---
license: bsd
tags:
- chemistry
- biology
- protein
- antibodies
- antibody
- heavy chain
- AbLang
- CDR
- OAS
---

# AbLang model for heavy chains

This is a huggingface version of AbLang: A language model for antibodies. It was introduced in
[this paper](https://doi.org/10.1101/2022.01.20.477061) and first released in
[this repository](https://github.com/oxpig/AbLang). This model is trained on uppercase amino acids: it only works with capital letter amino acids.


# Intended uses & limitations

The model could be used for protein feature extraction or to be fine-tuned on downstream tasks (TBA).

### How to use

Since this is a custom model, you need to install additional dependencies:

```python
pip install ablang
```

Here is how to use this model to get the features of a given antibody sequence in PyTorch:

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('qilowoq/AbLang_heavy')
model = AutoModel.from_pretrained('qilowoq/AbLang_heavy', trust_remote_code=True)

sequence_Example = ' '.join("EVQLQESGPGLVKPSETLSLTCTVSGGPINNAYWTWIRQPPGKGLEYLGYVYHTGVTNYNPSLKSRLTITIDTSRKQLSLSLKFVTAADSAVYYCAREWAEDGDFGNAFHVWGQGTMVAVSSASTKGPSVFPLAPSSKSTSGGTAALGCL")
encoded_input = tokenizer(sequence_Example, return_tensors='pt')
model_output = model(**encoded_input)
```

Sequence embeddings can be produced as follows:

```python
seq_embs = model_output.last_hidden_state[:, 0, :]
```

### Citation
```
@article{Olsen2022,
  title={AbLang: An antibody language model for completing antibody sequences},
  author={Tobias H. Olsen, Iain H. Moal and Charlotte M. Deane},
  journal={bioRxiv},
  doi={https://doi.org/10.1101/2022.01.20.477061},
  year={2022}
}
```