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---
library_name: peft
license: mit
datasets:
- AmelieSchreiber/binding_sites_random_split_by_family_550K
metrics:
- accuracy
- f1
- roc_auc
- precision
- recall
- matthews_correlation
---
## Training procedure
This model was finetuned on ~549K protein sequences from the UniProt database. The dataset can be found
[here](https://huggingface.co/datasets/AmelieSchreiber/binding_sites_random_split_by_family_550K). The model obtains
the following test metrics:
```
Epoch Training Loss Validation Loss Accuracy Precision Recall F1 Auc Mcc
1 0.037400 0.301413 0.939431 0.366282 0.833003 0.508826 0.888300 0.528311
```
The dataset size increase from ~209K protein sequences to ~549K clearly improved performance in terms of test metric.
We used Hugging Face's parameter efficient finetuning (PEFT) library to finetune with Low Rank Adaptation (LoRA). We decided
to use a rank of 2 for the LoRA, as this was shown to slightly improve the test metrics compared to rank 8 and rank 16 on the
same model trained on the smaller dataset.
### Framework versions
- PEFT 0.5.0
## Using the model
To use the model on one of your protein sequences try running the following:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch
# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_lora_binding_sites_v2_cp1"
# 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]))
``` |