# bert-base-arabertv02 by AUB MIND LAB: Open-Weight Model Source: https://savrn.com/models/bert-base-arabertv02 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bert-base-arabertv02 (136M 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 | 0.3 GB | 0.3 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 | 0.1 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-arabertv02 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bert-base-arabertv02/gpus) ## SAVRN's Notes on bert-base-arabertv02 If the text is Arabic and the job is masked-token prediction or a base for fine-tuning, AUB MIND LAB's 136M-parameter BERT-base model, trained on Wikipedia, Osian, the 1.5B-Arabic-Corpus, oscar-arabic-unshuffled and a private Assafir set, is the one on this page. Memory is a rounding error: 0.3 GB at 16-bit, 0.2 GB at 8-bit, 0.1 GB at 4-bit. The Index's cheapest host is a 192 GB MI300X at $1.85 per hour; you would never dedicate one to this. Here is the item that decides the deployment: the facts we hold list no license for this model. Open access means the files can be fetched, but with no stated terms we treat commercial use, modification and redistribution as unresolved until the publisher confirms them. Check that first. Then note the 512-token context, which means chunking anything longer than a page, and 24 files across five formats, so pick one. SAVRN Research, 2026-09-18 ## Model Card 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… Excerpt from the card by AUB MIND LAB. ## Configuration Architecture BertForMaskedLM Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 64,000 Model type bert ## Identity and Version Repository aubmindlab/bert-base-arabertv02 Publisher AUB MIND LAB Task Fill mask Modality Text Library transformers Parameters 136M parameters Languages ar Revision 016fb9d6768f522a59c6e0d2d5d5d43a4e1bff60 First published 2022-03-02 Last updated 2024-03-26 ## Files and Weights 24 files, 3.0 GB in total. The weights are 4 files totalling 2.4 GB in bin, h5, msgpack, safetensors. Weights4 files · 2.4 GB Configuration2 files · 496 B Tokenizer3 files · 3.5 MB Documentation1 file · 8.6 KB Other13 files · 644.6 MB Repository1 file · 502 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 541.0 MB | e0bbd3dffdc5 | | model.safetensors | Weights | 543.4 MB | 48873a2bb50c | | pytorch_model.bin | Weights | 543.5 MB | eac91bc3c317 | | tf_model.h5 | Weights | 741.9 MB | 7f1bc598fbc8 | | config.json | Configuration | 384 B | — | | special_tokens_map.json | Configuration | 112 B | — | | README.md | Documentation | 8.6 KB | — | | runs/eval/events.out.tfevents.1607681029.tpu-mother | Other | 2.6 MB | 7cd567fbe978 | | runs/eval/events.out.tfevents.1607686495.tpu-mother | Other | 2.7 MB | affcfcd40bb2 | | runs/eval/events.out.tfevents.1607688156.tpu-mother | Other | 2.6 MB | dc1dbb0c0f22 | | runs/eval_results.txt | Other | 159 B | — | | runs/events.out.tfevents.1605489565.tpu-mother | Other | 18.1 MB | d3d6144db24b | | runs/events.out.tfevents.1605730291.tpu-mother | Other | 18.1 MB | 06a04aa1aa66 | | runs/events.out.tfevents.1606636322.tpu-mother | Other | 18.1 MB | 240e55f19cf8 | | runs/events.out.tfevents.1606738342.tpu-mother | Other | 4.1 MB | 481d7808a125 | | runs/events.out.tfevents.1606738557.tpu-mother | Other | 18.1 MB | b2398ca5cab8 | | runs/events.out.tfevents.1607448114.tpu-mother | Other | 18.3 MB | 34a40cda6e3f | | runs/events.out.tfevents.1607698247.tpu-mother | Other | 18.4 MB | 8b56e2f1af02 | | runs/events.out.tfevents.1608595021.tpu-mother | Other | 18.3 MB | 9b6cda442142 | | tf1_model.tar.gz | Other | 505.0 MB | 38d3200f86bb | | .gitattributes | Repository | 502 B | — | | tokenizer.json | Tokenizer | 2.6 MB | — | | tokenizer_config.json | Tokenizer | 381 B | — | | vocab.txt | Tokenizer | 824.8 KB | — | ## License and Download License Not stated by the source Access Open weights, no gate Download size 2.4 GB [Download from AUB MIND LAB](https://huggingface.co/aubmindlab/bert-base-arabertv02) Released by AUB MIND LAB through its official repository on Hugging Face. ## Built From - Described by arXiv:2003.00104 - Trained on (disclosed) 1.5B-Arabic-Corpus - Trained on (disclosed) Assafir-private - Trained on (disclosed) Osian - Trained on (disclosed) oscar-arabic-unshuffled - Trained on (disclosed) wikipedia ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.4 GB | | 16-bit | 0.3 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare bert-base-arabertv02 - [bert-base-arabertv02 vs bert-base-multilingual-uncased](https://savrn.com/models/compare/bert-base-arabertv02-vs-bert-base-multilingual-uncased) - [bert-base-arabertv02 vs ModernBERT-base](https://savrn.com/models/compare/bert-base-arabertv02-vs-modernbert-base) - [bert-base-arabertv02 vs roberta-base](https://savrn.com/models/compare/bert-base-arabertv02-vs-roberta-base) ## Questions About bert-base-arabertv02 ### How much GPU memory does bert-base-arabertv02 need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (136M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bert-base-arabertv02 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. ### What is bert-base-arabertv02's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models 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. 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If you use our work, please cite Open weights mit 111M parameters 514 tokens transformers [View model](https://savrn.com/models/camembert-base) ## AUB MIND LAB [All models and datasets](https://savrn.com/model-publishers/aubmindlab) ## Versions - [016fb9d6768f](https://savrn.com/models/bert-base-arabertv02/versions/016fb9d6768f) · current 2026-09-18 ## Explore More - [All fill mask models](https://savrn.com/models/tasks/fill-mask) - [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 2026-10-20. - [Hugging Face record](https://huggingface.co/aubmindlab/bert-base-arabertv02) - [How the hub is built](https://savrn.com/model-hub/methodology)