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"## 1. Load the dataset\n",
"\n",
"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",
"\n",
"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."
]
},
{
"attachments": {},
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"source": [
"To run this notebook, you will need to install: pandas, openai, transformers, plotly, matplotlib, scikit-learn, torch (transformer dep), torchvision, and scipy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>ProductId</th>\n",
" <th>UserId</th>\n",
" <th>Score</th>\n",
" <th>Summary</th>\n",
" <th>Text</th>\n",
" <th>combined</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1351123200</td>\n",
" <td>B003XPF9BO</td>\n",
" <td>A3R7JR3FMEBXQB</td>\n",
" <td>5</td>\n",
" <td>where does one start...and stop... with a tre...</td>\n",
" <td>Wanted to save some to bring to my Chicago fam...</td>\n",
" <td>Title: where does one start...and stop... wit...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1351123200</td>\n",
" <td>B003JK537S</td>\n",
" <td>A3JBPC3WFUT5ZP</td>\n",
" <td>1</td>\n",
" <td>Arrived in pieces</td>\n",
" <td>Not pleased at all. When I opened the box, mos...</td>\n",
" <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" Time ProductId UserId Score \\\n",
"0 1351123200 B003XPF9BO A3R7JR3FMEBXQB 5 \n",
"1 1351123200 B003JK537S A3JBPC3WFUT5ZP 1 \n",
"\n",
" Summary \\\n",
"0 where does one start...and stop... with a tre... \n",
"1 Arrived in pieces \n",
"\n",
" Text \\\n",
"0 Wanted to save some to bring to my Chicago fam... \n",
"1 Not pleased at all. When I opened the box, mos... \n",
"\n",
" combined \n",
"0 Title: where does one start...and stop... wit... \n",
"1 Title: Arrived in pieces; Content: Not pleased... "
]
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"import pandas as pd\n",
"\n",
"input_datapath = 'data/fine_food_reviews_1k.csv' # to save space, we provide a pre-filtered dataset\n",
"df = pd.read_csv(input_datapath, index_col=0)\n",
"df = df[['Time', 'ProductId', 'UserId', 'Score', 'Summary', 'Text']]\n",
"df = df.dropna()\n",
"df['combined'] = \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
"df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# subsample to 1k most recent reviews and remove samples that are too long\n",
"df = df.sort_values('Time').tail(1_100)\n",
"df.drop('Time', axis=1, inplace=True)\n",
"\n",
"from transformers import GPT2TokenizerFast\n",
"tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n",
"\n",
"# remove reviews that are too long\n",
"df['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\n",
"df = df[df.n_tokens<8000].tail(1_000)\n",
"len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Get embeddings and save them for future reuse"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import openai\n",
"from openai.embeddings_utils import get_embedding\n",
"# Ensure you have your API key set in your environment per the README: https://github.com/openai/openai-python#usage\n",
"\n",
"# This will take just between 5 and 10 minutes\n",
"df['ada_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
"df['ada_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
"df.to_csv('data/fine_food_reviews_with_embeddings_1k.csv')"
]
}
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