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# protein-lm-gb1

This model was trained from scratch on the gb1 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9175
- Mse: 4.9175
- Rmse: 2.2175
- Mae: 1.9137
- R2: -0.0720
- Smape: 79.2041

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mse    | Rmse   | Mae    | R2      | Smape   |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:|:-------:|
| 5.202         | 1.0   | 7539  | 4.9090          | 4.9090 | 2.2156 | 1.9131 | -0.0702 | 79.1446 |
| 5.247         | 2.0   | 15078 | 4.9849          | 4.9849 | 2.2327 | 1.9185 | -0.0867 | 79.6722 |
| 5.2129        | 3.0   | 22617 | 4.9175          | 4.9175 | 2.2175 | 1.9137 | -0.0720 | 79.2041 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["gb1"], "model-index": [{"name": "protein-lm-gb1", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer dataset:gb1 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-gb1 #autotrain_compatible #endpoints_compatible #region-us 

protein-lm-gb1
==============


This model was trained from scratch on the gb1 dataset.
It achieves the following results on the evaluation set:


* Loss: 4.9175
* Mse: 4.9175
* Rmse: 2.2175
* Mae: 1.9137
* R2: -0.0720
* Smape: 79.2041


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-gb1 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-gb1 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2