---
title: "BERT-family models | Skills We Assign For | Azendo"
description: "BERT-family models for classification and extraction — why encoders still win, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/bert-family-models/"
---

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

# BERT-family models.

BERT and its descendants are encoder transformer models producing contextual representations of text. They are fine-tuned for classification, named entity recognition, similarity and extraction rather than generation.

## Where BERT-family models fits on a long engagement.

Encoder models remain the better answer for bounded tasks, and this is consistently underappreciated in the current environment. A fine-tuned BERT classifier runs in milliseconds on a CPU, costs effectively nothing per inference, and frequently beats a large generative model prompted for the same classification.

They need labelled training data, which is the real cost. A few thousand labelled examples is a meaningful investment, and it is why teams reach for a prompted language model instead — which is right for low volume and wrong once the same classification is running millions of times.

## What an assigned team does with BERT-family models.

The crossover is usually clear once someone calculates it. At high volume, the labelling cost is recovered quickly against per-token inference spend, and latency improves by an order of magnitude at the same time.

Making that comparison rather than defaulting to a generative model is part of the engineering judgement assigned under [ai engineering services](https://azendo.co/services/ai-engineering/).

## What we use BERT-family models for.

* High-volume classification Millisecond CPU inference where per-token generative cost would dominate.
* Entity extraction at scale Structured fields pulled from documents reliably and cheaply.
* The labelling investment justified Crossover volume calculated, so the decision is arithmetic rather than fashion.

## How BERT-family models capacity is assigned.

Model architecture selection is assigned inside AI capacity, with encoder and generative options compared on cost and latency at real volume.

## Roles we assign BERT-family models 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

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [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/)

## Tell us what your roadmap needs BERT-family models 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/)