Model pretrained on protein and antibody sequences using a masked language modeling (MLM) objective. It was introduced in the paper Large scale paired antibody language models.

The model is finetuned from IgT5-unpaired using paired antibody sequences from the Observed Antibody Space.

Use

The encoder part of the model and tokeniser can be loaded using the transformers library

python

from transformers import T5EncoderModel, T5Tokenizer

tokeniser = T5Tokenizer.from_pretrained("Exscientia/IgT5", do_lower_case=False)
model = T5EncoderModel.from_pretrained("Exscientia/IgT5")

The tokeniser is used to prepare batch inputs

python

# heavy chain sequences
sequences_heavy = [
    "VQLAQSGSELRKPGASVKVSCDTSGHSFTSNAIHWVRQAPGQGLEWMGWINTDTGTPTYAQGFTGRFVFSLDTSARTAYLQISSLKADDTAVFYCARERDYSDYFFDYWGQGTLVTVSS",
    "QVQLVESGGGVVQPGRSLRLSCAASGFTFSNYAMYWVRQAPGKGLEWVAVISYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRTEDTAVYYCASGSDYGDYLLVYWGQGTLVTVSS"
]

# light chain sequences
sequences_light = [
    "EVVMTQSPASLSVSPGERATLSCRARASLGISTDLAWYQQRPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDSAVYYCQQYSNWPLTFGGGTKVEIK",
    "ALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVSKRPSGVSNRFSGSKSGNTASLTISGLQSEDEADYYCNSLTSISTWVFGGGTKLTVL"
]

# The tokeniser expects input of the form ["V Q ... S S </s> E V ... I K", ...]
paired_sequences = []
for sequence_heavy, sequence_light in zip(sequences_heavy, sequences_light):
    paired_sequences.append(' '.join(sequence_heavy)+' </s> '+' '.join(sequence_light))

tokens = tokeniser.batch_encode_plus(
    paired_sequences, 
    add_special_tokens=True, 
    pad_to_max_length=True, 
    return_tensors="pt",
    return_special_tokens_mask=True
)

Note that the tokeniser adds a </s> token at the end of each paired sequence and pads using the <pad> token. For example a batch containing sequences V Q L </s> E V V, Q V </s> A L will be tokenised to V Q L </s> E V V </S> and Q V </s> A L </s> <pad> <pad>.

Sequence embeddings are generated by feeding tokens through the model

python

output = model(
    input_ids=tokens['input_ids'], 
    attention_mask=tokens['attention_mask']
)

residue_embeddings = output.last_hidden_state

To obtain a sequence representation, the residue tokens can be averaged over like so

python

import torch

# mask special tokens before summing over embeddings
residue_embeddings[tokens["special_tokens_mask"] == 1] = 0
sequence_embeddings_sum = residue_embeddings.sum(1)

# average embedding by dividing sum by sequence lengths
sequence_lengths = torch.sum(tokens["special_tokens_mask"] == 0, dim=1)
sequence_embeddings = sequence_embeddings_sum / sequence_lengths.unsqueeze(1)