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# 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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "output_AAVESM2_650M_v1", "results": []}]}
transformers pytorch tensorboard esm fill-mask generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #esm #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

output\_AAVESM2\_650M\_v1
=========================


This model is a fine-tuned version of 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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #esm #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #esm #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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