Scikit-LLM performs few-shot text classification by placing a small set of labeled examples in a prompt sent to a GPT model. It does not retrain model weights: fit() stores the demonstrations and predict() asks the selected model to assign labels. The standard workflow is a scikit-learn-style FewShotGPTClassifier; multi-label data uses MultiLabelFewShotGPTClassifier, while DynamicFewShotGPTClassifier retrieves only similar demonstrations when the complete training set is too large for one prompt.
What few-shot classification means in Scikit-LLM
Few-shot text classification assigns new text to predefined classes using a few labeled examples for each class. For instance, examples labeled billing, technical_support and account teach the model the distinctions before it classifies an unseen message.
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Scikit-LLM presents this process through estimators that follow familiar scikit-learn methods. Calling fit(X, y) records the examples; calling predict(X) constructs prompts containing those examples and queries the configured GPT model. No local gradient training or weight update occurs.
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Basic single-label implementation
The current documentation example uses FewShotGPTClassifier with the gpt-4o model and a built-in dataset:
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from skllm.models.gpt.classification.few_shot import FewShotGPTClassifier
from skllm.datasets import get_classification_dataset
X, y = get_classification_dataset()
clf = FewShotGPTClassifier(model="gpt-4o")
clf.fit(X, y)
labels = clf.predict(X)Replace the dataset with your own text column and label column when integrating the estimator into a pipeline. The GPT classifiers query the OpenAI API, so the runtime also needs valid API credentials and network access. Model names and constructor arguments are release-sensitive. Older package examples use openai_model="gpt-3.5-turbo"; check the API reference for the version installed in your environment rather than copying that parameter into a newer release unchanged.
How demonstrations affect the prompt
During fitting, Scikit-LLM keeps the labeled training samples available for prompt construction. At inference, those samples are sent along with the new text and the permitted class labels. The model infers the label from the examples; it is not learning a persistent classifier between calls.
The project documentation recommends no more than 10 samples per label. That is a practical prompt-size guideline, not an accuracy benchmark. Every additional example increases input-token consumption and generally makes inference slower. Keep demonstrations representative, consistently formatted and free of contradictory labels. The documentation also advises permuting example order to reduce recency bias. Permutation can make prompt behavior less dependent on which example appears last, but it is not a measured guarantee of higher accuracy.
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- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
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- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Choose the estimator that matches the output
| Estimator | Output and examples | Prompt strategy | Scaling trade-off |
|---|---|---|---|
FewShotGPTClassifier |
Exactly one class per text | Includes all supplied demonstrations | Simple to configure, but prompt size and token use grow with the training set |
MultiLabelFewShotGPTClassifier |
Several classes can be assigned to one text | Uses few-shot demonstrations and limits output with max_labels |
Useful for overlapping topics; each prediction still carries prompt and API costs |
DynamicFewShotGPTClassifier |
One class per text, with retrieved examples | Partitions data by class and retrieves similar neighbors at inference | Keeps prompts smaller as data grows, but adds indexing and retrieval work |
Multi-label example
from skllm.models.gpt.classification.few_shot import MultiLabelFewShotGPTClassifier
clf = MultiLabelFewShotGPTClassifier(max_labels=2, model="gpt-4o")
clf.fit(X, y)
labels = clf.predict(X)Set max_labels to the maximum number of labels a sample may receive. Your training labels must represent the multi-label format expected by the installed Scikit-LLM release.
When dynamic few-shot classification is preferable
The regular classifier places the complete demonstration set into every prompt, so it is best when each class has a small number of carefully selected examples. Dynamic few-shot classification instead vectorizes and stores the training data, partitions it by class, and performs neighbor lookup for each incoming text. Only a limited number of similar examples are then inserted into the prompt.
This design reduces prompt growth when the labeled corpus expands, but retrieval becomes part of the system’s latency and quality. The default scikit-learn kneighbors path may be slow on large datasets. Installing the optional Annoy extra enables an Annoy-based index:
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pip install "scikit-llm[annoy]"Use dynamic retrieval when a full prompt is impractical and semantic similarity can identify useful demonstrations. For a small, stable dataset, the regular estimator is simpler and easier to audit.
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- Start with a balanced set of up to 10 examples per label, following the project’s recommendation.
- Remove near-duplicates and examples whose wording does not clearly express the class.
- Measure token usage and response time in your own workload; the documentation publishes no common cost, latency or accuracy benchmark.
- Use dynamic retrieval rather than continually appending examples to a full-prompt classifier.
- Keep the model identifier explicit and pin the package version in deployment so a model or constructor change does not silently alter behavior.
There is no evidence in the documented sources that Scikit-LLM few-shot classifiers universally outperform conventional supervised algorithms. Compare them with a reproducible holdout evaluation using your labels, class balance, model version and prompt configuration.
Version and dependency checks
PyPI metadata for Scikit-LLM 0.4.2 lists an MIT license and Python 3.8 or newer. The release history records Scikit-LLM 1.4.3 on January 21, 2026, so 0.4.2 documentation may not match the newest package. Before deployment, inspect the installed release’s constructor signatures, supported model names and Python requirement, then run a small prediction smoke test.
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- Install the exact package version selected for your project.
- Confirm the Python interpreter satisfies that release’s requirement.
- Configure API credentials and verify a minimal GPT request.
- Check that imports and constructor parameters match the installed documentation.
- Run predictions on labeled validation data and log labels, token use and failures.
Common failure modes
Constructor or model errors
An example copied from an older release may use openai_model while the current class expects model, or may reference a model identifier no longer available to your account. Read the installed package documentation and update the argument rather than suppressing the error.
Slow or expensive predictions
Too many demonstrations create large prompts. Reduce examples, shorten text, or switch to DynamicFewShotGPTClassifier. For large dynamic datasets, use the Annoy extra and test retrieval latency separately from model latency.
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Check for inconsistent class names, ambiguous demonstrations and order effects. Normalize labels, improve example coverage and evaluate several demonstration permutations. Treat any improvement as workload-specific until measured on a fixed validation set.
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Related Scikit-LLM capabilities
The package documentation also describes zero-shot and multi-label zero-shot classification, GPT vectorization for downstream classifiers, GPT and PaLM fine-tuning, summarization and translation. Those features are separate workflows: zero-shot classification supplies no labeled demonstrations, vectorization creates features for another estimator, and fine-tuning changes model behavior through a training process. They should not be presented as interchangeable with few-shot prompting.
The Bottom Line
Use FewShotGPTClassifier for small single-label datasets, MultiLabelFewShotGPTClassifier when texts can receive several labels, and DynamicFewShotGPTClassifier when retrieving a few similar examples is more practical than sending the entire training set. Keep demonstrations compact, verify release-specific APIs, and benchmark your own workload before choosing a production design.
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