esm2_t12_35M_weighted_lora_rna_binding

ESM-2 RNA Binding Site LoRA

This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation (LoRA) of the esm2_t12_35M_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.

Training procedure

This is a Low Rank Adaptation (LoRA) of esm2_t12_35M_UR50D, trained on 166 protein sequences in the RNA binding sites dataset 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! You just need the RNA binding_sites.xml file found here. You may also need to run some pip install statements at the beginning of the script. If you are running in colab run:

!pip install transformers[torch] datasets peft -q

!pip install accelerate -U -q

Try to improve upon these metrics by adjusting the hyperparameters:

{'eval_loss': 0.4705817401409149,
'eval_precision': 0.1656961355214399,
'eval_recall': 0.7417061611374408,
'eval_f1': 0.27087840761575077,
'eval_auc': 0.7983065882100389,
'epoch': 9.0}

A similar model can also be trained using the Github with a training script and conda env YAML, which can be found here. 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:

!pip install transformers peft -q

then try tunning:

from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_weighted_lora_rna_binding"
# ESM2 base model
base_model_path = "facebook/esm2_t12_35M_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]))