BALM Paper
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Models from the publication: "Improving antibody language models with native pairing", Patterns (2024) • 4 items • Updated
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("brineylab/ft-ESM")
model = AutoModelForMaskedLM.from_pretrained("brineylab/ft-ESM")Quick Links
ft-ESM
ft-ESM is a finetuned version of the 650M-parameter ESM2 protein language model, finetuned on paired antibody sequences from Jaffe et al. Datasets used for pre-training are available on Zenodo and code is available on GitHub. More details can be found in our paper published in Patterns.
Use
Load the model and tokenizer as follows:
from transformers import EsmTokenizer, EsmForMaskedLM
model = EsmForMaskedLM.from_pretrained("brineylab/ft-ESM")
tokenizer = EsmTokenizer.from_pretrained("brineylab/ft-ESM")
The tokenizer expects sequences formatted as: HEAVY_CHAIN<cls><cls>LIGHT_CHAIN.
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Base model
facebook/esm2_t33_650M_UR50D
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="brineylab/ft-ESM")