wehub-resource-sync
702 lines
23 KiB
Plaintext
702 lines
23 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "ur8xi4C7S06n"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Copyright 2024 Google LLC\n",
|
||
"#\n",
|
||
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
|
||
"# you may not use this file except in compliance with the License.\n",
|
||
"# You may obtain a copy of the License at\n",
|
||
"#\n",
|
||
"# https://www.apache.org/licenses/LICENSE-2.0\n",
|
||
"#\n",
|
||
"# Unless required by applicable law or agreed to in writing, software\n",
|
||
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
|
||
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
|
||
"# See the License for the specific language governing permissions and\n",
|
||
"# limitations under the License."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "JAPoU8Sm5E6e"
|
||
},
|
||
"source": [
|
||
"# Text Classification with Generative Models on Vertex AI\n",
|
||
"\n",
|
||
"\n",
|
||
"<table align=\"left\">\n",
|
||
" <td style=\"text-align: center\">\n",
|
||
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\">\n",
|
||
" <img width=\"32px\" src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
|
||
" </a>\n",
|
||
" </td>\n",
|
||
" <td style=\"text-align: center\">\n",
|
||
" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fgemini%2Fprompts%2Fexamples%2Ftext_classification.ipynb\">\n",
|
||
" <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
|
||
" </a>\n",
|
||
" </td> \n",
|
||
" <td style=\"text-align: center\">\n",
|
||
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/gemini/prompts/examples/text_classification.ipynb\">\n",
|
||
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
|
||
" </a>\n",
|
||
" </td>\n",
|
||
" <td style=\"text-align: center\">\n",
|
||
" <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\">\n",
|
||
" <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
|
||
" </a>\n",
|
||
" </td>\n",
|
||
"</table>\n",
|
||
"\n",
|
||
"<div style=\"clear: both;\"></div>\n",
|
||
"\n",
|
||
"<b>Share to:</b>\n",
|
||
"\n",
|
||
"<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\" target=\"_blank\">\n",
|
||
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/8/81/LinkedIn_icon.svg\" alt=\"LinkedIn logo\">\n",
|
||
"</a>\n",
|
||
"\n",
|
||
"<a href=\"https://bsky.app/intent/compose?text=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\" target=\"_blank\">\n",
|
||
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg\" alt=\"Bluesky logo\">\n",
|
||
"</a>\n",
|
||
"\n",
|
||
"<a href=\"https://twitter.com/intent/tweet?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\" target=\"_blank\">\n",
|
||
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/5a/X_icon_2.svg\" alt=\"X logo\">\n",
|
||
"</a>\n",
|
||
"\n",
|
||
"<a href=\"https://reddit.com/submit?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\" target=\"_blank\">\n",
|
||
" <img width=\"20px\" src=\"https://redditinc.com/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png\" alt=\"Reddit logo\">\n",
|
||
"</a>\n",
|
||
"\n",
|
||
"<a href=\"https://www.facebook.com/sharer/sharer.php?u=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/examples/text_classification.ipynb\" target=\"_blank\">\n",
|
||
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/51/Facebook_f_logo_%282019%29.svg\" alt=\"Facebook logo\">\n",
|
||
"</a> \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "PpFdtPxGhVST"
|
||
},
|
||
"source": [
|
||
"| Authors |\n",
|
||
"| --- |\n",
|
||
"| [Polong Lin](https://github.com/polong-lin) |\n",
|
||
"| [Deepak Moonat](https://github.com/dmoonat) |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "tvgnzT1CKxrO"
|
||
},
|
||
"source": [
|
||
"## Overview\n",
|
||
"\n",
|
||
"Generative models like Gemini are powerful language models used for various natural language processing (NLP) tasks. One of those is text classification, which involves assigning one or more categories to a given piece of text. Although text classification can be done using traditional NLP techniques, LLMs can perform classification by providing prompts (as opposed to domain-specific labeled data), which can accelerate the time it takes to build a text classification solution. Classification models based on LLMs can be further tuned with many examples via custom model training, but that is beyond the scope of this notebook.\n",
|
||
"\n",
|
||
"In this notebook, you will explore how to do text classification using prompts with the Gemini API. Learn more about classification prompts in the [official documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/text/classification-prompts)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "d975e698c9a4"
|
||
},
|
||
"source": [
|
||
"### Objective\n",
|
||
"\n",
|
||
"By the end of the notebook, you should be able to use a large language model to perform various classification tasks, including:\n",
|
||
"\n",
|
||
"* Zero-shot prompting text classification\n",
|
||
"* Few-shot prompting text classification\n",
|
||
"* Common tasks:\n",
|
||
" * Sentiment analysis\n",
|
||
" * Topic classification\n",
|
||
" * Spam detection\n",
|
||
" * Intent recognition\n",
|
||
" * Language identification\n",
|
||
" * Toxicity detection\n",
|
||
" * Emotion detection"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "nSyXtwyz_o_v"
|
||
},
|
||
"source": [
|
||
"## Getting Started"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "2a5AEr0lkLKD"
|
||
},
|
||
"source": [
|
||
"### Install Vertex AI SDK and other required packages"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "82ad0c445061"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%pip install google-cloud-aiplatform --upgrade -q\n",
|
||
"%pip install pandas scikit-learn -q"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Xe7OuYuGkLKF"
|
||
},
|
||
"source": [
|
||
"### Authenticate your notebook environment (Colab only)\n",
|
||
"\n",
|
||
"If you are running this notebook on Google Colab, run the cell below to authenticate your environment.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "U9Gx2SAZkLKF"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import sys\n",
|
||
"\n",
|
||
"if \"google.colab\" in sys.modules:\n",
|
||
" from google.colab import auth\n",
|
||
"\n",
|
||
" auth.authenticate_user()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "5RVGmd8dhVSV"
|
||
},
|
||
"source": [
|
||
"### Set Google Cloud project information and initialize Vertex AI SDK\n",
|
||
"\n",
|
||
"To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
|
||
"\n",
|
||
"Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment).\"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "AhDbnhB0hVSV"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"PROJECT_ID = \"your-project-id\" # @param {type:\"string\"}\n",
|
||
"LOCATION = \"us-central1\" # @param {type:\"string\"}\n",
|
||
"\n",
|
||
"import vertexai\n",
|
||
"\n",
|
||
"vertexai.init(project=PROJECT_ID, location=LOCATION)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "960505627ddf"
|
||
},
|
||
"source": [
|
||
"### Import libraries"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "PyQmSRbKA8r-"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import warnings\n",
|
||
"\n",
|
||
"warnings.filterwarnings(\"ignore\")\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"from sklearn.metrics import classification_report\n",
|
||
"from vertexai.generative_models import GenerationConfig, GenerativeModel"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "UP76a2la7O-a"
|
||
},
|
||
"source": [
|
||
"### Import model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "7isig7e07O-a"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"generation_model = GenerativeModel(\"gemini-2.0-flash\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RvHZ-R0umYYR"
|
||
},
|
||
"source": [
|
||
"#### Generation config\n",
|
||
"\n",
|
||
"- Each call that you send to a model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values\n",
|
||
"- <strong>Experiment</strong> with different parameter values to get the best values for the task\n",
|
||
"\n",
|
||
"Refer to following [link](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompt-design-strategies#experiment-with-different-parameter-values) for understanding different parameters"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "ZnyExfPTi68w"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"generation_config = GenerationConfig(temperature=0.1, max_output_tokens=256)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "fIPcn5dZ7O-b"
|
||
},
|
||
"source": [
|
||
"## Text Classification"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "l2eDjxvafo5W"
|
||
},
|
||
"source": [
|
||
"In the section below, you will explore zero-shot prompting, few-shot prompting, and some common types of text classification tasks."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "bC3qkPZ8jFkY"
|
||
},
|
||
"source": [
|
||
"### Zero-shot prompting"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "W8RFu2ZX_o_y"
|
||
},
|
||
"source": [
|
||
"Zero-shot prompting is where you do not provide examples with labels, and rely on the LLM to make the classification on its own."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "_uNNGhC_e1nZ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Classify the following:\\n\n",
|
||
"text: \"I saw a furry animal in the park today with a long tail and big eyes.\"\n",
|
||
"label: dogs, cats\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "tjl-tckTjK2B"
|
||
},
|
||
"source": [
|
||
"### Few-shot prompting"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "5UC0w7n5_o_z"
|
||
},
|
||
"source": [
|
||
"With few-shot prompting, you provide examples to the Gemini model and expect it to predict classes based on the provided examples."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "b8jYL-hBjMtW"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"What is the topic for a given news headline? \\n\n",
|
||
"- business \\n\n",
|
||
"- entertainment \\n\n",
|
||
"- health \\n\n",
|
||
"- sports \\n\n",
|
||
"- technology \\n\\n\n",
|
||
"\n",
|
||
"Text: Pixel 7 Pro Expert Hands On Review. \\n\n",
|
||
"The answer is: technology \\n\n",
|
||
"\n",
|
||
"Text: Quit smoking? \\n\n",
|
||
"The answer is: health \\n\n",
|
||
"\n",
|
||
"Text: Birdies or bogeys? Top 5 tips to hit under par \\n\n",
|
||
"The answer is: sports \\n\n",
|
||
"\n",
|
||
"Text: Relief from local minimum-wage hike looking more remote \\n\n",
|
||
"The answer is: business \\n\n",
|
||
"\n",
|
||
"Text: You won't guess who just arrived in Bari, Italy for the movie premiere. \\n\n",
|
||
"The answer is:\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "WaiMLs4SjNLi"
|
||
},
|
||
"source": [
|
||
"### Other classification examples"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "LhkcRWrh_o_0"
|
||
},
|
||
"source": [
|
||
"Explore some more common text classification prompts below, which are all based on zero-shot prompts. You can also turn some of these into few-shot prompts by providing your own custom examples of text and the associated output classes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "8tEjKEAtXjf8"
|
||
},
|
||
"source": [
|
||
"#### Topic classification"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "bCnuQRVSXmyr"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Classify a piece of text into one of several predefined topics, such as sports, politics, or entertainment. \\n\n",
|
||
"text: President Biden will be visiting India in the month of March to discuss a few opportunities. \\n\n",
|
||
"class:\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "rB6rZD-6YAkC"
|
||
},
|
||
"source": [
|
||
"#### Spam detection"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "OfyHvhBfX_P_"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Given an email, classify it as spam or not spam. \\n\n",
|
||
"email: hi user, \\n\n",
|
||
" you have been selected as a winner of the lottery and can win upto 1 million dollar. \\n\n",
|
||
" kindly share your bank details and we can proceed from there. \\n\\n\n",
|
||
"\n",
|
||
" from, \\n\n",
|
||
" US Official Lottry Depatmint\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "cHKcxx0TYrGv"
|
||
},
|
||
"source": [
|
||
"#### Intent recognition"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "DsseGvWNYvK3"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Given a user's input, classify their intent, such as \"finding information\", \"making a reservation\", or \"placing an order\". \\n\n",
|
||
"user input: Hi, can you please book a table for two at Juan for May 1?\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "stsfav5aZtqV"
|
||
},
|
||
"source": [
|
||
"#### Language identification"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "88TqQSXIZxts"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Given a piece of text, classify the language it is written in. \\n\n",
|
||
"text: Selam nasıl gidiyor?\n",
|
||
"language:\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "3Z_jmrhOZ15J"
|
||
},
|
||
"source": [
|
||
"#### Toxicity detection"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "Umloy-o1Z5us"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Given a piece of text, classify it as toxic or non-toxic. \\n\n",
|
||
"text: i love sunny days\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "rTH5MeiVZ6Hr"
|
||
},
|
||
"source": [
|
||
"#### Emotion detection"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "u5ETwBSrZ-Xn"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"prompt = \"\"\"\n",
|
||
"Given a piece of text, classify the emotion it conveys, such as happiness, or anger. \\n\n",
|
||
"text: I'm still so delighted from yesterday's news\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
").text\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "7ddaadac64c7"
|
||
},
|
||
"source": [
|
||
"### Evaluation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "d5e2266cb257"
|
||
},
|
||
"source": [
|
||
"You can evaluate the outputs of the text classification task if the ground truth classes are available. To showcase how this works, start by creating a simple dataframe with product reviews and the ground truth sentiment."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "b0e04a03f24f"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"review_data_df = pd.DataFrame(\n",
|
||
" {\n",
|
||
" \"review\": [\n",
|
||
" \"i love this product. it does have everything i am looking for!\",\n",
|
||
" \"all i can say is that you will be happy after buying this product\",\n",
|
||
" \"its way too expensive and not worth the price\",\n",
|
||
" \"i am feeling okay. its neither good nor too bad.\",\n",
|
||
" ],\n",
|
||
" \"sentiment_groundtruth\": [\"positive\", \"positive\", \"negative\", \"neutral\"],\n",
|
||
" }\n",
|
||
")\n",
|
||
"\n",
|
||
"review_data_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "088327f41a26"
|
||
},
|
||
"source": [
|
||
"Now that you have the data with reviews and sentiments as ground truth labels, you can call the text generation model to each review row using the `apply` function. Each row will use the prompt in the `review` column to predict the sentiment using the Gemini API, and store the results in `sentiment_prediction` column. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "0fb691b6c721"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_sentiment(row):\n",
|
||
" prompt = f\"\"\"Classify the sentiment of the following review as \"positive\", \"neutral\" and \"negative\". \\n\\n\n",
|
||
" review: {row} \\n\n",
|
||
" sentiment:\n",
|
||
" \"\"\"\n",
|
||
" response = generation_model.generate_content(\n",
|
||
" contents=prompt, generation_config=generation_config\n",
|
||
" ).text\n",
|
||
" return response\n",
|
||
"\n",
|
||
"\n",
|
||
"review_data_df[\"sentiment_prediction\"] = review_data_df[\"review\"].apply(get_sentiment)\n",
|
||
"review_data_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "908c72bdf4c7"
|
||
},
|
||
"source": [
|
||
"In the end, you can call the `classification_report` function from sklearn to measure the accuracy and other classification metrics by passing ground truth sentiments `sentiment_groundtruth` and predicted sentiment `sentiment_prediction`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "7f22690ae395"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"report = classification_report(\n",
|
||
" review_data_df[\"sentiment_groundtruth\"], review_data_df[\"sentiment_prediction\"]\n",
|
||
")\n",
|
||
"print(report)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"name": "text_classification.ipynb",
|
||
"toc_visible": true
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"name": "python3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|