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---
license: other
widget:
- text: "Ḣ Q V Q [MASK] E"
---
## AntiBERTa2 🧬
AntiBERTa2 is an antibody-specific language model based on the [RoFormer model](https://arxiv.org/abs/2104.09864) - 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](https://arxiv.org/abs/2103.00020).
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](mailto:info@alchemab.com).
| Model variant | Parameters | Config |
| ------------- | ---------- | ------ |
| [AntiBERTa2](https://huggingface.co/alchemab/antiberta2) | 202M | 24L, 12H, 1024d |
| [AntiBERTa2-CSSP](https://huggingface.co/alchemab/antiberta2-cssp) | 202M | 24L, 12H, 1024d |
## Example usage
```
>>> from transformers import (
RoFormerModel,
RoFormerTokenizer,
RoFormerForSequenceClassification
)
>>> tokenizer = RoFormerTokenizer.from_pretrained("alchemab/antiberta2")
>>> model = RoFormerModel.from_pretrained("alchemab/antiberta2")
>>> model(**tokenizer("Ḣ Q V Q ... T V S S", return_tensors='pt')).last_hidden_state... # etc
>>> new_model = RoFormerForSequenceClassification.from_pretrained(
"alchemab/antiberta2-cssp") # this will of course raise warnings
# that a new linear layer will be added
# and randomly initialized
```
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