# bert-base-multilingual-cased by BERT community: Open Model Source: https://savrn.com/models/bert-base-multilingual-cased Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bert-base-multilingual-cased (179M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.4 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [bert-base-multilingual-cased on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bert-base-multilingual-cased/gpus) ## SAVRN's Notes on bert-base-multilingual-cased Nothing in the memory line will drive a hardware decision. The 16-bit weights are 0.4 GB and the model needs 0.4 GB to run; 8-bit takes 0.2 GB and 4-bit 0.1 GB, so the cheapest setup we list, one MI300X with 192 GB at $1.85 an hour on-demand, is priced for a different class of model. It predicts masked words, case-sensitive, in the 104 languages with the largest Wikipedias, and the hardware question is throughput per dollar, not fit. Apache 2.0 clears commercial use, modification and redistribution; keep the license, the copyright notices and any NOTICE file, and state significant changes. Three checks before you build on it. The 512-token context caps passage length. The only training data on record is Wikipedia. And the repository holds 3.2 GB across four formats, safetensors, pytorch, jax and tf, when the 16-bit weights you need are 0.4 GB. SAVRN Research, 2026-09-18 ## Model Card By BERT community, published under apache-2.0, revision 3f076fdb1ab6. ### BERT multilingual base model (cased) Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in [this paper](https://savrn.com/papers/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding) and first released in [this repository](https://github.com/google-research/bert). This model is case sensitive: it makes 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 multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling 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 (883 words)](https://savrn.com/models/bert-base-multilingual-cased/card) ## Configuration Architecture BertForMaskedLM Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 119,547 Model type bert ## Identity and Version Repository google-bert/bert-base-multilingual-cased Publisher BERT community Task Fill mask Modality Text Library transformers Parameters 179M parameters Languages af, sq, ar, an, hy, ast, az, ba Revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 First published 2022-03-02 Last updated 2024-02-19 ## Files and Weights 10 files, 3.2 GB in total. The weights are 4 files totalling 3.2 GB in bin, h5, msgpack, safetensors. Weights4 files · 3.2 GB Configuration1 file · 625 B Tokenizer3 files · 3.0 MB Documentation1 file · 7.1 KB Repository1 file · 445 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 711.9 MB | ebee9a143c50 | | model.safetensors | Weights | 714.3 MB | 876f584f15eb | | pytorch_model.bin | Weights | 714.3 MB | 3496a508a9a3 | | tf_model.h5 | Weights | 1.1 GB | 8c6fe40eebca | | config.json | Configuration | 625 B | — | | README.md | Documentation | 7.1 KB | — | | .gitattributes | Repository | 445 B | — | | tokenizer.json | Tokenizer | 2.0 MB | — | | tokenizer_config.json | Tokenizer | 49 B | — | | vocab.txt | Tokenizer | 995.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 3.2 GB [Download from BERT community](https://huggingface.co/google-bert/bert-base-multilingual-cased) Released by BERT community through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1810.04805](https://savrn.com/papers/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding) - Trained on (disclosed) wikipedia ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 3.2 GB | | 16-bit | 0.4 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Derived from[ai-agent-sme-sentiment-mbert](https://savrn.com/models/ai-agent-sme-sentiment-mbert) ## Compare bert-base-multilingual-cased - [bert-base-multilingual-cased vs bert-base-multilingual-uncased](https://savrn.com/models/compare/bert-base-multilingual-cased-vs-bert-base-multilingual-uncased) ## Questions About bert-base-multilingual-cased ### How much GPU memory does bert-base-multilingual-cased need? About 0.4 GB at 16-bit and 0.1 GB at 4-bit: the weights (179M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bert-base-multilingual-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-multilingual-cased commercially? Yes. bert-base-multilingual-cased 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-multilingual-cased's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Fill mask ### [bert-base-multilingual-uncased](https://savrn.com/models/bert-base-multilingual-uncased) [BERT community](https://savrn.com/model-publishers/google-bert) Pretrained model on the top 102 languages with the largest Wikipedia 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. BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process… Open weights apache-2.0 168M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-multilingual-uncased) Model · Fill mask ### [LAMB](https://savrn.com/models/lamb) [Aidan](https://savrn.com/model-publishers/aimgo) LAMB (LAtin ModernBERT) is a Latin encoder-only model based on the ModernBERT architecture, pre-trained on nearly 24B Latin tokens, and ready for use with any Latin orthography. If you use this in your work, please cite: Paper pending shortly. Access requested at publisher cc-by-nc-4.0 164M parameters [View model](https://savrn.com/models/lamb) Model · Fill mask ### [ModernBERT-base](https://savrn.com/models/modernbert-base) [Answer.AI](https://savrn.com/model-publishers/answerdotai) ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: - Rotary Positional Embeddings (RoPE) for long-context support. - Local-Global Alternating Attention for efficiency on long inputs. - Unpadding and Flash Attention for efficient inference. ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it… Open weights apache-2.0 150M parameters 8,192 tokens transformers [View model](https://savrn.com/models/modernbert-base) Model · Fill mask ### [logun-base](https://savrn.com/models/logun-base) [Heitorrosa](https://savrn.com/model-publishers/heitorrosa) This model is a fine-tuned version of Itau-Unibanco/NorBERTo-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 2 - evalbatchsize: 4 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupsteps: 500 - numepochs: 1.5 - mixedprecisiontraining: Native AMP - PEFT 0.20.0 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.23.1 Open weights cc-by-nc-sa-4.0 150M parameters 8,192 tokens peft [View model](https://savrn.com/models/logun-base) Model · Fill mask ### [bert-base-arabertv02](https://savrn.com/models/bert-base-arabertv02) [AUB MIND LAB](https://savrn.com/model-publishers/aubmindlab) AraBERT is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the Farasa Segmenter. We evaluate AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (To the extent of our knowledge). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and… Open weights 136M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-arabertv02) Model · Fill mask ### [distilbert-base-multilingual-cased](https://savrn.com/models/distilbert-base-multilingual-cased) [DistilBERT community](https://savrn.com/model-publishers/distilbert) This model is a distilled version of the BERT base multilingual model. The code for the distillation process can be found here. This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed here. The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average, this model, referred to as DistilmBERT, is twice as fast as mBERT-base. We encourage potential users of this model to check out the BERT base multilingual model card to learn more about usage, limitations and potential biases. You can use the raw model for either… Open weights apache-2.0 135M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-multilingual-cased) ## BERT community [All models and datasets](https://savrn.com/model-publishers/google-bert) ## Versions - [3f076fdb1ab6](https://savrn.com/models/bert-base-multilingual-cased/versions/3f076fdb1ab6) · current 2026-10-25 ## Explore More - [All fill mask models](https://savrn.com/models/tasks/fill-mask) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [BERT community website](https://github.com/google-research/bert) - [Hugging Face record](https://huggingface.co/google-bert/bert-base-multilingual-cased) - [How the hub is built](https://savrn.com/model-hub/methodology)