---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/esm2_t36_3B_UR50D
metrics:
- precision
- recall
- accuracy
model-index:
- name: esm2-t36-3B-lora-16-remote-homology-filtered
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# esm2-t36-3B-lora-16-remote-homology-filtered

This model is a fine-tuned version of [facebook/esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4403
- Precision: 0.7922
- Recall: 0.8139
- F1-score: 0.8029
- Accuracy: 0.7990

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.535         | 0.9992 | 664  | 0.5186          | 0.8002    | 0.6630 | 0.7252   | 0.7472   |
| 0.4946        | 2.0    | 1329 | 0.5065          | 0.6945    | 0.8969 | 0.7828   | 0.7496   |
| 0.4727        | 2.9992 | 1993 | 0.4592          | 0.7917    | 0.7876 | 0.7897   | 0.7889   |
| 0.4439        | 4.0    | 2658 | 0.4471          | 0.8087    | 0.7798 | 0.7940   | 0.7964   |
| 0.4234        | 4.9962 | 3320 | 0.4403          | 0.7922    | 0.8139 | 0.8029   | 0.7990   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1