---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output_AAVESM2_650M_v1
  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. -->

# output_AAVESM2_650M_v1

This model is a fine-tuned version of [facebook/esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.8905

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 36.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log        | 1.0   | 390   | 1.3347          | 0.5993   |
| 1.5408        | 2.0   | 780   | 1.0699          | 0.6796   |
| 1.1283        | 3.0   | 1170  | 0.8751          | 0.7373   |
| 0.9078        | 4.0   | 1560  | 0.7534          | 0.7711   |
| 0.9078        | 5.0   | 1950  | 0.6711          | 0.8022   |
| 0.7705        | 6.0   | 2340  | 0.6078          | 0.8169   |
| 0.6863        | 7.0   | 2730  | 0.5668          | 0.8318   |
| 0.6277        | 8.0   | 3120  | 0.5461          | 0.8386   |
| 0.5863        | 9.0   | 3510  | 0.5143          | 0.8514   |
| 0.5863        | 10.0  | 3900  | 0.4992          | 0.8522   |
| 0.5564        | 11.0  | 4290  | 0.4940          | 0.8533   |
| 0.5199        | 12.0  | 4680  | 0.4727          | 0.8633   |
| 0.5025        | 13.0  | 5070  | 0.4586          | 0.8638   |
| 0.5025        | 14.0  | 5460  | 0.4549          | 0.8673   |
| 0.4814        | 15.0  | 5850  | 0.4442          | 0.8698   |
| 0.4746        | 16.0  | 6240  | 0.4306          | 0.8750   |
| 0.4527        | 17.0  | 6630  | 0.4291          | 0.8742   |
| 0.4382        | 18.0  | 7020  | 0.4213          | 0.8751   |
| 0.4382        | 19.0  | 7410  | 0.4193          | 0.8751   |
| 0.4328        | 20.0  | 7800  | 0.4143          | 0.8760   |
| 0.4191        | 21.0  | 8190  | 0.4071          | 0.8836   |
| 0.4106        | 22.0  | 8580  | 0.3980          | 0.8819   |
| 0.4106        | 23.0  | 8970  | 0.3987          | 0.8822   |
| 0.4037        | 24.0  | 9360  | 0.4027          | 0.8819   |
| 0.3893        | 25.0  | 9750  | 0.3868          | 0.8893   |
| 0.3991        | 26.0  | 10140 | 0.3882          | 0.8846   |
| 0.3786        | 27.0  | 10530 | 0.3939          | 0.8859   |
| 0.3786        | 28.0  | 10920 | 0.3959          | 0.8848   |
| 0.38          | 29.0  | 11310 | 0.3950          | 0.8850   |
| 0.3764        | 30.0  | 11700 | 0.3783          | 0.8893   |
| 0.3708        | 31.0  | 12090 | 0.3799          | 0.8891   |
| 0.3708        | 32.0  | 12480 | 0.3915          | 0.8867   |
| 0.3656        | 33.0  | 12870 | 0.3780          | 0.8903   |
| 0.3617        | 34.0  | 13260 | 0.3805          | 0.8874   |
| 0.361         | 35.0  | 13650 | 0.3776          | 0.8920   |
| 0.3595        | 36.0  | 14040 | 0.3712          | 0.8888   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2