
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlm-roberta-base-Confusion-mlm-20230603

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:
- Accuracy: 0.8799
- Loss: 0.4204

## 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: 8
- eval_batch_size: 8
- 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.6139        | 1.0   | 130  | 0.9435   | 0.2902          |
| 0.6053        | 2.0   | 260  | 0.925    | 0.3066          |
| 0.6449        | 3.0   | 390  | 0.8884   | 0.4615          |
| 0.694         | 4.0   | 520  | 0.9058   | 0.3164          |
| 0.6768        | 5.0   | 650  | 0.9173   | 0.3421          |
| 0.6429        | 6.0   | 780  | 0.8968   | 0.3863          |
| 0.5945        | 7.0   | 910  | 0.8920   | 0.4843          |
| 0.632         | 8.0   | 1040 | 0.8984   | 0.3297          |
| 0.6015        | 9.0   | 1170 | 0.9219   | 0.3380          |
| 0.5431        | 10.0  | 1300 | 0.8967   | 0.4618          |
| 0.4813        | 11.0  | 1430 | 0.8885   | 0.4543          |
| 0.5598        | 12.0  | 1560 | 0.8885   | 0.4255          |
| 0.557         | 13.0  | 1690 | 0.8861   | 0.4283          |
| 0.4345        | 14.0  | 1820 | 0.8777   | 0.4792          |
| 0.5099        | 15.0  | 1950 | 0.9402   | 0.2961          |
| 0.5013        | 16.0  | 2080 | 0.8755   | 0.5784          |
| 0.5079        | 17.0  | 2210 | 0.9288   | 0.3294          |
| 0.557         | 18.0  | 2340 | 0.9167   | 0.3471          |
| 0.4574        | 19.0  | 2470 | 0.8969   | 0.4371          |
| 0.3938        | 20.0  | 2600 | 0.8799   | 0.4204          |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-base-Confusion-mlm-20230603", "results": []}]}
transformers pytorch tensorboard xlm-roberta fill-mask generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-Confusion-mlm-20230603
=======================================


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


* Accuracy: 0.8799
* Loss: 0.4204


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: 8
* eval\_batch\_size: 8
* 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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* 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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #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: 8
* eval\_batch\_size: 8
* 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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3