bert-base-multilingual-cased

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bert-base-multilingual-uncased

BERT community

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…

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.

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ModernBERT-base

Answer.AI

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…

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logun-base

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

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bert-base-arabertv02

AUB MIND LAB

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…

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distilbert-base-multilingual-cased

DistilBERT community

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…