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# fedcsis-intent_baseline-xlm_r-es

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the 
[leyzer-fedcsis](https://huggingface.co/datasets/cartesinus/leyzer-fedcsis) dataset.

Test set results:
- Accuracy: **0.970738**

It achieves the following results on the evaluation set:
- Loss: 0.1440
- Accuracy: **0.9749**

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.6266        | 1.0   | 941  | 0.9415          | 0.8074   | 0.8074 |
| 0.7228        | 2.0   | 1882 | 0.4892          | 0.8999   | 0.8999 |
| 0.3612        | 3.0   | 2823 | 0.3110          | 0.9346   | 0.9346 |
| 0.2236        | 4.0   | 3764 | 0.2433          | 0.9518   | 0.9518 |
| 0.1464        | 5.0   | 4705 | 0.1963          | 0.9594   | 0.9594 |
| 0.1056        | 6.0   | 5646 | 0.1698          | 0.9667   | 0.9667 |
| 0.0725        | 7.0   | 6587 | 0.1574          | 0.9693   | 0.9693 |
| 0.0602        | 8.0   | 7528 | 0.1476          | 0.9729   | 0.9729 |
| 0.0619        | 9.0   | 8469 | 0.1474          | 0.9743   | 0.9743 |
| 0.052         | 10.0  | 9410 | 0.1440          | 0.9749   | 0.9749 |


### Framework versions

- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

## Citation

If you use this model, please cite the following:
```
@inproceedings{kubis2023caiccaic,
	author={Marek Kubis and Paweł Skórzewski and Marcin Sowański and Tomasz Ziętkiewicz},
	pages={1319–1324},
	title={Center for Artificial Intelligence Challenge on Conversational AI Correctness},
	booktitle={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
	year={2023},
	doi={10.15439/2023B6058},
	url={http://dx.doi.org/10.15439/2023B6058},
	volume={35},
	series={Annals of Computer Science and Information Systems}
}

```
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fedcsis-intent_baseline-xlm_r-es", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

fedcsis-intent\_baseline-xlm\_r-es
==================================


This model is a fine-tuned version of xlm-roberta-base on the
leyzer-fedcsis dataset.


Test set results:


* Accuracy: 0.970738


It achieves the following results on the evaluation set:


* Loss: 0.1440
* Accuracy: 0.9749


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: 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: 10


### Training results



### Framework versions


* Transformers 4.27.0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2


If you use this model, please cite the following:



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.27.0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #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: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.27.0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #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: 2e-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: 10### Training results### Framework versions


* Transformers 4.27.0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2


If you use this model, please cite the following: