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--- |
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license: bsd |
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tags: |
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- chemistry |
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- biology |
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- protein |
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- antibodies |
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- antibody |
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- light chain |
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- AbLang |
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- CDR |
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- OAS |
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--- |
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# AbLang model for light chains |
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This is a huggingface version of AbLang: A language model for antibodies. It was introduced in |
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[this paper](https://doi.org/10.1101/2022.01.20.477061) and first released in |
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[this repository](https://github.com/oxpig/AbLang). This model is trained on uppercase amino acids: it only works with capital letter amino acids. |
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# Intended uses & limitations |
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The model could be used for protein feature extraction or to be fine-tuned on downstream tasks (TBA). |
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### How to use |
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Here is how to use this model to get the features of a given antibody sequence in PyTorch: |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('qilowoq/AbLang_light') |
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model = AutoModel.from_pretrained('qilowoq/AbLang_light', trust_remote_code=True) |
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sequence_Example = ' '.join("DIQMTQSPSTLSASIGDTVRISCRASQSITGNWVAWYQQRPGKAPRLLIYRGAALLGGVPSRFSGSAAGTDFTLTIGNLQAEDFGTFYCQQYDTYPGTFGQGTKVEVKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNR") |
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encoded_input = tokenizer(sequence_Example, return_tensors='pt') |
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model_output = model(encoded_input) |
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``` |
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Sentence embeddings can be produced as follows: |
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```python |
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seq_embs = model_output.last_hidden_state[:, 0, :] |
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``` |
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### Citation |
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``` |
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@article{Olsen2022, |
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title={AbLang: An antibody language model for completing antibody sequences}, |
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author={Tobias H. Olsen, Iain H. Moal and Charlotte M. Deane}, |
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journal={bioRxiv}, |
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doi={https://doi.org/10.1101/2022.01.20.477061}, |
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year={2022} |
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} |
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``` |
