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) of 
the [esm2_t6_8M_UR50D](https://huggingface.co./facebook/esm2_t6_8M_UR50D) model for the (binary) token classification task of 
predicting RNA binding sites of proteins. 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 `80/20` train/test split. This model was trained with class weighting due to the imbalanced nature 
of the RNA binding site dataset (fewer binding sites than non-binding sites). You can train your own version 
using [this notebook](https://huggingface.co./AmelieSchreiber/esm2_t6_8M_weighted_lora_rna_binding/blob/main/LoRA_binding_sites_no_sweeps_v2.ipynb)! 
You just need the RNA `binding_sites.xml` file [found here](https://huggingface.co./datasets/AmelieSchreiber/data_of_protein-rna_binding_sites). 
You may also need to run some `pip install` statements at the beginning of the script. If you are running in colab run:

```python
!pip install transformers[torch] datasets peft -q
```
```python
!pip install accelerate -U -q
```
Try to improve upon these metrics by adjusting the hyperparameters:
```
{'eval_loss': 0.49476009607315063,
'eval_precision': 0.14372964169381108,
'eval_recall': 0.7526652452025586,
'eval_f1': 0.24136752136752138,
'eval_auc': 0.7710141129858947,
'epoch': 15.0}
```

A similar model can also be trained using the Github with a training script and conda env YAML, which can be
[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). This version uses wandb sweeps for hyperparameter search. 
However, it does not use class weighting. 


### Framework versions

- PEFT 0.4.0

## Using the Model

To use the model, try running the following pip install statements:
```python
!pip install transformers peft -q
```
then try tunning:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t6_8M_weighted_lora_rna_binding"
# ESM2 base model
base_model_path = "facebook/esm2_t6_8M_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]))

```