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# ESM1b_AAV2_classification

To load tokenizer from ESM, you need to install transformers with this version as follow:

!git clone -b add_esm-proper --single-branch https://github.com/liujas000/transformers.git 
!pip -q install ./transformers

This model is a fine-tuned version of [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) 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: 0.2250
- Accuracy: 0.9620
- F1: 0.9632
- Precision: 0.9642
- Recall: 0.9622
- Auroc: 0.9620

## 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: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- 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   | 232  | 0.1311          | 0.9495   | 0.9501 | 0.9711    | 0.9299 | 0.9502 |
| No log        | 2.0   | 464  | 0.1032          | 0.9606   | 0.9620 | 0.9583    | 0.9657 | 0.9604 |
| 0.1924        | 3.0   | 696  | 0.0995          | 0.9627   | 0.9641 | 0.9584    | 0.9700 | 0.9625 |
| 0.1924        | 4.0   | 928  | 0.1218          | 0.9611   | 0.9624 | 0.9607    | 0.9641 | 0.9610 |
| 0.067         | 5.0   | 1160 | 0.1187          | 0.9622   | 0.9633 | 0.9678    | 0.9588 | 0.9623 |
| 0.067         | 6.0   | 1392 | 0.1514          | 0.9612   | 0.9621 | 0.9710    | 0.9534 | 0.9615 |
| 0.0271        | 7.0   | 1624 | 0.1890          | 0.9612   | 0.9626 | 0.9580    | 0.9673 | 0.9610 |
| 0.0271        | 8.0   | 1856 | 0.2250          | 0.9620   | 0.9632 | 0.9642    | 0.9622 | 0.9620 |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "ESM1b_AAV2_classification", "results": []}]}
transformers pytorch tensorboard esm text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #esm #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

ESM1b\_AAV2\_classification
===========================


To load tokenizer from ESM, you need to install transformers with this version as follow:


!git clone -b add\_esm-proper --single-branch URL
!pip -q install ./transformers


This model is a fine-tuned version of facebook/esm-1b 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: 0.2250
* Accuracy: 0.9620
* F1: 0.9632
* Precision: 0.9642
* Recall: 0.9622
* Auroc: 0.9620


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: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 1024
* 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 4.13.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 1024
* 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 4.13.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #esm #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: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 1024
* 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 4.13.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #esm #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: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 1024
* 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 4.13.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3