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# distilbert-base-uncased-PINA-dfnew-tuning

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3403
- Accuracy: 0.9438
- Precision: 0.8528
- Recall: 0.8454

## 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: 0.0002
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 0.8484        | 1.0   | 1436  | 0.4963          | 0.8896   | 0.7918    | 0.7575 |
| 0.3783        | 2.0   | 2872  | 0.4298          | 0.9114   | 0.8288    | 0.7918 |
| 0.2649        | 3.0   | 4308  | 0.3808          | 0.9302   | 0.8484    | 0.8148 |
| 0.1951        | 4.0   | 5744  | 0.3627          | 0.9363   | 0.8631    | 0.8205 |
| 0.149         | 5.0   | 7180  | 0.3403          | 0.9438   | 0.8528    | 0.8454 |
| 0.1061        | 6.0   | 8616  | 0.3415          | 0.9455   | 0.8571    | 0.8366 |
| 0.0745        | 7.0   | 10052 | 0.3441          | 0.9467   | 0.8554    | 0.8418 |
| 0.0452        | 8.0   | 11488 | 0.3850          | 0.9500   | 0.8697    | 0.8711 |
| 0.0273        | 9.0   | 12924 | 0.3941          | 0.9506   | 0.8546    | 0.8469 |
| 0.0166        | 10.0  | 14360 | 0.4046          | 0.9525   | 0.8621    | 0.8492 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "distilbert-base-uncased-PINA-dfnew-tuning", "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 

distilbert-base-uncased-PINA-dfnew-tuning
=========================================


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


* Loss: 0.3403
* Accuracy: 0.9438
* Precision: 0.8528
* Recall: 0.8454


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: 0.0002
* 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: 10


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* 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: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 0.0002
* 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: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3