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--- |
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language: |
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- pl |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cartesinus/leyzer-fedcsis |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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base_model: xlm-roberta-base |
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model-index: |
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- name: fedcsis-slot_baseline-xlm_r-pl |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# fedcsis-slot_baseline-xlm_r-pl |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the |
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[leyzer-fedcsis](https://huggingface.co/cartesinus/leyzer-fedcsis) dataset. |
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Results on test set: |
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- Precision: 0.9621 |
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- Recall: 0.9583 |
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- F1: 0.9602 |
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- Accuracy: 0.9857 |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1009 |
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- Precision: 0.9579 |
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- Recall: 0.9512 |
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- F1: 0.9546 |
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- Accuracy: 0.9860 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.1608 | 1.0 | 798 | 0.2575 | 0.8881 | 0.8916 | 0.8898 | 0.9532 | |
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| 0.1561 | 2.0 | 1596 | 0.1188 | 0.9459 | 0.9389 | 0.9424 | 0.9806 | |
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| 0.0979 | 3.0 | 2394 | 0.1060 | 0.9507 | 0.9486 | 0.9497 | 0.9838 | |
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| 0.0579 | 4.0 | 3192 | 0.0916 | 0.9573 | 0.9475 | 0.9524 | 0.9851 | |
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| 0.0507 | 5.0 | 3990 | 0.1109 | 0.9527 | 0.9506 | 0.9516 | 0.9839 | |
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| 0.0344 | 6.0 | 4788 | 0.0987 | 0.9575 | 0.9488 | 0.9531 | 0.9855 | |
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| 0.0266 | 7.0 | 5586 | 0.1010 | 0.9584 | 0.9501 | 0.9542 | 0.9854 | |
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| 0.0211 | 8.0 | 6384 | 0.1051 | 0.9575 | 0.9498 | 0.9536 | 0.9855 | |
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| 0.0168 | 9.0 | 7182 | 0.1009 | 0.9577 | 0.9516 | 0.9546 | 0.9861 | |
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| 0.016 | 10.0 | 7980 | 0.1009 | 0.9579 | 0.9512 | 0.9546 | 0.9860 | |
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### Per slot evaluation |
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| slot_name | precision | recall | f1 | tc_size | |
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|-----------|-----------|--------|----|---------| |
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| album | 0.2000 | 0.3333 | 0.2500 | 9 | |
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| all_lang | 1.0000 | 1.0000 | 1.0000 | 5 | |
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| artist | 0.9341 | 0.9444 | 0.9392 | 90 | |
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| av_alias | 0.6667 | 0.8000 | 0.7273 | 5 | |
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| caption | 0.9651 | 0.9432 | 0.9540 | 88 | |
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| category | 0.0000 | 0.0000 | 0.0000 | 1 | |
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| category_a | 1.0000 | 0.9167 | 0.9565 | 12 | |
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| category_b | 1.0000 | 1.0000 | 1.0000 | 25 | |
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| channel | 0.9492 | 0.9333 | 0.9412 | 60 | |
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| channel_id | 0.9701 | 0.9644 | 0.9673 | 337 | |
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| count | 1.0000 | 0.9167 | 0.9565 | 12 | |
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| date | 0.9764 | 0.9841 | 0.9802 | 126 | |
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| date_day | 1.0000 | 0.9500 | 0.9744 | 20 | |
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| date_month | 0.9677 | 1.0000 | 0.9836 | 30 | |
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| device_name | 0.9091 | 1.0000 | 0.9524 | 10 | |
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| email | 1.0000 | 0.9913 | 0.9956 | 115 | |
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| event_name | 0.8788 | 0.9355 | 0.9063 | 31 | |
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| file_name | 0.9778 | 0.9778 | 0.9778 | 45 | |
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| file_size | 1.0000 | 1.0000 | 1.0000 | 12 | |
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| filename | 0.9722 | 0.9589 | 0.9655 | 73 | |
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| filter | 1.0000 | 1.0000 | 1.0000 | 35 | |
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| from | 0.9811 | 0.9123 | 0.9455 | 57 | |
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| hashtag | 1.0000 | 1.0000 | 1.0000 | 28 | |
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| img_query | 0.9707 | 0.9678 | 0.9693 | 342 | |
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| label | 1.0000 | 1.0000 | 1.0000 | 5 | |
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| location | 0.9766 | 0.9728 | 0.9747 | 257 | |
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| mail | 1.0000 | 1.0000 | 1.0000 | 3 | |
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| message | 0.9250 | 0.9487 | 0.9367 | 117 | |
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| mime_type | 0.9375 | 1.0000 | 0.9677 | 15 | |
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| name | 0.9412 | 0.9796 | 0.9600 | 49 | |
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| pathname | 0.8889 | 0.8889 | 0.8889 | 18 | |
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| percent | 1.0000 | 1.0000 | 1.0000 | 3 | |
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| phone_number | 0.9774 | 0.9774 | 0.9774 | 177 | |
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| phone_type | 1.0000 | 1.0000 | 1.0000 | 21 | |
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| picture_url | 0.9846 | 0.9412 | 0.9624 | 68 | |
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| playlist | 0.9516 | 0.9672 | 0.9593 | 122 | |
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| portal | 0.9869 | 0.9869 | 0.9869 | 153 | |
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| priority | 0.7500 | 1.0000 | 0.8571 | 6 | |
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| purpose | 0.0000 | 0.0000 | 0.0000 | 5 | |
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| query | 0.9663 | 0.9690 | 0.9677 | 355 | |
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| rating | 0.9630 | 0.9286 | 0.9455 | 28 | |
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| review_count | 1.0000 | 1.0000 | 1.0000 | 20 | |
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| section | 0.9730 | 0.9730 | 0.9730 | 74 | |
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| seek_time | 1.0000 | 1.0000 | 1.0000 | 3 | |
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| sender | 1.0000 | 1.0000 | 1.0000 | 6 | |
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| sender_address | 1.0000 | 0.9444 | 0.9714 | 18 | |
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| song | 0.8824 | 0.8898 | 0.8861 | 118 | |
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| src_lang_de | 0.9880 | 0.9762 | 0.9820 | 84 | |
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| src_lang_en | 0.9455 | 0.9630 | 0.9541 | 54 | |
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| src_lang_es | 0.9853 | 0.9306 | 0.9571 | 72 | |
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| src_lang_fr | 0.9733 | 0.9733 | 0.9733 | 75 | |
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| src_lang_it | 0.9872 | 0.9506 | 0.9686 | 81 | |
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| src_lang_pl | 0.9818 | 1.0000 | 0.9908 | 54 | |
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| status | 0.8810 | 0.9487 | 0.9136 | 39 | |
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| subject | 0.9636 | 0.9725 | 0.9680 | 109 | |
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| text_de | 0.9762 | 0.9762 | 0.9762 | 84 | |
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| text_en | 0.9796 | 0.9697 | 0.9746 | 99 | |
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| text_es | 0.8734 | 0.9583 | 0.9139 | 72 | |
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| text_fr | 0.9733 | 0.9733 | 0.9733 | 75 | |
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| text_it | 0.9872 | 0.9506 | 0.9686 | 81 | |
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| text_multi | 0.0000 | 0.0000 | 0.0000 | 4 | |
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| text_pl | 0.9310 | 1.0000 | 0.9643 | 54 | |
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| time | 0.9063 | 0.8788 | 0.8923 | 33 | |
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| to | 0.9648 | 0.9648 | 0.9648 | 199 | |
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| topic | 0.0000 | 0.0000 | 0.0000 | 3 | |
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| translator | 0.9838 | 0.9838 | 0.9838 | 185 | |
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| trg_lang_de | 0.9474 | 0.9730 | 0.9600 | 37 | |
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| trg_lang_en | 1.0000 | 0.9565 | 0.9778 | 46 | |
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| trg_lang_es | 0.9792 | 0.9792 | 0.9792 | 48 | |
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| trg_lang_fr | 0.9808 | 1.0000 | 0.9903 | 51 | |
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| trg_lang_general | 0.9500 | 0.9500 | 0.9500 | 20 | |
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| trg_lang_it | 0.9825 | 0.9492 | 0.9655 | 59 | |
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| trg_lang_pl | 0.9302 | 0.9756 | 0.9524 | 41 | |
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| txt_query | 0.9375 | 0.9146 | 0.9259 | 82 | |
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| username | 0.9615 | 0.8929 | 0.9259 | 28 | |
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| value | 0.8750 | 0.8750 | 0.8750 | 8 | |
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| weight | 1.0000 | 1.0000 | 1.0000 | 3 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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## Citation |
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If you use this model, please cite the following: |
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``` |
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@inproceedings{kubis2023caiccaic, |
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author={Marek Kubis and Paweł Skórzewski and Marcin Sowański and Tomasz Ziętkiewicz}, |
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pages={1319–1324}, |
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title={Center for Artificial Intelligence Challenge on Conversational AI Correctness}, |
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booktitle={Proceedings of the 18th Conference on Computer Science and Intelligence Systems}, |
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year={2023}, |
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doi={10.15439/2023B6058}, |
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url={http://dx.doi.org/10.15439/2023B6058}, |
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volume={35}, |
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series={Annals of Computer Science and Information Systems} |
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} |
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``` |
