---
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_2024-04-26_10-08-51
  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_2024-04-26_10-08-51

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: 0.4537
- Accuracy: 0.8984

## 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: 0.0008701568055793088
- train_batch_size: 28
- eval_batch_size: 28
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6764        | 1.0   | 55   | 0.6794          | 0.5820   |
| 0.5521        | 2.0   | 110  | 0.6192          | 0.6777   |
| 0.5409        | 3.0   | 165  | 0.5147          | 0.7383   |
| 0.5518        | 4.0   | 220  | 0.3518          | 0.8672   |
| 0.1386        | 5.0   | 275  | 0.3596          | 0.8574   |
| 0.303         | 6.0   | 330  | 0.4030          | 0.8359   |
| 0.1962        | 7.0   | 385  | 0.3143          | 0.8848   |
| 0.1501        | 8.0   | 440  | 0.3232          | 0.8652   |
| 0.2994        | 9.0   | 495  | 0.3014          | 0.8770   |
| 0.0914        | 10.0  | 550  | 0.2980          | 0.8887   |
| 0.2108        | 11.0  | 605  | 0.2854          | 0.8770   |
| 0.2896        | 12.0  | 660  | 0.3684          | 0.8691   |
| 0.0818        | 13.0  | 715  | 0.3349          | 0.8828   |
| 0.3152        | 14.0  | 770  | 0.3530          | 0.8848   |
| 0.0554        | 15.0  | 825  | 0.3371          | 0.8887   |
| 0.1928        | 16.0  | 880  | 0.3347          | 0.875    |
| 0.2658        | 17.0  | 935  | 0.3765          | 0.8867   |
| 0.4242        | 18.0  | 990  | 0.4166          | 0.8945   |
| 0.0964        | 19.0  | 1045 | 0.3400          | 0.8945   |
| 0.0375        | 20.0  | 1100 | 0.3581          | 0.9004   |
| 0.1781        | 21.0  | 1155 | 0.3816          | 0.8848   |
| 0.1563        | 22.0  | 1210 | 0.3940          | 0.8867   |
| 0.017         | 23.0  | 1265 | 0.4098          | 0.8926   |
| 0.1866        | 24.0  | 1320 | 0.4710          | 0.8770   |
| 0.0632        | 25.0  | 1375 | 0.4541          | 0.8828   |
| 0.1501        | 26.0  | 1430 | 0.4645          | 0.8828   |
| 0.109         | 27.0  | 1485 | 0.4434          | 0.8926   |
| 0.0353        | 28.0  | 1540 | 0.4264          | 0.8984   |
| 0.4502        | 29.0  | 1595 | 0.4479          | 0.8984   |
| 0.0341        | 30.0  | 1650 | 0.4537          | 0.8984   |


### Framework versions

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