Introduction to Scikit-LLM: Merging Scikit-Learn with Large Language Models
Scikit-LLM is a groundbreaking project that effortlessly combines the renowned machine learning library Scikit-learn with the capabilities of large language models such as ChatGPT. This integration enriches the potential of text analysis tasks, offering enhanced performance and versatility.
Seamless Installation
To set up Scikit-LLM, users can simply run the following command in their terminal:
pip install scikit-llm
This quick installation ensures that users can swiftly begin leveraging the power of large language models in their text analytics.
How to Support Scikit-LLM
The community can support the growth and development of Scikit-LLM in various ways. Enthusiasts can star the project on GitHub, which helps increase its visibility. Developers and users can also provide feedback or suggest improvements in the issues section on GitHub or join discussions on Discord. Additionally, spreading the word through posts on LinkedIn and similar platforms can significantly boost the project's recognition. For those interested in exploring related projects, Scikit-LLM's creators also recommend checking out Dingo and Falcon.
Quick Start and Documentation
Getting started with Scikit-LLM is straightforward. Here's a brief example of performing zero-shot text classification using GPT:
# Import the necessary modules
from skllm.datasets import get_classification_dataset
from skllm.config import SKLLMConfig
from skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier
# Configure the credentials
SKLLMConfig.set_openai_key("<YOUR_KEY>")
SKLLMConfig.set_openai_org("<YOUR_ORGANIZATION_ID>")
# Load a demo dataset
X, y = get_classification_dataset() # labels: positive, negative, neutral
# Initialize the model and make the predictions
clf = ZeroShotGPTClassifier(model="gpt-4")
clf.fit(X,y)
clf.predict(X)
This example illustrates how to configure the necessary credentials, load a dataset, and utilize the zero-shot classification model from Scikit-LLM to make predictions. For more in-depth information, users are directed to consult the comprehensive documentation.
Citing Scikit-LLM
For academic and professional references, Scikit-LLM can be cited using its dedicated BibTeX entry. This helps acknowledge the work of its creators, Iryna Kondrashchenko and Oleh Kostromin, and the organization beastbyte.ai, based in Linz, Austria.
In summary, Scikit-LLM not only bridges the gap between traditional machine learning and modern language models but also offers a user-friendly experience, encouraging widespread adoption and support from the community.