---
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)
```