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Text Summarization with Scikit-LLM

This article explains how to use Scikit-LLM's text summarization feature to handle large volumes of text in machine learning pipelines. It covers building a custom Hugging Face summarizer transformer, integrating it into a scikit-learn pipeline with TF-IDF vectorization and a classifier, and demonstrates the process with code examples.

SourceMachine Learning MasteryAuthor: Iván Palomares Carrascosa
Text Summarization with Scikit-LLM
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Key points

  • Scikit-LLM bridges traditional ML and LLMs, offering zero-shot classification and text summarization.
  • A custom HuggingFaceSummarizer class inherits from BaseEstimator and TransformerMixin to load a pretrained model and produce summaries.
  • A pipeline chains summarization, TF-IDF vectorization, and logistic regression for end-to-end text classification.
  • The example uses a lightweight model (distilbart-cnn-12-6), which can be replaced with more powerful alternatives.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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