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Open-weight model · Fill mask

bert-base-german-cased

by BERT community google-bert/bert-base-german-cased

Eval data: Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification) Update April 3rd, 2020: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens.

Parameters110M
Context512
Weights2.4 GB
Licensemit
AccessOpen weights
Monthly Downloads429.5k

Runs On

What it takes to serve bert-base-german-cased (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.

SAVRN's Notes on bert-base-german-cased

Fill-mask work in German is the job here: a 110M parameter encoder with a 30,000-token vocabulary and a 512-token window, evaluated on named entity recognition and classification. At 16-bit the weights are 0.2 GB and the model needs 0.3 GB, so the cheapest listed setup, one MI300X with 192 GB at $1.85 per hour, leaves nearly the whole card idle. In our facilities this rides beside other work; the question is how many copies you pack, not whether it fits.

The MIT license permits commercial use, modification and redistribution as long as the copyright and permission notices travel with the files. Before committing, check that 512 tokens covers your documents, note that the publisher changed the vocabulary file in April 2020 and kept an old-vocab variant, and match your serving stack to one of the five shipped formats: safetensors, onnx, pytorch, jax or tf.

SAVRN Research, 2026-09-18

Model Card

By BERT community, published under mit, revision 0b061b4f7ce1.

German BERT

Overview

Language model: bert-base-cased
Language: German
Training data: Wiki, OpenLegalData, News (~ 12GB)
Eval data: Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification)
Infrastructure: 1x TPU v2
Published: Jun 14th, 2019

Update April 3rd, 2020: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens. For details see the related FARM issue. If you want to use the old vocab we have also uploaded a "deepset/bert-base-german-cased-oldvocab" model.

Details

Read the full model card (446 words)

Configuration

Architecture
BertForMaskedLM
Context length (tokens)
512
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Vocabulary size
30,000
Model type
bert

Identity and Version

Repository
google-bert/bert-base-german-cased
Publisher
BERT community
Task
Fill mask
Modality
Text
Library
transformers
Parameters
110M parameters
Languages
de
Revision
0b061b4f7ce140d10dce6b6bd8a77da8ec80931b
First published
2022-03-02
Last updated
2024-02-19

Files and Weights

18 files, 2.4 GB in total. The weights are 5 files totalling 2.4 GB in bin, h5, msgpack, onnx, safetensors.

Weights5 files · 2.4 GB
Configuration5 files · 1.3 KB
Tokenizer6 files · 1.7 MB
Documentation1 file · 4.2 KB
Repository1 file · 445 B
Every file
FileTypeSizeSHA-256
flax_model.msgpackWeights436.5 MB fb127d4c33e1
model.safetensorsWeights438.8 MB a89b4ca42f0a
onnx/model.onnxWeights528.9 MB c5bfbc7da6be
pytorch_model.binWeights438.9 MB 56a21938415b
tf_model.h5Weights532.9 MB 3318e2e8ae8e
config.jsonConfiguration433 B —
onnx/added_tokens.jsonConfiguration74 B —
onnx/config.jsonConfiguration606 B —
onnx/generation_config.jsonConfiguration90 B —
onnx/special_tokens_map.jsonConfiguration125 B —
README.mdDocumentation4.2 KB —
.gitattributesRepository445 B —
onnx/tokenizer.jsonTokenizer726.3 KB —
onnx/tokenizer_config.jsonTokenizer1.2 KB —
onnx/vocab.txtTokenizer254.7 KB —
tokenizer.jsonTokenizer485.1 KB —
tokenizer_config.jsonTokenizer49 B —
vocab.txtTokenizer254.7 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
2.4 GB
Download from BERT community

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

Memory Requirements

PrecisionWeights in memory
As published2.4 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.

Compare bert-base-german-cased

Questions About bert-base-german-cased

How much GPU memory does bert-base-german-cased 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-german-cased 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-german-cased commercially?

Yes. bert-base-german-cased is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is bert-base-german-cased's context length?

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

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