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# bert-finetuned-sla

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3274
- F1: 0.6555
- Roc Auc: 0.7660
- Accuracy: 0.5294

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 30   | 0.4994          | 0.0    | 0.5     | 0.0      |
| No log        | 2.0   | 60   | 0.4408          | 0.0    | 0.5     | 0.0      |
| No log        | 3.0   | 90   | 0.3761          | 0.4444 | 0.6462  | 0.1961   |
| No log        | 4.0   | 120  | 0.3438          | 0.6496 | 0.7604  | 0.4706   |
| No log        | 5.0   | 150  | 0.3274          | 0.6555 | 0.7660  | 0.5294   |
| No log        | 6.0   | 180  | 0.3093          | 0.6557 | 0.7699  | 0.4706   |
| No log        | 7.0   | 210  | 0.3083          | 0.6560 | 0.7738  | 0.5098   |
| No log        | 8.0   | 240  | 0.3030          | 0.6457 | 0.7703  | 0.4706   |
| No log        | 9.0   | 270  | 0.3096          | 0.6667 | 0.7811  | 0.4902   |
| No log        | 10.0  | 300  | 0.2976          | 0.6718 | 0.7907  | 0.5098   |
| No log        | 11.0  | 330  | 0.2986          | 0.6769 | 0.7924  | 0.5294   |
| No log        | 12.0  | 360  | 0.3046          | 0.6562 | 0.7777  | 0.5098   |
| No log        | 13.0  | 390  | 0.2988          | 0.6870 | 0.7997  | 0.4902   |
| No log        | 14.0  | 420  | 0.3026          | 0.6769 | 0.7924  | 0.5098   |
| No log        | 15.0  | 450  | 0.3005          | 0.6870 | 0.7997  | 0.5098   |
| No log        | 16.0  | 480  | 0.3012          | 0.6822 | 0.7941  | 0.5098   |
| 0.2216        | 17.0  | 510  | 0.3013          | 0.6977 | 0.8032  | 0.5294   |
| 0.2216        | 18.0  | 540  | 0.3033          | 0.6977 | 0.8032  | 0.5294   |
| 0.2216        | 19.0  | 570  | 0.3024          | 0.6977 | 0.8032  | 0.5294   |
| 0.2216        | 20.0  | 600  | 0.3027          | 0.6923 | 0.8015  | 0.5098   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-sla", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-sla
==================


This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3274
* F1: 0.6555
* Roc Auc: 0.7660
* Accuracy: 0.5294


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2