model will encode this combined text and it will output a single vector embedding."
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Time</th>\n",
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" <th>ProductId</th>\n",
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" <th>UserId</th>\n",
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" <th>Score</th>\n",
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" <th>Summary</th>\n",
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" <th>Text</th>\n",
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" <th>combined</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1351123200</td>\n",
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" <td>B003XPF9BO</td>\n",
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" <td>A3R7JR3FMEBXQB</td>\n",
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" <td>5</td>\n",
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" <td>where does one start...and stop... with a tre...</td>\n",
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" <td>Wanted to save some to bring to my Chicago fam...</td>\n",
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" <td>Title: where does one start...and stop... wit...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1351123200</td>\n",
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" <td>B003JK537S</td>\n",
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" <td>A3JBPC3WFUT5ZP</td>\n",
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" <td>1</td>\n",
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" <td>Arrived in pieces</td>\n",
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" <td>Not pleased at all. When I opened the box, mos...</td>\n",
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" <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Time ProductId UserId Score \\\n",
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"0 1351123200 B003XPF9BO A3R7JR3FMEBXQB 5 \n",
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"1 1351123200 B003JK537S A3JBPC3WFUT5ZP 1 \n",
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"\n",
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" Summary \\\n",
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"0 where does one start...and stop... with a tre... \n",
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"1 Arrived in pieces \n",
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"\n",
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" Text \\\n",
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"0 Wanted to save some to bring to my Chicago fam... \n",
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"1 Not pleased at all. When I opened the box, mos... \n",
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"\n",
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" combined \n",
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"0 Title: where does one start...and stop... wit... \n",
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"1 Title: Arrived in pieces; Content: Not pleased... "
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]
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},
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"execution_count": 1,
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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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"import pandas as pd\n",
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"\n",
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"input_datapath = 'data/fine_food_reviews_1k.csv' # to save space, we provide a pre-filtered dataset\n",
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"df = pd.read_csv(input_datapath, index_col=0)\n",
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"df = df[['Time', 'ProductId', 'UserId', 'Score', 'Summary', 'Text']]\n",
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"df = df.dropna()\n",
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"df['combined'] = \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
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"df.head(2)"
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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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"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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"1000"
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]
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},
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"execution_count": 2,
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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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"# subsample to 1k most recent reviews and remove samples that are too long\n",
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"df = df.sort_values('Time').tail(1_100)\n",
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"df.drop('Time', axis=1, inplace=True)\n",
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"\n",
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"from transformers import GPT2TokenizerFast\n",
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"tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n",
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"\n",
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"# remove reviews that are too long\n",
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"df['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\n",
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"df = df[df.n_tokens<2000].tail(1_000)\n",
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"len(df)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Get embeddings and save them for future reuse"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from openai.embeddings_utils import get_embedding\n",
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"\n",
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"# This will take just under 10 minutes\n",
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"df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-similarity-babbage-001'))\n",
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"df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-search-babbage-doc-001'))\n",
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"df.to_csv('data/fine_food_reviews_with_embeddings_1k.csv')"
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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 3.9.9 ('openai')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
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"nbformat": 4,
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