---
title: "Large Language Models: DeBERTa — Decoding-Enhanced BERT with Disentangled Attention"
url: https://daily.dev/posts/large-language-models-deberta-decoding-enhanced-bert-with-disentangled-attention-lbyyzelil
source_url: https://towardsdatascience.com/large-language-models-deberta-decoding-enhanced-bert-with-disentangled-attention-90016668db4b?source=rss----7f60cf5620c9---4
type: article
source: "Towards Data Science"
published: 2023-11-29T17:21:34.394Z
updated: 2023-11-29T17:21:32.939Z
tags: ["machine-learning", "nlp", "llm", "transformers", "bert"]
reading_time: 7
upvotes: 1
comments: 0
language: en
---

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

# Large Language Models: DeBERTa — Decoding-Enhanced BERT with Disentangled Attention

**[Towards Data Science](https://daily.dev/sources/tds)** · 7 min read · 1 upvotes · 0 comments

## Summary

DeBERTa is a model that incorporates disentangled attention and an enhanced mask decoder to improve language models. Disentangled attention helps capture content-to-position relations, while the enhanced mask decoder incorporates absolute positioning. These techniques have shown improvements in NLP benchmarks and have made DeBERTa a popular choice in NLP pipelines.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/large-language-models-deberta-decoding-enhanced-bert-with-disentangled-attention-90016668db4b?source=rss----7f60cf5620c9---4>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#nlp](https://daily.dev/tags/nlp), [#llm](https://daily.dev/tags/llm), [#transformers](https://daily.dev/tags/transformers), [#bert](https://daily.dev/tags/bert)

[View this post on daily.dev](https://daily.dev/posts/large-language-models-deberta-decoding-enhanced-bert-with-disentangled-attention-lbyyzelil)