---
title: "sentence-transformers | Skills We Assign For | Azendo"
description: "sentence-transformers for embeddings — model choice, domain fine-tuning, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/sentence-transformers/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# sentence-transformers.

sentence-transformers is a Python library for computing dense vector representations of text, providing pretrained embedding models and tooling to fine-tune them on domain-specific data.

## Where sentence-transformers fits on a long engagement.

Self-hosted embeddings change the economics of retrieval. Embedding a large corpus through a provider API is a meaningful cost and an ongoing one as documents change; a local model removes both, and for many corpora the quality difference is smaller than the cost difference.

Domain fine-tuning is where the real gains are. General embedding models perform poorly on specialised vocabulary — medical, legal, industrial — because similar-looking terms have distinct meanings. Fine-tuning on a few thousand domain pairs often improves retrieval more than any other single change.

## What an assigned team does with sentence-transformers.

Changing embedding model means re-embedding everything. Vectors from different models are not comparable, so the decision carries a migration cost that grows with the corpus.

Evaluating candidates properly before committing is therefore worth real time, and it is the kind of upfront work scoped under [managed ai services](https://azendo.co/services/ai-engineering/).

## What we use sentence-transformers for.

* Embedding cost removed Local models where a large or frequently changing corpus makes API embedding expensive.
* Fine-tuning on domain vocabulary Specialised terminology handled, where general models conflate distinct terms.
* Model chosen before the corpus grows Evaluation upfront, because changing later means re-embedding everything.

## How sentence-transformers capacity is assigned.

Embedding work is assigned inside AI capacity, with model selection evaluated before ingestion because migration cost grows with the corpus.

## Roles we assign sentence-transformers for

* [NLP Engineer AI engineering](https://azendo.co/services/ai-engineering/nlp-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)

## Tell us what your roadmap needs sentence-transformers for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)