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BERT

Model ● Covered in 14 stories Compare ⇄ + Follow

BERT is a 2018 machine learning model developed by Google that uses bidirectional learning to handle natural language processing tasks such as sentiment analysis and named entity recognition. Recent coverage shows BERT being optimized and deployed across various hardware platforms including ONNX Runtime, AWS Inferentia, Habana Gaudi accelerators, and Graphcore IPUs through Hugging Face libraries, with implementations achieving significant improvements in inference latency and cost efficiency.

Updated 3 August 2026

Specifications

No specifications recorded yet.

Latest developments

Accelerating over 130,000 Hugging Face models with ONNX Runtime Hugging Face Blog · 2 years ago · 15

The Transformer Family Version 2.0 Lilian Weng · 3 years ago · 42

Pre-Train BERT with Hugging Face Transformers and Habana Gaudi Hugging Face Blog · 3 years ago · 49

Hugging Face's TensorFlow Philosophy Hugging Face Blog · 3 years ago · 21

Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers Hugging Face Blog · 4 years ago · 1

Getting Started with Transformers on Habana Gaudi Hugging Face Blog · 4 years ago · 40

Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia Hugging Face Blog · 4 years ago · 30

BERT 101 - State Of The Art NLP Model Explained Hugging Face Blog · 4 years ago · 44

Timeline

Month Quarter Year

Q4 2023

4 Oct Accelerating over 130,000 Hugging Face models with ONNX Runtime

Q1 2023

27 Jan The Transformer Family Version 2.0

Q3 2022

Q2 2022

Q1 2022

Q4 2021

Q1 2021

14 Feb How to Win a Data Hackathon (Hacklytics 2021)

Q1 2019

31 Jan Generalized Language Models

Relationships

Products & technology

Hugging Face integrated with this model · 2 sources
Google develops this model · 1 source
Google integrated with this model · 1 source
Graphcore integrated with this model · 1 source
Intel integrated with this model · 1 source
DistilBERT derived from this model · 1 source
Hugging Face deploys this model · 1 source

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TLDRocket 3 August 2026

The story of AI in August 2026 is one of consolidation meeting enforcement. Alibaba's Qwen3.8-Max, a 2.4-trillion-parameter mixture-of-experts model with a million-token context window, arrives as a direct challenge to OpenAI and Anthropic's frontier offerings—priced at $2 per million input tokens, it's positioning itself as the capable-and-cheaper alternative that China's AI push has long promised. But the more consequential development is structural: enterprises are finally getting the plumbing to actually deploy these models. June, backed by Salesforce's Marc Benioff, raised $20 million to automate AI integration into legacy systems, scanning corporate infrastructure and generating deployment workflows without expensive consultant fees. This solves what's become clear as the real bottleneck—not model capability, but implementation friction in messy, heterogeneous corporate environments.

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