# Hugging Face — Training and Finetuning Sparse Embedding Models with Sentence Transformers

- Company: Hugging Face (huggingface.co)
- Announced: 2025-07-01T00:00:00+00:00
- Subject: Platform
- Models affected: sparse-encoder/example-inference-free-splade-distilbert-base-uncased-nq, naver/splade-v3
- Source: https://huggingface.co/blog/train-sparse-encoder
- Record: https://forck.live/items/1670-training-and-finetuning-sparse-embedding-models-with-sentence-transformers

A blog post explaining how to use the Sentence Transformers library to train and finetune sparse embedding models, including components like model, datasets, loss functions, training arguments, evaluators, and trainer class, with practical examples.

## Evidence

Verbatim from https://huggingface.co/blog/train-sparse-encoder:

> Sentence Transformers is a Python library for using and training dense embedding, reranker (cross encoder), and sparse embedding models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. In this blogpost, I'll show you how to use it to finetune a sparse encoder/embedding model and explain why you might want to do so.

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