Scikit-LLM: Sklearn Meets Large Language Models

Seamlessly integrate powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks.

Installation 💾

pip install scikit-llm

Support us 🤝

You can support the project in the following ways:

⭐ Star Scikit-LLM on GitHub (click the star button in the top right corner)
🐦 Check out our related project - Falcon AutoML
💡 Provide your feedback or propose ideas in the issues section
🔗 Post about Scikit-LLM on LinkedIn or other platforms

Documentation 📚

Configuring OpenAI API Key

At the moment Scikit-LLM is only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required.

from skllm.config import SKLLMConfig
SKLLMConfig.set_openai_key("<YOUR_KEY>")
SKLLMConfig.set_openai_org("<YOUR_ORGANISATION>")

Important notice:

If you have a free trial OpenAI account, the rate limits are not sufficient (specifically 3 requests per minute). Please switch to the "pay as you go" plan first.
When calling SKLLMConfig.set_openai_org, you have to provide your organization ID and NOT the name. You can find your ID here.

Zero-Shot Text Classification

One of the powerful ChatGPT features is the ability to perform text classification without being re-trained. For that, the only requirement is that the labels must be descriptive.

We provide a class ZeroShotGPTClassifier that allows to create such a model as a regular scikit-learn classifier.

Example 1: Training as a regular classifier

from skllm import ZeroShotGPTClassifier
from skllm.datasets import get_classification_dataset

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

clf = ZeroShotGPTClassifier(openai_model = "gpt-3.5-turbo")
clf.fit(X, y)
labels = 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).

Example 2: Training without labeled data

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

from skllm import ZeroShotGPTClassifier
from skllm.datasets import get_classification_dataset

X, _ = get_classification_dataset()

clf = ZeroShotGPTClassifier()
clf.fit(None, ['positive', 'negative', 'neutral'])
labels = clf.predict(X)

Note: unlike in a typical supervised setting, the performance of a zero-shot classifier greatly depends on how the label itself is structured. It has to be expressed in natural language, be descriptive and self-explanatory. For example, in the previous semantic classification task, it could be beneficial to transform a label from "<semantics>" to "the semantics of the provided text is <semantics>".

Multi-Label Zero-Shot Text Classification

With a class MultiLabelZeroShotGPTClassifier it is possible to perform the classification in multi-label setting, which means that each sample might be assigned to one or several distinct classes.

Example:

from skllm import MultiLabelZeroShotGPTClassifier
from skllm.datasets import get_multilabel_classification_dataset

X, y = get_multilabel_classification_dataset()

clf = MultiLabelZeroShotGPTClassifier(max_labels=3)
clf.fit(X, y)
labels = clf.predict(X)

Similarly to the ZeroShotGPTClassifier it is sufficient if only candidate labels are provided. However, this time the classifier expects y of a type List[List[str]].

from skllm import MultiLabelZeroShotGPTClassifier
from skllm.datasets import get_multilabel_classification_dataset

X, _ = get_multilabel_classification_dataset()
candidate_labels = [
    "Quality", 
    "Price", 
    "Delivery", 
    "Service", 
    "Product Variety", 
    "Customer Support", 
    "Packaging", 
    "User Experience", 
    "Return Policy", 
    "Product Information"
]
clf = MultiLabelZeroShotGPTClassifier(max_labels=3)
clf.fit(None, [candidate_labels])
labels = clf.predict(X)

Text Vectorization

As an alternative to using GPT as a classifier, it can be used solely for data preprocessing. GPTVectorizer allows to embed a chunk of text of arbitrary length to a fixed-dimensional vector, that can be used with virtually any classification or regression model.

Example 1: Embedding the text

from skllm.preprocessing import GPTVectorizer

model = GPTVectorizer()
vectors = model.fit_transform(X)

Example 2: Combining the Vectorizer with the XGBoost Classifier in a Sklearn Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier

le = LabelEncoder()
y_train_encoded = le.fit_transform(y_train)
y_test_encoded = le.transform(y_test)

steps = [('GPT', GPTVectorizer()), ('Clf', XGBClassifier())]
clf = Pipeline(steps)
clf.fit(X_train, y_train_encoded)
yh = clf.predict(X_test)

Text Summarization

GPT excels at performing summarization tasks. Therefore, we provide GPTSummarizer that can be used both as stand-alone estimator, or as a preprocessor (in this case we can make an analogy with a dimensionality reduction preprocessor).