需要自备 OpenAI API Key 和组织 ID 并先调用 SKLLMConfig 配置;示例默认使用 gpt-4 模型,需确认账号有对应模型权限;README 未说明离线或本地模型支持
scikit-llm
fnnx-aiMIT活跃维护把大语言模型包装成 scikit-learn 风格的估计器,让文本分类、标注等任务能直接套用 fit/predict 和 Pipeline,省去自己写调用逻辑。
Seamlessly integrate LLMs into scikit-learn.
v1.4.3发布于 2026-01-21 13:57 UTC3,540star287fork同步于 2026-11-10 17:42 UTC
pip install scikit-llm 硬件要求按 README 说明
README 未说明,仅通过 API 调用 GPT 模型,未提及本地显存或 GPU 要求
把 ChatGPT 等大模型封装成 scikit-learn 估计器,让文本分类等任务沿用 fit/predict 流程
把大语言模型包装成 scikit-learn 风格的估计器,让文本分类、标注等任务能直接套用 fit/predict 和 Pipeline,省去自己写调用逻辑。
核心能力
- 提供 ZeroShotGPTClassifier 等 sklearn 风格估计器
- 用 fit/predict 接口调用 GPT 模型做文本分类
- 通过 SKLLMConfig 统一配置 OpenAI 密钥与组织 ID
- 内置演示数据集 get_classification_dataset 便于快速上手
容易踩的坑
内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README
- v1.4.3最新正式版2026-01-21 · 2026-01-21 13:57 UTC
Changes
- Fix prompt typo templates.py (#125)
- Remove debug print statement and bump to v1.4.3 (#129)
🧹 Maintenance
- Bump peter-evans/create-pull-request from 6 to 7 (#116)
- v1.4.22025-09-20 · 2025-09-20 19:36 UTC
查看更新说明
Changes
🚀 Features
- Added model constants file and standardize model name usage (#124)
- Added Anthropic API support (#118)
- Added support for reasoning models/gpt-5
- v1.4.12024-11-09 · 2024-11-09 20:57 UTC
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Changes
- Fixed labels extraction for multi-label classifiers (#117)
- v1.4.02024-08-04 · 2024-08-04 20:11 UTC
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Changes
- Replaced gpt4all with llama-cpp-python (#110)
- v1.3.12024-07-24 · 2024-07-24 19:12 UTC
查看更新说明
Changes
- Added gpt-4o-mini to the list of tunable models (#108)
用户点评
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Scikit-LLM: Scikit-Learn 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)
- 💡 Provide your feedback or propose ideas in the issues section or Discord
- 📰 Post about Scikit-LLM on LinkedIn or other platforms
- 🔗 Check out our other projects: Dingo, Falcon
Quick Start & Documentation 📚
Quick start example of 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)
For more information please refer to the documentation.
Citation
You can cite Scikit-LLM using the following BibTeX:
@software{ScikitLLM,
author = {Iryna Kondrashchenko and Oleh Kostromin},
year = {2023},
publisher = {beastbyte.ai},
address = {Linz, Austria},
title = {Scikit-LLM: Scikit-Learn Meets Large Language Models},
url = {https://github.com/iryna-kondr/scikit-llm }
}