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---
tags:
- antibody language model
- antibody
- protein language model
base_model: Exscientia/IgT5_unpaired
license: mit
---
# IgT5 model
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](https://arxiv.org/abs/2403.17889).
The model is finetuned from IgT5-unpaired using paired antibody sequences from the [Observed Antibody Space](https://opig.stats.ox.ac.uk/webapps/oas/).
# 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)
```
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