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# fedcsis-slot_baseline-xlm_r-leyzer_en

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.
It achieves the following results on the evaluation set:
- Loss: 0.1017
- Precision: 0.9735
- Recall: 0.9722
- F1: 0.9729
- Accuracy: 0.9852

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2565        | 1.0   | 814  | 0.2709          | 0.8433    | 0.8717 | 0.8573 | 0.9313   |
| 0.183         | 2.0   | 1628 | 0.1226          | 0.9413    | 0.9568 | 0.9490 | 0.9758   |
| 0.0961        | 3.0   | 2442 | 0.1082          | 0.9561    | 0.9612 | 0.9586 | 0.9798   |
| 0.0528        | 4.0   | 3256 | 0.0795          | 0.9678    | 0.9690 | 0.9684 | 0.9855   |
| 0.0334        | 5.0   | 4070 | 0.0742          | 0.9720    | 0.9709 | 0.9715 | 0.9855   |
| 0.027         | 6.0   | 4884 | 0.0960          | 0.9705    | 0.9714 | 0.9710 | 0.9838   |
| 0.0234        | 7.0   | 5698 | 0.0910          | 0.9730    | 0.9736 | 0.9733 | 0.9861   |
| 0.0111        | 8.0   | 6512 | 0.0871          | 0.9732    | 0.9728 | 0.9730 | 0.9871   |
| 0.0067        | 9.0   | 7326 | 0.1016          | 0.9714    | 0.9716 | 0.9715 | 0.9861   |
| 0.0067        | 10.0  | 8140 | 0.1017          | 0.9735    | 0.9722 | 0.9729 | 0.9852   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- 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": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "fedcsis-slot_baseline-xlm_r-leyzer_en", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

fedcsis-slot\_baseline-xlm\_r-leyzer\_en
========================================


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


* Loss: 0.1017
* Precision: 0.9735
* Recall: 0.9722
* F1: 0.9729
* Accuracy: 0.9852


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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #xlm-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: 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #xlm-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: 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2


If you use this model, please cite the following: