Model descriptions

<h1>Model descriptions</h1>

PAIR ([paper](https://www.biorxiv.org/content/10.1101/2024.07.22.604688)) is a flexible fine-tuning framework to improve the quality of protein representations for function predictions. PAIR uses a text decoder to guide the fine-tuning process of a protein encoder so that the learned representations can extract information contained within the diverse set of annotations in Swiss-Prot. This model fine-tunes ESM-1b ([repo](https://huggingface.co/facebook/esm1b_t33_650M_UR50S)) with PAIR.  

<h1>Intended use</h1>

The model can be used for feature extractions in protein function prediction tasks. 

<h1>How to load the model for feature extractions?</h1>

```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
model = AutoModel.from_pretrained("h4duan/PAIR-esm1b").to("cuda")
protein = ["AETCZAO"]

def extract_feature(protein):
  ids = tokenizer(protein, return_tensors="pt", padding=True, max_length=1024, truncation=True, return_attention_mask=True)
  input_ids = torch.tensor(ids['input_ids']).to("cuda")
  attention_mask = torch.tensor(ids['attention_mask']).to("cuda")
  with torch.no_grad():
    embedding_repr = model(input_ids=input_ids,attention_mask=attention_mask).last_hidden_state
  return torch.mean(embedding_repr, dim=1)

feature = extract_feature(protein)
```

<h1>How to extract the features in batch?</h1>

```python
proteins = ["AETCZAO","SKTZP"]

def extract_features_batch(proteins):
  ids = tokenizer(proteins, return_tensors="pt", padding=True, max_length=1024, truncation=True, return_attention_mask=True)
  input_ids = torch.tensor(ids['input_ids']).to("cuda")
  attention_mask = torch.tensor(ids['attention_mask']).to("cuda")
  with torch.no_grad():
    embedding_repr = model(input_ids=input_ids,attention_mask=attention_mask).last_hidden_state
  attention_mask = attention_mask.unsqueeze(-1)
  attention_mask = attention_mask.expand(-1, -1, embedding_repr.size(-1))
  masked_embedding_repr = embedding_repr * attention_mask
  sum_embedding_repr = masked_embedding_repr.sum(dim=1)
  non_zero_count = attention_mask.sum(dim=1) 
  mean_embedding_repr = sum_embedding_repr / non_zero_count
  return mean_embedding_repr

feature = extract_feature_batch(proteins)
```