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# xlm-roberta-base-finetuned-ours-DS

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9899
- Accuracy: 0.725
- Precision: 0.6875
- Recall: 0.6723
- F1: 0.6779

## 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: 1.6820964947491663e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9962        | 1.99  | 199  | 0.8025          | 0.59     | 0.6055    | 0.5507 | 0.4746 |
| 0.6724        | 3.98  | 398  | 0.8205          | 0.705    | 0.6678    | 0.6520 | 0.6492 |
| 0.4259        | 5.97  | 597  | 0.9899          | 0.725    | 0.6875    | 0.6723 | 0.6779 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1

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

xlm-roberta-base-finetuned-ours-DS
==================================


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


* Loss: 0.9899
* Accuracy: 0.725
* Precision: 0.6875
* Recall: 0.6723
* F1: 0.6779


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: 1.6820964947491663e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.6820964947491663e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #xlm-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: 1.6820964947491663e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #xlm-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: 1.6820964947491663e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 6### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1