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Quick Start

Scikit-LLM allows you to seamlessly integrate powerful language models into scikit-learn for enhanced text analysis tasks. Let's see how it is possible to use Scikit-LLM to perform zero-shot text classification with GPT-4.

Installation​

First of all, it is necessary to install Scikit-LLM. You can do it by running the following command:

pip install scikit-llm

API Key Configuration​

For this example, we will use GPT-4, which requires an OpenAI API key. You can get one here.

Once you have your API key, you can set it as follows:

from skllm.config import SKLLMConfig

SKLLMConfig.set_openai_key("<YOUR_KEY>")
SKLLMConfig.set_openai_org("<YOUR_ORGANIZATION_ID>")

Note: Scikit-LLM supports other language models, including the locally hosted ones. For more information, please refer to the Backend Families section.

Zero-Shot Text Classification​

Now, we are ready to perform zero-shot text classification with GPT-4. Let's start by loading a sample dataset:

from skllm.datasets import get_classification_dataset

# demo sentiment analysis dataset
# labels: positive, negative, neutral
X, y = get_classification_dataset()

Then, we can create a classifier instance and fit it using conventional scikit-learn syntax:

from skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier

clf = ZeroShotGPTClassifier(model="gpt-4")
clf.fit(X,y)
clf.predict(X)

Scikit-LLM will automatically query the OpenAI API and transform the response into a regular list of labels.

Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the "training" set).

Furthermore, since the "training" data is not strictly required, it can be fully omitted. The only thing that has to be provided is the list of candidate labels.

from skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier

clf = ZeroShotGPTClassifier(model="gpt-4")
clf.fit(None, ["positive", "negative", "neutral"])
clf.predict(X)