Question 1

How do I use scikit-llm for zero-shot text classification?

Accepted Answer

Import ZeroShotGPTClassifier, set your OpenAI key via SKLLMConfig, load data (e.g., with get_classification_dataset), and use fit/predict as shown in the quick start. It mimics standard scikit-learn workflows for easy adoption.

Question 2

Is scikit-llm free to use?

Accepted Answer

No, since it integrates with OpenAI's API, usage incurs costs based on OpenAI's pricing for models like GPT-4. The library itself is open-source, but API calls are billed separately, so budget accordingly for production.

Question 3

Can I use scikit-llm with local LLMs like LLaMA?

Accepted Answer

Currently, scikit-llm primarily supports OpenAI models, so it doesn't natively work with local LLMs. You'd need custom modifications or wait for future updates to expand provider support.

Question 4

Scikit-llm or Hugging Face Transformers for scikit-learn integration?

Accepted Answer

Scikit-llm excels for zero-shot classification with OpenAI's API in a scikit-learn wrapper, while Hugging Face Transformers offers more flexibility with fine-tuning and local models but requires more setup and code changes.

Question 5

How to handle API rate limits and errors with scikit-llm?

Accepted Answer

You'll need to implement custom error handling around API calls, as scikit-llm doesn't explicitly manage retries or limits. Check the documentation for tips and monitor OpenAI's usage to avoid disruptions.

Question 6

What types of text analysis tasks can scikit-llm handle beyond classification?

Accepted Answer

Based on the README, it focuses on zero-shot classification, but the scikit-learn compatibility might allow extensions. For now, it's best suited for classification tasks, with potential for future feature additions.

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