---
license: mit
---

# ESM-2 Full Finetune for Binding Sites

This model is a full finetune of ESM-2, to illustrate how full finetuning overfits and generalizes quite poorly compared to 
LoRA and QLoRA finetuning. This model was finetuned on the 600K dataset. We also note that on the 24GB A10 GPU, the batch size 
has to be significantly smaller than when using LoRA or QLoRA. To finetune a similar model, use 
[this script](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_binding_sites_finetune/blob/main/finetune.py).

## Overfitting 

```python
Train metrics:

{'eval_loss': 0.13651661574840546,
'eval_accuracy': 0.9656322509450104,
'eval_precision': 0.38616650354104665,
'eval_recall': 0.9618091516702236,
'eval_f1': 0.55107594226701,
'eval_auc': 0.9637635647574605,
'eval_mcc': 0.5977943918337999}

Test metrics:

{'eval_loss': 0.2910114824771881,
'eval_accuracy': 0.923270649115702,
'eval_precision': 0.14887069127765168,
'eval_recall': 0.533511928419524,
'eval_f1': 0.23278520670392827,
'eval_auc': 0.7327381144575454,
'eval_mcc': 0.25329082069818704}
```