---
library_name: transformers
license: mit
---

## Preferential-250k
Preferential-250k is an antibody language model that uses an [ESM-2](https://www.science.org/doi/10.1126/science.ade2574) architecture. 
It was pre-trained on paired sequences from [Jaffe et al.](https://www.nature.com/articles/s41586-022-05371-z) and [Hurtado et al.](https://doi.org/10.1016/j.celrep.2024.114307) 
Datasets used for pre-training are available on [Zenodo](https://doi.org/10.5281/zenodo.14019655) and code is available on [GitHub](https://github.com/brineylab/preferential-masking-paper). 
More details can be found in [our paper](https://doi.org/10.1016/j.patter.2025.101239) published in Patterns.

### Use
Load the model and tokenizer as follows:
```python
from transformers import EsmTokenizer, EsmForMaskedLM

model = EsmForMaskedLM.from_pretrained("brineylab/preferential-250k")
tokenizer = EsmTokenizer.from_pretrained("brineylab/preferential-250k")
```

The tokenizer expects sequences formatted as: `HEAVY_CHAIN<cls><cls>LIGHT_CHAIN`.

The model can be finetuned for classification tasks (such as specificity and pair classification in the paper) by loading the model with a sequence classification head:
```python
from transformers import EsmForSequenceClassification

model = EsmForSequenceClassification.from_pretrained("brineylab/preferential-250k")

# freeze the base model weights prior to finetuning
for param in model.base_model.parameters():
    param.requires_grad = False
```