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# results

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7398
- F1: 0.3764
- Recall: 0.4257
- Precision: 0.5363
- Accuracy: 0.7155

## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Recall | Precision | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:--------:|
| 1.4981        | 0.03  | 10   | 1.4740          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.473         | 0.05  | 20   | 1.4299          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.4055        | 0.08  | 30   | 1.3260          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.2743        | 0.11  | 40   | 1.1795          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.2937        | 0.13  | 50   | 1.1463          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.193         | 0.16  | 60   | 1.1367          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.1709        | 0.18  | 70   | 1.0957          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.0956        | 0.21  | 80   | 1.0457          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 1.0756        | 0.24  | 90   | 0.9852          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 0.862         | 0.26  | 100  | 1.0718          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 0.9677        | 0.29  | 110  | 0.8931          | 0.1556 | 0.2    | 0.1274    | 0.6368   |
| 0.7597        | 0.32  | 120  | 0.8502          | 0.2195 | 0.2499 | 0.2034    | 0.6561   |
| 0.7797        | 0.34  | 130  | 0.8028          | 0.2818 | 0.3758 | 0.3547    | 0.6888   |
| 0.8114        | 0.37  | 140  | 0.9480          | 0.2918 | 0.3313 | 0.3515    | 0.6907   |
| 0.8693        | 0.4   | 150  | 0.7799          | 0.3703 | 0.4000 | 0.5420    | 0.7081   |
| 0.9561        | 0.42  | 160  | 0.7844          | 0.3825 | 0.4261 | 0.3493    | 0.7199   |
| 0.6979        | 0.45  | 170  | 0.7656          | 0.3882 | 0.4404 | 0.4166    | 0.7165   |
| 0.8083        | 0.47  | 180  | 0.8847          | 0.3596 | 0.3688 | 0.5985    | 0.7051   |
| 0.8009        | 0.5   | 190  | 0.7665          | 0.3244 | 0.3916 | 0.3801    | 0.7021   |
| 0.6833        | 0.53  | 200  | 0.8408          | 0.4270 | 0.4631 | 0.4603    | 0.6724   |
| 0.749         | 0.55  | 210  | 0.7344          | 0.3889 | 0.4745 | 0.4888    | 0.7120   |
| 0.7106        | 0.58  | 220  | 0.7037          | 0.4511 | 0.4965 | 0.4562    | 0.7343   |
| 0.7631        | 0.61  | 230  | 0.7118          | 0.4331 | 0.4626 | 0.4472    | 0.7378   |
| 0.7672        | 0.63  | 240  | 0.6925          | 0.5035 | 0.4976 | 0.5470    | 0.7412   |
| 0.7662        | 0.66  | 250  | 0.7188          | 0.4425 | 0.4812 | 0.4662    | 0.7308   |
| 0.726         | 0.69  | 260  | 0.7120          | 0.4052 | 0.4616 | 0.4508    | 0.7353   |
| 0.9073        | 0.71  | 270  | 0.7969          | 0.3495 | 0.3862 | 0.3547    | 0.7036   |
| 0.6709        | 0.74  | 280  | 0.7429          | 0.3800 | 0.4279 | 0.4814    | 0.7145   |
| 0.9403        | 0.77  | 290  | 0.8199          | 0.3926 | 0.3870 | 0.5164    | 0.6992   |
| 0.9277        | 0.79  | 300  | 0.7304          | 0.3599 | 0.4349 | 0.3786    | 0.7086   |
| 0.9503        | 0.82  | 310  | 0.7764          | 0.4613 | 0.5283 | 0.4263    | 0.6828   |
| 0.6553        | 0.84  | 320  | 0.7386          | 0.4051 | 0.4593 | 0.5329    | 0.7081   |
| 0.7655        | 0.87  | 330  | 0.7527          | 0.5087 | 0.5252 | 0.5375    | 0.7204   |
| 0.6663        | 0.9   | 340  | 0.7248          | 0.4618 | 0.4841 | 0.5283    | 0.7353   |
| 0.915         | 0.92  | 350  | 0.8947          | 0.4279 | 0.4875 | 0.3979    | 0.6596   |
| 0.8718        | 0.95  | 360  | 0.7893          | 0.3796 | 0.3938 | 0.5016    | 0.7110   |
| 0.8338        | 0.98  | 370  | 0.7293          | 0.4124 | 0.4277 | 0.4147    | 0.7130   |


### Framework versions

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

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

results
=======


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


* Loss: 0.7398
* F1: 0.3764
* Recall: 0.4257
* Precision: 0.5363
* Accuracy: 0.7155


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: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1


### 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: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1### Training results### Framework versions


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