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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, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ALBERT 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 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:

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antiberta2

alchemab

AntiBERTa2 is an antibody-specific language model based on the RoFormer model - it is pre-trained using masked language modelling. We also provide a multimodal version of AntiBERTa2, AntiBERTa2-CSSP, that has been trained using a contrastive objective, similar to the CLIP method. Further details on both AntiBERTa2 and AntiBERTa2-CSSP are described in our paper accepted at the NeurIPS MLSB Workshop 2023. Both AntiBERTa2 models are only available for non-commercial use. Output antibody sequences (e.g. from infilling via masked language models) can only be used for non-commercial use. For any users seeking commercial use of our model and generated antibodies, please reach out to us at info@alchemab.com.

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