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# unsupervised-fine-tune-roberta-exist

This model is a fine-tuned version of [nouman-10/unsupervised-exist-rb](https://huggingface.co/nouman-10/unsupervised-exist-rb) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9911
- Accuracy: 0.7238
- F1: 0.7262

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 194  | 0.5147          | 0.7471   | 0.7434 |
| No log        | 2.0   | 388  | 0.5395          | 0.7384   | 0.7458 |
| 0.4616        | 3.0   | 582  | 0.6484          | 0.75     | 0.7440 |
| 0.4616        | 4.0   | 776  | 0.9610          | 0.7355   | 0.7407 |
| 0.4616        | 5.0   | 970  | 1.2414          | 0.7326   | 0.7262 |
| 0.1786        | 6.0   | 1164 | 1.7050          | 0.7209   | 0.7209 |
| 0.1786        | 7.0   | 1358 | 1.7930          | 0.7384   | 0.7273 |
| 0.0557        | 8.0   | 1552 | 1.8999          | 0.7355   | 0.7378 |
| 0.0557        | 9.0   | 1746 | 1.9886          | 0.7209   | 0.7225 |
| 0.0557        | 10.0  | 1940 | 1.9911          | 0.7238   | 0.7262 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "unsupervised-fine-tune-roberta-exist", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

unsupervised-fine-tune-roberta-exist
====================================


This model is a fine-tuned version of nouman-10/unsupervised-exist-rb on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.9911
* Accuracy: 0.7238
* F1: 0.7262


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: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #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: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2