---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/esm2_t30_150M_UR50D
metrics:
- accuracy
model-index:
- name: esm2_t130_150M-lora-classifier_2024-04-25_21-48-08
  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_t130_150M-lora-classifier_2024-04-25_21-48-08

This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co./facebook/esm2_t30_150M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5189
- Accuracy: 0.8809

## 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: 0.0005701568055793089
- train_batch_size: 12
- eval_batch_size: 12
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6192        | 1.0   | 128  | 0.6737          | 0.6055   |
| 0.4321        | 2.0   | 256  | 0.6507          | 0.6289   |
| 0.571         | 3.0   | 384  | 0.5572          | 0.7188   |
| 0.3053        | 4.0   | 512  | 0.5090          | 0.7852   |
| 0.5055        | 5.0   | 640  | 0.3370          | 0.8516   |
| 0.2786        | 6.0   | 768  | 0.3710          | 0.8594   |
| 0.1327        | 7.0   | 896  | 0.3055          | 0.8711   |
| 0.2127        | 8.0   | 1024 | 0.2891          | 0.8945   |
| 0.0913        | 9.0   | 1152 | 0.3454          | 0.8691   |
| 0.0134        | 10.0  | 1280 | 0.3354          | 0.8809   |
| 0.2597        | 11.0  | 1408 | 0.3436          | 0.8848   |
| 0.0276        | 12.0  | 1536 | 0.4181          | 0.8633   |
| 0.0929        | 13.0  | 1664 | 0.3722          | 0.8789   |
| 0.9377        | 14.0  | 1792 | 0.5086          | 0.8730   |
| 0.2894        | 15.0  | 1920 | 0.3311          | 0.8906   |
| 0.3138        | 16.0  | 2048 | 0.4739          | 0.8809   |
| 0.0088        | 17.0  | 2176 | 0.3875          | 0.8867   |
| 0.3591        | 18.0  | 2304 | 0.4032          | 0.8809   |
| 0.0436        | 19.0  | 2432 | 0.4316          | 0.8887   |
| 0.0037        | 20.0  | 2560 | 0.4931          | 0.8789   |
| 0.0322        | 21.0  | 2688 | 0.4787          | 0.8809   |
| 0.0035        | 22.0  | 2816 | 0.4460          | 0.8770   |
| 0.0859        | 23.0  | 2944 | 0.4914          | 0.8828   |
| 0.039         | 24.0  | 3072 | 0.4955          | 0.8770   |
| 0.4208        | 25.0  | 3200 | 0.5211          | 0.8828   |
| 0.1874        | 26.0  | 3328 | 0.5376          | 0.8711   |
| 0.4433        | 27.0  | 3456 | 0.5319          | 0.875    |
| 0.2976        | 28.0  | 3584 | 0.5201          | 0.8809   |
| 0.0223        | 29.0  | 3712 | 0.5179          | 0.8809   |
| 0.0021        | 30.0  | 3840 | 0.5189          | 0.8809   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2