README.md

---
library_name: peft
license: mit
language:
- en
tags:
- transformers
- biology
- esm
- esm2
- protein
- protein language model
---
# ESM-2 RNA Binding Site LoRA

This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation ([LoRA](https://huggingface.co/docs/peft/task_guides/token-classification-lora)) of 
the [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) model for the (binary) token classification task of 
predicting RNA binding sites of proteins. The Github with the training script and conda env YAML can be
[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). You can also find a version of this model 
that was fine-tuned without LoRA [here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_UR50D_rna_binding_site_predictor). 

## Training procedure

This is a Low Rank Adaptation (LoRA) of `esm2_t6_8M_UR50D`, 
trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
using a `75/25` train/test split. It achieves an evaluation loss of `0.15336574614048004`. 

### Framework versions

- PEFT 0.4.0

This model uses a LoRA configuration with the rank of the LoRA set to `32`. In particular, the configuration is:

```python
peft_config = LoraConfig(
    task_type=TaskType.TOKEN_CLS, 
    inference_mode=False, 
    r=32, 
    lora_alpha=16, 
    target_modules=["query", "key", "value"],
    lora_dropout=0.1, 
    bias="all"
)
```

## Using the Model

To use, try running:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t30_150M_LoRA_RNA_binding"
# ESM2 base model
base_model_path = "facebook/esm2_t30_150M_UR50D"

# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)

# Ensure the model is in evaluation mode
loaded_model.eval()

# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)

# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT"  # Replace with your actual sequence

# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')

# Run the model
with torch.no_grad():
    logits = loaded_model(**inputs).logits

# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)

# Define labels
id2label = {
    0: "No binding site",
    1: "Binding site"
}

# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
    if token not in ['<pad>', '<cls>', '<eos>']:
        print((token, id2label[prediction]))

```