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
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 |
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 |
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}
}
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FacebookAI/xlm-roberta-base