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# roberta-base-finetuned-cv

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: 0.1731
- Precision: 0.7668
- Recall: 0.8235
- F1: 0.7941
- Accuracy: 0.9699

## 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: 5e-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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 17   | 0.4505          | 0.0       | 0.0    | 0.0    | 0.8958   |
| No log        | 2.0   | 34   | 0.4233          | 0.1936    | 0.3653 | 0.2530 | 0.8852   |
| No log        | 3.0   | 51   | 0.2362          | 0.4170    | 0.5294 | 0.4666 | 0.9394   |
| No log        | 4.0   | 68   | 0.1773          | 0.5681    | 0.6850 | 0.6211 | 0.9552   |
| No log        | 5.0   | 85   | 0.1570          | 0.7360    | 0.7486 | 0.7422 | 0.9635   |
| No log        | 6.0   | 102  | 0.1592          | 0.7308    | 0.7495 | 0.7400 | 0.9620   |
| No log        | 7.0   | 119  | 0.1560          | 0.6909    | 0.8378 | 0.7573 | 0.9637   |
| No log        | 8.0   | 136  | 0.1603          | 0.7520    | 0.7913 | 0.7712 | 0.9665   |
| No log        | 9.0   | 153  | 0.1453          | 0.7306    | 0.8207 | 0.7730 | 0.9654   |
| No log        | 10.0  | 170  | 0.1509          | 0.7469    | 0.8539 | 0.7968 | 0.9667   |
| No log        | 11.0  | 187  | 0.1541          | 0.7594    | 0.8235 | 0.7902 | 0.9686   |
| No log        | 12.0  | 204  | 0.1514          | 0.7585    | 0.8283 | 0.7918 | 0.9682   |
| No log        | 13.0  | 221  | 0.1583          | 0.7823    | 0.8216 | 0.8015 | 0.9690   |
| No log        | 14.0  | 238  | 0.1571          | 0.7519    | 0.8397 | 0.7934 | 0.9686   |
| No log        | 15.0  | 255  | 0.1590          | 0.7851    | 0.8283 | 0.8061 | 0.9702   |
| No log        | 16.0  | 272  | 0.1594          | 0.7598    | 0.8491 | 0.8020 | 0.9697   |
| No log        | 17.0  | 289  | 0.1757          | 0.7799    | 0.8169 | 0.7980 | 0.9698   |
| No log        | 18.0  | 306  | 0.1687          | 0.7768    | 0.8188 | 0.7972 | 0.9696   |
| No log        | 19.0  | 323  | 0.1706          | 0.7653    | 0.8321 | 0.7973 | 0.9700   |
| No log        | 20.0  | 340  | 0.1731          | 0.7668    | 0.8235 | 0.7941 | 0.9699   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

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

roberta-base-finetuned-cv
=========================


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


* Loss: 0.1731
* Precision: 0.7668
* Recall: 0.8235
* F1: 0.7941
* Accuracy: 0.9699


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: 5e-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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2