---
license: bsd-3-clause
library_name: peft
tags:
- generated_from_trainer
base_model: hugohrban/progen2-small
metrics:
- precision
- recall
- accuracy
model-index:
- name: progen2-small-lora-64-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. -->

# progen2-small-lora-64-remote-homology-filtered

This model is a fine-tuned version of [hugohrban/progen2-small](https://huggingface.co/hugohrban/progen2-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1353
- Precision: 0.9499
- Recall: 0.9699
- F1-score: 0.9598
- Accuracy: 0.9595

## 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: 192
- eval_batch_size: 192
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 384
- 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

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.3088        | 0.9985 | 332  | 0.2490          | 0.8692    | 0.9277 | 0.8975   | 0.8943   |
| 0.1806        | 2.0    | 665  | 0.1541          | 0.9259    | 0.9507 | 0.9382   | 0.9375   |
| 0.1036        | 2.9985 | 997  | 0.1190          | 0.9522    | 0.9541 | 0.9532   | 0.9532   |
| 0.0533        | 4.0    | 1330 | 0.1140          | 0.9538    | 0.9628 | 0.9582   | 0.9581   |
| 0.0272        | 4.9925 | 1660 | 0.1353          | 0.9499    | 0.9699 | 0.9598   | 0.9595   |


### Framework versions

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