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# libCap_prBERTbfd_clf

This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5197
- Accuracy: 0.7457
- F1: 0.7940
- Precision: 0.7567
- Recall: 0.8352
- Auroc: 0.7268

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log        | 0.98  | 34   | 0.6393          | 0.6396   | 0.7053 | 0.6782    | 0.7345 | 0.6197 |
| No log        | 1.98  | 68   | 0.5713          | 0.6962   | 0.7499 | 0.7256    | 0.7759 | 0.6795 |
| No log        | 2.98  | 102  | 0.5652          | 0.7126   | 0.7388 | 0.7918    | 0.6924 | 0.7168 |
| No log        | 3.98  | 136  | 0.5360          | 0.7330   | 0.7896 | 0.7345    | 0.8536 | 0.7076 |
| No log        | 4.98  | 170  | 0.5223          | 0.7423   | 0.7830 | 0.7740    | 0.7921 | 0.7318 |
| No log        | 5.98  | 204  | 0.5180          | 0.7454   | 0.7882 | 0.7699    | 0.8075 | 0.7323 |
| No log        | 6.98  | 238  | 0.5179          | 0.7440   | 0.7934 | 0.7537    | 0.8376 | 0.7243 |
| No log        | 7.98  | 272  | 0.5197          | 0.7457   | 0.7940 | 0.7567    | 0.8352 | 0.7268 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "libCap_prBERTbfd_clf", "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 

libCap\_prBERTbfd\_clf
======================


This model is a fine-tuned version of Rostlab/prot\_bert\_bfd on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5197
* Accuracy: 0.7457
* F1: 0.7940
* Precision: 0.7567
* Recall: 0.8352
* Auroc: 0.7268


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: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* 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: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* 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: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1