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# protBERTbfd_AAV2_classification

This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on AAV2 dataset with ~230k sequences (Bryant et al 2020). 

The WT sequence (aa561-588): D E E E I R T T N P V A T E Q Y G S V S T N L Q R G N R
Maximum length: 50

It achieves the following results on the evaluation set. Note:this is result of the last epoch, I think the pushed model is loaded with best checkpoint - best val_loss, I'm not so sure though :/
- Loss: 0.1341
- Accuracy: 0.9615
- F1: 0.9627
- Precision: 0.9637
- Recall: 0.9618
- Auroc: 0.9615

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log        | 1.0   | 116  | 0.2582          | 0.9064   | 0.9157 | 0.8564    | 0.9839 | 0.9038 |
| No log        | 2.0   | 232  | 0.1447          | 0.9424   | 0.9432 | 0.9618    | 0.9252 | 0.9430 |
| No log        | 3.0   | 348  | 0.1182          | 0.9542   | 0.9556 | 0.9573    | 0.9539 | 0.9542 |
| No log        | 4.0   | 464  | 0.1129          | 0.9585   | 0.9602 | 0.9520    | 0.9685 | 0.9581 |
| 0.2162        | 5.0   | 580  | 0.1278          | 0.9553   | 0.9558 | 0.9776    | 0.9351 | 0.9561 |
| 0.2162        | 6.0   | 696  | 0.1139          | 0.9587   | 0.9607 | 0.9465    | 0.9752 | 0.9581 |
| 0.2162        | 7.0   | 812  | 0.1127          | 0.9620   | 0.9633 | 0.9614    | 0.9652 | 0.9619 |
| 0.2162        | 8.0   | 928  | 0.1341          | 0.9615   | 0.9627 | 0.9637    | 0.9618 | 0.9615 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "protBERTbfd_AAV2_classification", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

protBERTbfd\_AAV2\_classification
=================================


This model is a fine-tuned version of Rostlab/prot\_bert\_bfd on AAV2 dataset with ~230k sequences (Bryant et al 2020).


The WT sequence (aa561-588): D E E E I R T T N P V A T E Q Y G S V S T N L Q R G N R
Maximum length: 50


It achieves the following results on the evaluation set. Note:this is result of the last epoch, I think the pushed model is loaded with best checkpoint - best val\_loss, I'm not so sure though :/


* Loss: 0.1341
* Accuracy: 0.9615
* F1: 0.9627
* Precision: 0.9637
* Recall: 0.9618
* Auroc: 0.9615


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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1