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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline

**[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 5 min read · 0 upvotes · 0 comments

## Summary

A step-by-step tutorial demonstrates building a scikit-learn pipeline that unifies text embeddings with tabular features for classification. Using Hugging Face's sentence-transformers (all-MiniLM-L6-v2) to embed text via a custom TransformerMixin class, combined with numeric and categorical features processed through a ColumnTransformer, then fed into a random forest classifier. The example uses the SMS Spam Collection dataset augmented with synthetic tabular data to simulate a customer churn/triage spam-detection scenario, and evaluates the resulting model's accuracy.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://machinelearningmastery.com/combining-llm-embeddings-with-tabular-features-in-a-unified-scikit-learn-pipeline>

## Questions this post answers

### How can I combine text embeddings with tabular features in a single scikit-learn pipeline?

Create a custom transformer class inheriting from TransformerMixin and BaseEstimator that generates embeddings with a sentence-transformers model like all-MiniLM-L6-v2 inside its transform() method, then combine it with numeric and categorical preprocessing branches using a ColumnTransformer, followed by a classifier such as a random forest.

_See how developers wire up mixed text and tabular pipelines like this one by following ML engineering content on daily.dev._

### Which sentence-transformers model works well for generating lightweight, CPU-friendly text embeddings?

The all-MiniLM-L6-v2 model from Hugging Face's sentence-transformers library is a lightweight, CPU-friendly option for generating text embeddings, avoiding the need for a paid API like OpenAI or Google Gemini, or a massive open-source model like LLaMA 3.

_Developers weighing embedding model options can follow more comparisons like this on daily.dev._

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- [7 Advanced Feature Engineering Tricks for Text Data Using LLM Embeddings](https://daily.dev/posts/7-advanced-feature-engineering-tricks-for-text-data-using-llm-embeddings-ocyzcaq7e) · Machine Learning Mastery · 2 upvotes · 0 comments
- [From Text to Tables: Feature Engineering with LLMs for Tabular Data](https://daily.dev/posts/from-text-to-tables-feature-engineering-with-llms-for-tabular-data-wtgtcffld) · Machine Learning Mastery · 1 upvotes · 0 comments
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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#embeddings](https://daily.dev/tags/embeddings), [#scikit](https://daily.dev/tags/scikit)

[View this post on daily.dev](https://daily.dev/posts/combining-llm-embeddings-with-tabular-features-in-a-unified-scikit-learn-pipeline-xickiagte)

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