{"id":24958,"date":"2025-01-12T22:42:52","date_gmt":"2025-01-12T22:42:52","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/zero-shot-and-few-shot-text-classification-with-scikit-llm\/"},"modified":"2025-01-12T22:42:52","modified_gmt":"2025-01-12T22:42:52","slug":"zero-shot-and-few-shot-text-classification-with-scikit-llm","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=24958","title":{"rendered":"Zero-Shot and Few-Shot Text Classification with SCIKIT-LLM"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>Analyzing customer sentiment and key themes from textual data has always been a time-intensive task, requiring data collection, manual labeling, and fine-tuning specialized models. But what if you could skip the hassle of training a model and still achieve accurate results? Enter zero-shot text classification, a groundbreaking approach powered by <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/03\/an-introduction-to-large-language-models-llms\/\" target=\"_blank\" rel=\"noreferrer noopener\">Large Language Models<\/a> (LLMs). In this article, we\u2019ll explore how zero-shot classification simplifies sentiment analysis using the SKLLM library (a blend of scikit-learn and LLMs). In this tutorial, you\u2019ll see how to use the <a href=\"https:\/\/skllm.beastbyte.ai\/docs\/zero-shot-text-classification\" target=\"_blank\" rel=\"nofollow noopener\">SKLLM library<\/a><b> <\/b>(scikit-learn + LLM) to classify the Women\u2019s E-Commerce Clothing Reviews dataset from <a href=\"https:\/\/www.kaggle.com\/datasets\/nicapotato\/womens-ecommerce-clothing-reviews\" target=\"_blank\" rel=\"nofollow noopener\">Kaggle<\/a>.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-learning-objectives\">Learning Objectives<\/h3>\n<ul class=\"wp-block-list\">\n<li>Understand the traditional process of sentiment analysis and its challenges.<\/li>\n<li>Learn the concept and advantages of zero-shot text classification with LLMs.<\/li>\n<li>Explore the SKLLM library and its integration with <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2015\/01\/scikit-learn-python-machine-learning-tool\/\" target=\"_blank\" rel=\"noreferrer noopener\">scikit-learn<\/a>.<\/li>\n<li>Classify sentiments in the Women\u2019s E-Commerce Clothing Reviews dataset without custom training.<\/li>\n<li>Gain hands-on experience with zero-shot classification for practical use cases.<\/li>\n<\/ul>\n<p><em><strong>This article was published as a part of the\u00a0<\/strong><\/em><a href=\"https:\/\/www.analyticsvidhya.com\/datahack\/blogathon\" target=\"_blank\" rel=\"noreferrer noopener\"><em><strong>Data Science Blogathon.<\/strong><\/em><\/a><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-zero-shot-text-classification\">What is Zero-Shot Text Classification?<\/h2>\n<p>Online retailers often receive large volumes of text reviews from customers, making it challenging to quickly analyze the sentiments or key themes. Traditionally, companies would:<\/p>\n<ul class=\"wp-block-list\">\n<li>Collect and clean review data.<\/li>\n<li>Manually label thousands of samples (e.g., \u201cpositive,\u201d \u201cnegative,\u201d \u201cneutral\u201d).<\/li>\n<li>Fine-tune a dedicated classification model on this labeled data.<\/li>\n<\/ul>\n<p>While effective, fine-tuning requires considerable time, expertise, and computational resources. Enter zero-shot text classification: using Large Language Models (LLMs) directly to classify text with minimal effort. You can simply provide a set of descriptive labels (e.g., \u201cpositive,\u201d \u201cnegative,\u201d \u201cneutral\u201d) and let the model infer the correct class\u2014no custom training required!<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-why-zero-shot-is-so-efficient\">Why Zero-Shot is So Efficient?<\/h2>\n<p>Below we will discuss the points to understand that why zero-shot is so efficient:<\/p>\n<ul class=\"wp-block-list\">\n<li><b>No Fine-Tuning Required:<\/b> <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/08\/fine-tuning-large-language-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">Finetuning LLMs<\/a> like GPT-4o can be a costly affair. You don\u2019t need to spend hours (or even days) training a sentiment classifier on your dataset. Instead, you leverage pre-trained LLMs like GPT-4o, giving you a high-quality classifier immediately.<\/li>\n<li><b>Easy Adaptation to New Labels:<\/b> If your label set changes (e.g., from \u201cpositive, negative, neutral\u201d to more specific sentiments like \u201chappy, frustrated, curious, annoyed\u201d), you simply update your label list. There is no need to retrain a model.<\/li>\n<li><b>Fewer Data Requirements: <\/b>In typical supervised learning, you need labeled data for each class. Zero-shot classification only requires you to describe your classes (labels) in <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/01\/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python\/\" target=\"_blank\" rel=\"noreferrer noopener\">natural language<\/a>. This is particularly helpful if you have limited or unlabeled data.<\/li>\n<li><b>Speed to Deployment:<\/b> By skipping data annotation and model training steps, you can deploy your classification solution much faster.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-dataset-overview\">Dataset Overview<\/h2>\n<p>We\u2019ll use the Women\u2019s E-Commerce Clothing Reviews dataset from Kaggle.<\/p>\n<p><a href=\"https:\/\/www.kaggle.com\/datasets\/nicapotato\/womens-ecommerce-clothing-reviews\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Click here to access the dataset.<\/a><\/p>\n<p>Key points about the dataset:<\/p>\n<ul class=\"wp-block-list\">\n<li>It contains thousands of customer reviews of women\u2019s clothing items.<\/li>\n<li>The main text is in the \u201cReview Text\u201d column.<\/li>\n<li>Other metadata like \u201cTitle,\u201d \u201cRating,\u201d \u201cRecommended IND,\u201d and more are available but not always necessary for zero-shot classification.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-step-by-step-guide\">Step-by-Step Guide<\/h2>\n<p>Below we will learn how to streamline sentiment analysis and theme detection with zero-shot text classification using <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/03\/an-introduction-to-large-language-models-llms\/\" target=\"_blank\" rel=\"noreferrer noopener\">Large Language Models<\/a> (LLMs). In this tutorial, we\u2019ll walk you through leveraging the SKLLM library to classify real-world data effortlessly\u2014no custom training required!<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-step1-installation-and-setup\">Step1: Installation and Setup<\/h3>\n<p>Make sure you have Python 3.7+ and install SKLLM:<\/p>\n<pre class=\"wp-block-code\"><code>pip install scikit-llm<\/code><\/pre>\n<p>\u00a0Additionally, ensure you have a valid API key for an LLM provider (e.g., <a href=\"https:\/\/platform.openai.com\/\" target=\"_blank\" rel=\"nofollow noopener\">OpenAI\u2019s API<\/a>). Set it in your environment:\u00a0\u00a0<\/p>\n<pre class=\"wp-block-code\"><code>from skllm.config import SKLLMConfig\n\n# Replace with your actual OpenAI API key\nSKLLMConfig.set_openai_key(\"your_openai_api_key\")<\/code><\/pre>\n<p>(You can also store it in a .env file or handle it within your code, but environment variables are often cleaner.)<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-step2-import-libraries-and-load-the-dataset\">Step2: Import Libraries and Load the Dataset<\/h3>\n<pre class=\"wp-block-code\"><code>import pandas as pd\nfrom skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier\n\n# Load dataset\ndf = pd.read_csv(\"Womens Clothing E-Commerce Reviews.csv\")\n\n# Inspect the first few rows\nprint(df.head())<\/code><\/pre>\n<p>We\u2019ll focus on the \u201c<b>Review Text<\/b>\u201d column. Some rows may have missing values for reviews, so let\u2019s drop any NaNs:<\/p>\n<pre class=\"wp-block-code\"><code># Filter out rows without review text\ndf = df.dropna(subset=[\"Review Text\"]).reset_index(drop=True)\n\n# Extract the review texts into X\nX = df[\"Review Text\"].tolist()<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step3-define-your-labels\">Step3: Define Your Labels<\/h3>\n<p>We\u2019ll do a sentiment classification: <b><i>[\u201cpositive\u201d, \u201cnegative\u201d, \u201cneutral\u201d].<\/i><\/b><\/p>\n<p>Why these three? They\u2019re common sentiment tags. However, you\u2019re free to change or expand them: for example, <b><i>[\u201cpositive\u201d, \u201cnegative\u201d, \u201cneutral\u201d, \u201cmixed\u201d]<\/i><\/b>.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-step4-zero-shot-classification\">Step4: Zero-Shot Classification<\/h3>\n<p>Instantiate the <b>ZeroShotGPTClassifier<\/b>. We\u2019ll choose gpt-4o as the model, but you can select a different model if you want.<\/p>\n<pre class=\"wp-block-code\"><code># Create a zero-shot classifier\nclf = ZeroShotGPTClassifier(model=\"gpt-4o\")\n\n# Fit the classifier - here we pass `None` for X because we don't need training data\nclf.fit(None, [\"positive\", \"negative\", \"neutral\"])<\/code><\/pre>\n<p>Why <b><i>fit(None, labels)?<\/i><\/b> In a pure zero-shot scenario, no actual training occurs. The call to fit() is effectively telling the classifier which labels are possible. The model can then choose among them for each review.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-step5-classify-the-reviews\">Step5: Classify the Reviews<\/h3>\n<pre class=\"wp-block-code\"><code># Predict labels for the entire dataset\npredictions = clf.predict(X)\n\n# Let\u2019s see the first few results\nfor review_text, sentiment in zip(X[:5], predictions[:5]):\n    print(f\"Review: {review_text}\")\n    print(f\"Predicted Sentiment: {sentiment}\")\n    print(\"-\" * 50)<\/code><\/pre>\n<p>This loop will print out each review along with the zero-shot classifier\u2019s predicted sentiment.<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-results-discussion\">Results Discussion<\/h4>\n<p>With a traditional ML approach, you\u2019d need:<\/p>\n<ul class=\"wp-block-list\">\n<li><b>Labeling<\/b>: A large subset of these reviews labeled as positive, negative, neutral.<\/li>\n<li><b>Model Training<\/b>: Fine-tuning or training a classifier from scratch (e.g., an SVM, a BERT-based model).<\/li>\n<li><b>Model Validation<\/b>: Manually verifying performance on a validation set.<\/li>\n<li><b>Continuous Updates<\/b>: If new sentiments or categories emerge, you\u2019d need more labeled data and additional training.<\/li>\n<\/ul>\n<p>Zero-shot eliminates most of that overhead:<\/p>\n<ul class=\"wp-block-list\">\n<li><b>Immediate Start<\/b>: You only provide a label list and a well-crafted prompt behind the scenes.<\/li>\n<li><b>No Labeled Data Required<\/b>: The LLM has learned enough semantics about language to infer meaning from descriptive labels.<\/li>\n<li><b>Easy to Refine<\/b>: Need new categories like \u201cslightly positive\u201d or \u201cambivalent\u201d? Just add them to the list of candidate labels.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-potential-limitations-to-note\">Potential Limitations to Note<\/h2>\n<ul class=\"wp-block-list\">\n<li><b>Accuracy Variation<\/b>: The quality of zero-shot classification can vary. For simple sentiment analysis, it often works surprisingly well. For highly specialized or technical domains, the model might misinterpret certain text or domain jargon.<\/li>\n<li><b>Cost<\/b>: Using a large model like GPT-4o involves API costs if you are calling an external service.<\/li>\n<li><b>Data Privacy<\/b>: You must ensure sending data to an API is allowed (especially if the text is sensitive).<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-few-shot-text-classification\">Few-Shot Text Classification<\/h2>\n<p>Few-shot text classification is a task of classifying a text into one of the pre-defined classes based on a few examples of each class. For example, given a few examples of the classes positive, negative, and neutral, the model should be able to classify new text into one of these categories.<\/p>\n<p><strong>Note: <\/strong>The estimators provided by Scikit-LLM do not automatically select a subset of the training data; they use the entire training set to build the few-shot examples. If your training set is large, consider splitting it into training and validation sets while keeping the training set small (ideally no more than 10 examples per class). Also, be sure to permute the order of these samples to avoid any recency bias in the LLM\u2019s attention.<\/p>\n<pre class=\"wp-block-code\"><code>from skllm.models.gpt.classification.few_shot import (\n    FewShotGPTClassifier,\n    MultiLabelFewShotGPTClassifier,\n)\nfrom skllm.datasets import (\n    get_classification_dataset,\n    get_multilabel_classification_dataset,\n)\n\n# Single-label classification\nX, y = get_classification_dataset()\nclf = FewShotGPTClassifier(model=\"gpt-4o\")\nclf.fit(X, y)\nlabels = clf.predict(X)\n\n# Multi-label classification\nX, y = get_multilabel_classification_dataset()\nclf = MultiLabelFewShotGPTClassifier(max_labels=2, model=\"gpt-4o\")\nclf.fit(X, y)\nlabels = clf.predict(X)<\/code><\/pre>\n<h2 class=\"wp-block-heading\" id=\"h-chain-of-thought-text-classification\">Chain-of-Thought Text Classification<\/h2>\n<p>Chain-of-thought text classification is similar to zero-shot classification in that it does not require labeled data beforehand. The main difference is that the model generates intermediate reasoning steps along with the label. This added \u201cchain of thought\u201d can improve performance but increases token usage (and thus potential cost).<\/p>\n<pre class=\"wp-block-code\"><code>from skllm.models.gpt.classification.zero_shot import CoTGPTClassifier\nfrom skllm.datasets import get_classification_dataset\n\n# Demo sentiment analysis dataset\n# Labels: positive, negative, neutral\nX, y = get_classification_dataset()\n\nclf = CoTGPTClassifier(model=\"gpt-4o\")\nclf.fit(X, y)\npredictions = clf.predict(X)\n\n# Each prediction has [label, reasoning]\nlabels, reasoning = predictions[:, 0], predictions[:, 1]<\/code><\/pre>\n<p>By testing a few-shot approach or a chain-of-thought approach, you may see an improvement over the baseline zero-shot classification results<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>Scikit-LLM\u2019s library is a fast, flexible, and easy alternative to building a custom sentiment analysis pipeline. Without the need to label data or fine-tune a model, you can immediately classify customer feedback into descriptive categories.<\/p>\n<p>In the case of the Women\u2019s E-Commerce Clothing Reviews dataset, you can quickly unlock insights\u2014such as customer sentiment\u2014without the usual overhead of dataset preparation, labeling, and model retraining. This advantage is especially powerful if you need to iterate on or expand your classification labels over time.<\/p>\n<p>As the AI ecosystem evolves, zero-shot and few-shot techniques will continue to grow in importance. They enable rapid prototyping and accelerate business workflows by leveraging the massive knowledge already embedded in large language models.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-key-takeaways\">Key Takeaways<\/h3>\n<ul class=\"wp-block-list\">\n<li>Zero-shot classification simplifies sentiment analysis without the need for manual labeling or model training.<\/li>\n<li>The SKLLM library integrates scikit-learn with LLMs for efficient text classification.<\/li>\n<li>SCIKIT-LLM enables efficient Zero-Shot and Few-Shot text classification, eliminating the need for manual labeling and model training.<\/li>\n<li>Large Language Models (LLMs) like GPT-4 enable immediate, high-quality classification results.<\/li>\n<li>With SCIKIT-LLM, you can quickly deploy text classification solutions using pre-trained Large Language Models, saving time and resources.<\/li>\n<li>The Women\u2019s E-Commerce Clothing Reviews dataset provides a practical example of zero-shot classification in action.<\/li>\n<li>Zero-shot text classification is fast, adaptable, and requires minimal data, making it ideal for quick deployment.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1736450350477\"><strong class=\"schema-faq-question\">Q1. How do I decide between zero-shot, few-shot, and chain-of-thought classification methods?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Zero-shot is great for quick proofs-of-concept or when labeled data is scarce. Few-shot improves accuracy by using a small set of examples per class, requiring a minimal labeled dataset. Chain-of-thought enhances performance further by leveraging intermediate reasoning but increases token usage and costs.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1736493182750\"><strong class=\"schema-faq-question\">Q2. How many examples should I include for a few-shot setup?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. It\u2019s generally recommended to include up to 10 examples per class. Beyond that, the prompt may become too long or expensive to process, and performance gains may plateau. Also, remember to shuffle (permute) the examples to avoid recency bias from the model.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1736493216985\"><strong class=\"schema-faq-question\">Q3. Will adding chain-of-thought reasoning automatically improve accuracy?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Not always. While chain-of-thought can provide the model with a structured reasoning path, its effectiveness depends on the complexity of the task and the clarity of your prompts. It can lead to better explanations and decisions in many cases, but it also consumes more tokens and increases your API cost.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1736493239290\"><strong class=\"schema-faq-question\">Q4. Is it expensive to run these classifications at scale?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Cost depends on your token usage, which varies with model choice, prompt length, and dataset size. Zero-shot and few-shot prompts can be relatively short, especially if you keep examples per class to a minimum. Chain-of-thought methods add to prompt length because the model needs to generate explanations in addition to labels.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><strong>The media shown in this article is not owned by Analytics Vidhya and is used at the Author\u2019s discretion.<\/strong><\/p>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/adarsh2039075\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_tHXFGNS.webp\" width=\"48\" height=\"48\" alt=\"Adarsh Balan\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>    Hi! I&#8217;m Adarsh, a Business Analytics graduate from ISB, currently deep into research and exploring new frontiers. I&#8217;m super passionate about data science, AI, and all the innovative ways they can transform industries. Whether it&#8217;s building models, working on data pipelines, or diving into machine learning, I love experimenting with the latest tech. AI isn&#8217;t just my interest, it&#8217;s where I see the future heading, and I&#8217;m always excited to be a part of that journey!    <\/p>\n<\/p><\/div>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Analyzing customer sentiment and key themes from textual data has always been a time-intensive task, requiring data collection, manual labeling, and fine-tuning specialized models. But what if you could skip the hassle of training a model and still achieve accurate results? Enter zero-shot text classification, a groundbreaking approach powered by Large Language Models (LLMs). In [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":24959,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[5815,16969,16968,16970,836,16967],"dealstore":[],"offerexpiration":[],"class_list":["post-24958","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-blogathon","tag-classification","tag-fewshot","tag-scikitllm","tag-text","tag-zeroshot"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Zero-Shot and Few-Shot Text Classification with SCIKIT-LLM - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=24958\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Zero-Shot and Few-Shot Text Classification with SCIKIT-LLM - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Analyzing customer sentiment and key themes from textual data has always been a time-intensive task, requiring data collection, manual labeling, and fine-tuning specialized models. 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