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BERT

Foundation model

Bidirectional Encoder Representations from Transformers

Release date: 2018-10-11Updated: 2023-05-28 18:51:44.623672
Live demoGitHubHugging FaceCompare
Parameters
340M
Context length
2K
Chinese support
Not supported
Reasoning ability

Bidirectional Encoder Representations from Transformers is an AI model published by Google Research, released on 2018-10-11, for Foundation model, with 340M parameters, and 2K context length, requiring about 1.3GB storage.

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

BERT

Model basics

Reasoning traces
Not supported
Thinking modes
Thinking modes not supported
Context length
2K tokens
Max output length
No data
Model type
Foundation model
Modality (in / out)
No data
Release date
2018-10-11
Model file size
1.3GB
MoE architecture
No
Total params / Active params
340M / N/A
Knowledge cutoff
No data
BERT

Open source & experience

Code license
Apache 2.0
Weights license
Apache 2.0- Commercial use permitted
GitHub repo
https://github.com/google-research/bert
Hugging Face
https://huggingface.co/docs/transformers/model_doc/bert
Live demo
No live demo
BERT

Official resources

Paper
Paper
DataLearnerAI blog
No blog post yet
BERT

API details

API speed
No data
No public API pricing yet.
BERT

Benchmark Results

No benchmark data to show.

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BERT

Publisher

Bidirectional Encoder Representations from Transformers

Model Overview

Bidirectional Encoder Representations from Transformers is an AI model published by Google Research, released on 2018-10-11, for Foundation model, with 340M parameters, and 2K context length, requiring about 1.3GB storage.

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