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# xlm-roberta-base-Confusion-mlm-20230607

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: 0.8736
- Loss: 0.5270

## 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 |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log        | 1.0   | 130  | 0.8677   | 0.6115          |
| No log        | 2.0   | 260  | 0.9247   | 0.2752          |
| No log        | 3.0   | 390  | 0.8571   | 0.6575          |
| 0.8615        | 4.0   | 520  | 0.8643   | 0.5735          |
| 0.8615        | 5.0   | 650  | 0.8911   | 0.3851          |
| 0.8615        | 6.0   | 780  | 0.8134   | 0.7165          |
| 0.8615        | 7.0   | 910  | 0.8413   | 0.6240          |
| 0.8129        | 8.0   | 1040 | 0.8861   | 0.4053          |
| 0.8129        | 9.0   | 1170 | 0.8606   | 0.5256          |
| 0.8129        | 10.0  | 1300 | 0.8776   | 0.5630          |
| 0.8129        | 11.0  | 1430 | 0.8784   | 0.5410          |
| 0.7179        | 12.0  | 1560 | 0.8807   | 0.5745          |
| 0.7179        | 13.0  | 1690 | 0.8889   | 0.4201          |
| 0.7179        | 14.0  | 1820 | 0.8785   | 0.4649          |
| 0.7179        | 15.0  | 1950 | 0.8859   | 0.4714          |
| 0.6857        | 16.0  | 2080 | 0.8453   | 0.5769          |
| 0.6857        | 17.0  | 2210 | 0.8407   | 0.5363          |
| 0.6857        | 18.0  | 2340 | 0.8724   | 0.5814          |
| 0.6857        | 19.0  | 2470 | 0.9098   | 0.3953          |
| 0.6107        | 20.0  | 2600 | 0.8736   | 0.5270          |


### 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-20230607", "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-20230607
=======================================


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: 0.8736
* Loss: 0.5270


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