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# fedcsis-slot_baseline-xlm_r-pl

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

Results on test set:
- Precision: 0.9621
- Recall: 0.9583
- F1: 0.9602
- Accuracy: 0.9857

It achieves the following results on the evaluation set:
- Loss: 0.1009
- Precision: 0.9579
- Recall: 0.9512
- F1: 0.9546
- Accuracy: 0.9860

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1608        | 1.0   | 798  | 0.2575          | 0.8881    | 0.8916 | 0.8898 | 0.9532   |
| 0.1561        | 2.0   | 1596 | 0.1188          | 0.9459    | 0.9389 | 0.9424 | 0.9806   |
| 0.0979        | 3.0   | 2394 | 0.1060          | 0.9507    | 0.9486 | 0.9497 | 0.9838   |
| 0.0579        | 4.0   | 3192 | 0.0916          | 0.9573    | 0.9475 | 0.9524 | 0.9851   |
| 0.0507        | 5.0   | 3990 | 0.1109          | 0.9527    | 0.9506 | 0.9516 | 0.9839   |
| 0.0344        | 6.0   | 4788 | 0.0987          | 0.9575    | 0.9488 | 0.9531 | 0.9855   |
| 0.0266        | 7.0   | 5586 | 0.1010          | 0.9584    | 0.9501 | 0.9542 | 0.9854   |
| 0.0211        | 8.0   | 6384 | 0.1051          | 0.9575    | 0.9498 | 0.9536 | 0.9855   |
| 0.0168        | 9.0   | 7182 | 0.1009          | 0.9577    | 0.9516 | 0.9546 | 0.9861   |
| 0.016         | 10.0  | 7980 | 0.1009          | 0.9579    | 0.9512 | 0.9546 | 0.9860   |

### Per slot evaluation

| slot_name | precision | recall | f1 | tc_size |
|-----------|-----------|--------|----|---------|
| album | 0.2000 | 0.3333 | 0.2500 | 9 |
| all_lang | 1.0000 | 1.0000 | 1.0000 | 5 |
| artist | 0.9341 | 0.9444 | 0.9392 | 90 |
| av_alias | 0.6667 | 0.8000 | 0.7273 | 5 |
| caption | 0.9651 | 0.9432 | 0.9540 | 88 |
| category | 0.0000 | 0.0000 | 0.0000 | 1 |
| category_a | 1.0000 | 0.9167 | 0.9565 | 12 |
| category_b | 1.0000 | 1.0000 | 1.0000 | 25 |
| channel | 0.9492 | 0.9333 | 0.9412 | 60 |
| channel_id | 0.9701 | 0.9644 | 0.9673 | 337 |
| count | 1.0000 | 0.9167 | 0.9565 | 12 |
| date | 0.9764 | 0.9841 | 0.9802 | 126 |
| date_day | 1.0000 | 0.9500 | 0.9744 | 20 |
| date_month | 0.9677 | 1.0000 | 0.9836 | 30 |
| device_name | 0.9091 | 1.0000 | 0.9524 | 10 |
| email | 1.0000 | 0.9913 | 0.9956 | 115 |
| event_name | 0.8788 | 0.9355 | 0.9063 | 31 |
| file_name | 0.9778 | 0.9778 | 0.9778 | 45 |
| file_size | 1.0000 | 1.0000 | 1.0000 | 12 |
| filename | 0.9722 | 0.9589 | 0.9655 | 73 |
| filter | 1.0000 | 1.0000 | 1.0000 | 35 |
| from | 0.9811 | 0.9123 | 0.9455 | 57 |
| hashtag | 1.0000 | 1.0000 | 1.0000 | 28 |
| img_query | 0.9707 | 0.9678 | 0.9693 | 342 |
| label | 1.0000 | 1.0000 | 1.0000 | 5 |
| location | 0.9766 | 0.9728 | 0.9747 | 257 |
| mail | 1.0000 | 1.0000 | 1.0000 | 3 |
| message | 0.9250 | 0.9487 | 0.9367 | 117 |
| mime_type | 0.9375 | 1.0000 | 0.9677 | 15 |
| name | 0.9412 | 0.9796 | 0.9600 | 49 |
| pathname | 0.8889 | 0.8889 | 0.8889 | 18 |
| percent | 1.0000 | 1.0000 | 1.0000 | 3 |
| phone_number | 0.9774 | 0.9774 | 0.9774 | 177 |
| phone_type | 1.0000 | 1.0000 | 1.0000 | 21 |
| picture_url | 0.9846 | 0.9412 | 0.9624 | 68 |
| playlist | 0.9516 | 0.9672 | 0.9593 | 122 |
| portal | 0.9869 | 0.9869 | 0.9869 | 153 |
| priority | 0.7500 | 1.0000 | 0.8571 | 6 |
| purpose | 0.0000 | 0.0000 | 0.0000 | 5 |
| query | 0.9663 | 0.9690 | 0.9677 | 355 |
| rating | 0.9630 | 0.9286 | 0.9455 | 28 |
| review_count | 1.0000 | 1.0000 | 1.0000 | 20 |
| section | 0.9730 | 0.9730 | 0.9730 | 74 |
| seek_time | 1.0000 | 1.0000 | 1.0000 | 3 |
| sender | 1.0000 | 1.0000 | 1.0000 | 6 |
| sender_address | 1.0000 | 0.9444 | 0.9714 | 18 |
| song | 0.8824 | 0.8898 | 0.8861 | 118 |
| src_lang_de | 0.9880 | 0.9762 | 0.9820 | 84 |
| src_lang_en | 0.9455 | 0.9630 | 0.9541 | 54 |
| src_lang_es | 0.9853 | 0.9306 | 0.9571 | 72 |
| src_lang_fr | 0.9733 | 0.9733 | 0.9733 | 75 |
| src_lang_it | 0.9872 | 0.9506 | 0.9686 | 81 |
| src_lang_pl | 0.9818 | 1.0000 | 0.9908 | 54 |
| status | 0.8810 | 0.9487 | 0.9136 | 39 |
| subject | 0.9636 | 0.9725 | 0.9680 | 109 |
| text_de | 0.9762 | 0.9762 | 0.9762 | 84 |
| text_en | 0.9796 | 0.9697 | 0.9746 | 99 |
| text_es | 0.8734 | 0.9583 | 0.9139 | 72 |
| text_fr | 0.9733 | 0.9733 | 0.9733 | 75 |
| text_it | 0.9872 | 0.9506 | 0.9686 | 81 |
| text_multi | 0.0000 | 0.0000 | 0.0000 | 4 |
| text_pl | 0.9310 | 1.0000 | 0.9643 | 54 |
| time | 0.9063 | 0.8788 | 0.8923 | 33 |
| to | 0.9648 | 0.9648 | 0.9648 | 199 |
| topic | 0.0000 | 0.0000 | 0.0000 | 3 |
| translator | 0.9838 | 0.9838 | 0.9838 | 185 |
| trg_lang_de | 0.9474 | 0.9730 | 0.9600 | 37 |
| trg_lang_en | 1.0000 | 0.9565 | 0.9778 | 46 |
| trg_lang_es | 0.9792 | 0.9792 | 0.9792 | 48 |
| trg_lang_fr | 0.9808 | 1.0000 | 0.9903 | 51 |
| trg_lang_general | 0.9500 | 0.9500 | 0.9500 | 20 |
| trg_lang_it | 0.9825 | 0.9492 | 0.9655 | 59 |
| trg_lang_pl | 0.9302 | 0.9756 | 0.9524 | 41 |
| txt_query | 0.9375 | 0.9146 | 0.9259 | 82 |
| username | 0.9615 | 0.8929 | 0.9259 | 28 |
| value | 0.8750 | 0.8750 | 0.8750 | 8 |
| weight | 1.0000 | 1.0000 | 1.0000 | 3 |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- 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}
}

```
{"language": ["pl"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["cartesinus/leyzer-fedcsis"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "fedcsis-slot_baseline-xlm_r-pl", "results": []}]}
transformers pytorch tensorboard safetensors xlm-roberta token-classification generated_from_trainer pl dataset:cartesinus/leyzer-fedcsis base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #pl #dataset-cartesinus/leyzer-fedcsis #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us 

fedcsis-slot\_baseline-xlm\_r-pl
================================


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


Results on test set:


* Precision: 0.9621
* Recall: 0.9583
* F1: 0.9602
* Accuracy: 0.9857


It achieves the following results on the evaluation set:


* Loss: 0.1009
* Precision: 0.9579
* Recall: 0.9512
* F1: 0.9546
* Accuracy: 0.9860


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



### Per slot evaluation



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* 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 ### Per slot evaluation ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #pl #dataset-cartesinus/leyzer-fedcsis #base_model-xlm-roberta-base #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 ### Per slot evaluation ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2


If you use this model, please cite the following:
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #pl #dataset-cartesinus/leyzer-fedcsis #base_model-xlm-roberta-base #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### Per slot evaluation### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2


If you use this model, please cite the following: