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# tuned_cair_five_classes

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0976
- F1: 0.9767
- Roc Auc: 0.9815
- Accuracy: 0.9325

## 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: 8
- eval_batch_size: 8
- 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   | 250  | 0.1394          | 0.9480 | 0.9577  | 0.845    |
| 0.1037        | 2.0   | 500  | 0.1221          | 0.95   | 0.9577  | 0.87     |
| 0.1037        | 3.0   | 750  | 0.1107          | 0.9598 | 0.9680  | 0.8875   |
| 0.0593        | 4.0   | 1000 | 0.0965          | 0.9662 | 0.9728  | 0.905    |
| 0.0593        | 5.0   | 1250 | 0.0872          | 0.9734 | 0.9787  | 0.92     |
| 0.0352        | 6.0   | 1500 | 0.0824          | 0.9753 | 0.9802  | 0.925    |
| 0.0352        | 7.0   | 1750 | 0.0906          | 0.9701 | 0.9759  | 0.915    |
| 0.02          | 8.0   | 2000 | 0.0900          | 0.9734 | 0.9787  | 0.925    |
| 0.02          | 9.0   | 2250 | 0.0930          | 0.9727 | 0.9776  | 0.9225   |
| 0.0141        | 10.0  | 2500 | 0.0932          | 0.9734 | 0.9787  | 0.9175   |
| 0.0141        | 11.0  | 2750 | 0.0925          | 0.9760 | 0.9808  | 0.9275   |
| 0.0098        | 12.0  | 3000 | 0.0964          | 0.9741 | 0.9798  | 0.93     |
| 0.0098        | 13.0  | 3250 | 0.0964          | 0.9747 | 0.9798  | 0.9275   |
| 0.0069        | 14.0  | 3500 | 0.0981          | 0.9734 | 0.9787  | 0.925    |
| 0.0069        | 15.0  | 3750 | 0.0930          | 0.9767 | 0.9815  | 0.9325   |
| 0.0058        | 16.0  | 4000 | 0.0939          | 0.9767 | 0.9815  | 0.9325   |
| 0.0058        | 17.0  | 4250 | 0.0959          | 0.9767 | 0.9815  | 0.935    |
| 0.0048        | 18.0  | 4500 | 0.0972          | 0.9753 | 0.9799  | 0.925    |
| 0.0048        | 19.0  | 4750 | 0.0971          | 0.9767 | 0.9815  | 0.9325   |
| 0.0042        | 20.0  | 5000 | 0.0976          | 0.9767 | 0.9815  | 0.9325   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "tuned_cair_five_classes", "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 

tuned\_cair\_five\_classes
==========================


This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0976
* F1: 0.9767
* Roc Auc: 0.9815
* Accuracy: 0.9325


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: 8
* eval\_batch\_size: 8
* 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 4.25.1
* Pytorch 1.13.1+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: 8
* eval\_batch\_size: 8
* 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 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* 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: 8
* eval\_batch\_size: 8
* 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 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* 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: 8
* eval\_batch\_size: 8
* 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 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2