"# If you have not run the \"Obtain_dataset.ipynb\" notebook, you can download the datafile from here: https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\n",
"print(f\"Ada similarity embedding performance on 1k Amazon reviews: mse={mse:.2f}, mae={mae:.2f}\")\n"
"print(f\"ada-002 embedding performance on 1k Amazon reviews: mse={mse:.2f}, mae={mae:.2f}\")\n"
]
},
{
@ -76,7 +75,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the embeddings are able to predict the scores with an average error of 0.60 per score prediction. This is roughly equivalent to predicting 1 out of 3 reviews perfectly, and 1 out of two reviews by a one star error."
"We can see that the embeddings are able to predict the scores with an average error of 0.53 per score prediction. This is roughly equivalent to predicting half of reviews perfectly, and half off by one star."
"# If you have not run the \"Obtain_dataset.ipynb\" notebook, you can download the datafile from here: https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\n",