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Open-weight model · Fill mask

bert-base-uncased

by BERT community google-bert/bert-base-uncased

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.

Parameters110M
Context512
Weights3.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads47.2M

Runs On

What it takes to serve bert-base-uncased (110M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.2 GB 0.3 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By BERT community, published under apache-2.0, revision 86b5e0934494.

BERT base model (uncased)

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

Read the full model card (1,267 words)

Configuration

Architecture
BertForMaskedLM
Context length (tokens)
512
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Vocabulary size
30,522
Model type
bert

Identity and Version

Repository
google-bert/bert-base-uncased
Publisher
BERT community
Task
Fill mask
Modality
Text
Library
transformers
Parameters
110M parameters
Languages
en
Revision
86b5e0934494bd15c9632b12f734a8a67f723594
First published
2022-03-02
Last updated
2024-02-19

Files and Weights

16 files, 3.5 GB in total. The weights are 8 files totalling 3.5 GB in bin, h5, mlmodel, msgpack, onnx, ot, safetensors.

Weights8 files · 3.5 GB
Configuration2 files · 1.2 KB
Tokenizer3 files · 697.6 KB
Documentation2 files · 21.9 KB
Repository1 file · 491 B
Every file
FileTypeSizeSHA-256
coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodelWeights164.9 KB —
coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.binWeights531.8 MB c0c9f4914b4f
flax_model.msgpackWeights438.1 MB ea201fabe466
model.onnxWeights532.1 MB 44d7a2896d34
model.safetensorsWeights440.4 MB 68d45e234eb4
pytorch_model.binWeights440.5 MB 097417381d6c
rust_model.otWeights534.2 MB afd9aa425fd4
tf_model.h5Weights536.1 MB a7a17d6d844b
config.jsonConfiguration570 B —
coreml/fill-mask/float32_model.mlpackage/Manifest.jsonConfiguration617 B —
LICENSEDocumentation11.4 KB —
README.mdDocumentation10.5 KB —
.gitattributesRepository491 B —
tokenizer.jsonTokenizer466.1 KB —
tokenizer_config.jsonTokenizer48 B —
vocab.txtTokenizer231.5 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
3.5 GB
Download from BERT community

Released by BERT community through its official repository on Hugging Face. Read the license.

Built From

  • Described by arXiv:1810.04805
  • Trained on (disclosed) bookcorpus
  • Trained on (disclosed) wikipedia

Memory Requirements

PrecisionWeights in memory
As published3.5 GB
16-bit0.2 GB
8-bit0.1 GB
4-bit0.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Built on This Model

Compare bert-base-uncased

Questions About bert-base-uncased

How much GPU memory does bert-base-uncased need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (110M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run bert-base-uncased on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use bert-base-uncased commercially?

Yes. bert-base-uncased is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is bert-base-uncased's context length?

512 tokens, from the maximum position embeddings in its published configuration.

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