page.md

title	Few-shot text classification
nextjs	metadata title description Few-shot text classification Learn about few-shot text classification.

Overview

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 class positive, negative, and neutral, the model should be able to classify a new text into one of these classes.

The estimators provided by Scikit-LLM do not automatically select the subset of the training data, and instead use the entire training set to construct the examples. Therefore, if your training set is large, you might want to consider splitting it into training and validation sets, while keeping the training set small (we recommend not to exceed 10 examples per class). Additionally, it is advisable to permute the order of the samples in order to avoid the recency bias.

Example using GPT-4:

from skllm.models.gpt.classification.few_shot import (
FewShotGPTClassifier,
MultiLabelFewShotGPTClassifier,
)
from skllm.datasets import (
    get_classification_dataset,
    get_multilabel_classification_dataset,
)

# single label
X, y = get_classification_dataset()
clf = FewShotGPTClassifier(model="gpt-4o")
clf.fit(X,y)
labels = clf.predict(X)

# multi-label
X, y = get_multilabel_classification_dataset()
clf = MultiLabelFewShotGPTClassifier(max_labels=2, model="gpt-4o")
clf.fit(X,y)
labels = clf.predict(X)

---

API Reference

The following API reference only lists the parameters needed for the initialization of the estimator. The remaining methods follow the syntax of a scikit-learn classifier.

FewShotGPTClassifier

from skllm.models.gpt.classification.few_shot import FewShotGPTClassifier

Parameter	Type	Description
model	str	Model to use, by default "gpt-3.5-turbo".
default_label	str	Default label for failed prediction; if "Random" -> selects randomly based on class frequencies, by default "Random".
prompt_template	Optional[str]	Custom prompt template to use, by default None.
key	Optional[str]	Estimator-specific API key; if None, retrieved from the global config, by default None.
org	Optional[str]	Estimator-specific ORG key; if None, retrieved from the global config, by default None.

MultiLabelFewShotGPTClassifier

from skllm.models.gpt.classification.few_shot import MultiLabelFewShotGPTClassifier

Parameter	Type	Description
model	str	Model to use, by default "gpt-3.5-turbo".
default_label	str	Default label for failed prediction; if "Random" -> selects randomly based on class frequencies, by default "Random".
max_labels	Optional[int]	Maximum labels per sample, by default 5.
prompt_template	Optional[str]	Custom prompt template to use, by default None.
key	Optional[str]	Estimator-specific API key; if None, retrieved from the global config, by default None.
org	Optional[str]	Estimator-specific ORG key; if None, retrieved from the global config, by default None.

FewShotClaudeClassifier

from skllm.models.claude.classification.few_shot import FewShotClaudeClassifier

Parameter	Type	Description
model	str	Model to use, by default "claude-3-haiku-20240307"
default_label	str	Default label for failed prediction; if "Random" -> selects randomly based on class frequencies
prompt_template	Optional[str]	Custom prompt template to use, by default None
key	Optional[str]	Estimator-specific API key; if None, retrieved from the global config

MultiLabelFewShotClaudeClassifier

from skllm.models.claude.classification.few_shot import MultiLabelFewShotClaudeClassifier

Parameter	Type	Description
model	str	Model to use, by default "claude-3-haiku-20240307"
default_label	str	Default label for failed prediction; if "Random" -> selects randomly based on class frequencies
max_labels	Optional[int]	Maximum labels per sample, by default 5
prompt_template	Optional[str]	Custom prompt template to use, by default None
key	Optional[str]	Estimator-specific API key; if None, retrieved from the global config