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# Classfication_BERT

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0165
- Accuracy: 0.55

## 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: 16
- eval_batch_size: 16
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 10   | 0.9250          | 0.6316   |
| No log        | 2.0   | 20   | 0.7696          | 0.8421   |
| No log        | 3.0   | 30   | 0.7026          | 0.8421   |
| No log        | 4.0   | 40   | 0.6086          | 0.8421   |
| No log        | 5.0   | 50   | 0.6338          | 0.7368   |
| No log        | 6.0   | 60   | 0.6311          | 0.6842   |
| No log        | 7.0   | 70   | 0.6727          | 0.6842   |
| No log        | 8.0   | 80   | 0.5988          | 0.6842   |
| No log        | 9.0   | 90   | 0.7095          | 0.6842   |
| No log        | 10.0  | 100  | 0.6706          | 0.6842   |
| No log        | 11.0  | 110  | 0.8180          | 0.6842   |
| No log        | 12.0  | 120  | 0.9376          | 0.6842   |
| No log        | 13.0  | 130  | 0.8223          | 0.6842   |
| No log        | 14.0  | 140  | 0.8983          | 0.6842   |
| No log        | 15.0  | 150  | 0.9612          | 0.6842   |
| No log        | 16.0  | 160  | 0.8906          | 0.6842   |
| No log        | 17.0  | 170  | 1.0335          | 0.6842   |
| No log        | 18.0  | 180  | 1.0222          | 0.6842   |
| No log        | 19.0  | 190  | 1.0010          | 0.6842   |
| No log        | 20.0  | 200  | 1.0184          | 0.6842   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

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

Classfication\_BERT
===================


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


* Loss: 1.0165
* Accuracy: 0.55


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: 16
* eval\_batch\_size: 16
* 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 4.26.1
* 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: 16
* eval\_batch\_size: 16
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 16
* eval\_batch\_size: 16
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2