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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: predict-perception-bert-blame-concept |
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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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# predict-perception-bert-blame-concept |
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This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7359 |
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- Rmse: 0.6962 |
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- Rmse Blame::a Un concetto astratto o un'emozione: 0.6962 |
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- Mae: 0.5010 |
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- Mae Blame::a Un concetto astratto o un'emozione: 0.5010 |
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- R2: 0.3974 |
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- R2 Blame::a Un concetto astratto o un'emozione: 0.3974 |
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- Cos: 0.3913 |
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- Pair: 0.0 |
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- Rank: 0.5 |
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- Neighbors: 0.5507 |
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- Rsa: nan |
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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: 1e-05 |
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- train_batch_size: 20 |
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- eval_batch_size: 8 |
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- seed: 1996 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un concetto astratto o un'emozione | Mae | Mae Blame::a Un concetto astratto o un'emozione | R2 | R2 Blame::a Un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:-------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:| |
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| 1.0979 | 1.0 | 15 | 1.2387 | 0.9033 | 0.9033 | 0.6603 | 0.6603 | -0.0144 | -0.0144 | 0.0435 | 0.0 | 0.5 | 0.3432 | nan | |
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| 1.0172 | 2.0 | 30 | 1.1498 | 0.8703 | 0.8703 | 0.5964 | 0.5964 | 0.0584 | 0.0584 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan | |
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| 0.9879 | 3.0 | 45 | 1.2139 | 0.8942 | 0.8942 | 0.6197 | 0.6197 | 0.0060 | 0.0060 | 0.2174 | 0.0 | 0.5 | 0.4582 | nan | |
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| 0.9723 | 4.0 | 60 | 1.1152 | 0.8571 | 0.8571 | 0.5982 | 0.5982 | 0.0867 | 0.0867 | 0.2174 | 0.0 | 0.5 | 0.3921 | nan | |
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| 0.9584 | 5.0 | 75 | 1.0607 | 0.8358 | 0.8358 | 0.5959 | 0.5959 | 0.1314 | 0.1314 | 0.0435 | 0.0 | 0.5 | 0.4165 | nan | |
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| 0.9023 | 6.0 | 90 | 1.0031 | 0.8128 | 0.8128 | 0.5827 | 0.5827 | 0.1786 | 0.1786 | -0.0435 | 0.0 | 0.5 | 0.3862 | nan | |
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| 0.8745 | 7.0 | 105 | 0.9715 | 0.7999 | 0.7999 | 0.5796 | 0.5796 | 0.2044 | 0.2044 | 0.3043 | 0.0 | 0.5 | 0.3665 | nan | |
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| 0.8082 | 8.0 | 120 | 0.8984 | 0.7692 | 0.7692 | 0.5699 | 0.5699 | 0.2643 | 0.2643 | 0.1304 | 0.0 | 0.5 | 0.3390 | nan | |
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| 0.7475 | 9.0 | 135 | 0.8532 | 0.7497 | 0.7497 | 0.5849 | 0.5849 | 0.3013 | 0.3013 | 0.0435 | 0.0 | 0.5 | 0.3100 | nan | |
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| 0.6599 | 10.0 | 150 | 0.8737 | 0.7586 | 0.7586 | 0.5822 | 0.5822 | 0.2846 | 0.2846 | 0.3043 | 0.0 | 0.5 | 0.3830 | nan | |
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| 0.5867 | 11.0 | 165 | 0.8159 | 0.7331 | 0.7331 | 0.5752 | 0.5752 | 0.3318 | 0.3318 | 0.2174 | 0.0 | 0.5 | 0.4439 | nan | |
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| 0.5081 | 12.0 | 180 | 0.8367 | 0.7424 | 0.7424 | 0.6071 | 0.6071 | 0.3148 | 0.3148 | 0.0435 | 0.0 | 0.5 | 0.3561 | nan | |
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| 0.4801 | 13.0 | 195 | 0.8353 | 0.7417 | 0.7417 | 0.5567 | 0.5567 | 0.3160 | 0.3160 | 0.3913 | 0.0 | 0.5 | 0.5850 | nan | |
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| 0.3714 | 14.0 | 210 | 0.8050 | 0.7282 | 0.7282 | 0.5824 | 0.5824 | 0.3408 | 0.3408 | 0.1304 | 0.0 | 0.5 | 0.3975 | nan | |
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| 0.3306 | 15.0 | 225 | 0.7833 | 0.7183 | 0.7183 | 0.5570 | 0.5570 | 0.3585 | 0.3585 | 0.2174 | 0.0 | 0.5 | 0.4604 | nan | |
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| 0.2674 | 16.0 | 240 | 0.8148 | 0.7326 | 0.7326 | 0.5475 | 0.5475 | 0.3328 | 0.3328 | 0.3043 | 0.0 | 0.5 | 0.4891 | nan | |
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| 0.2129 | 17.0 | 255 | 0.8715 | 0.7576 | 0.7576 | 0.5537 | 0.5537 | 0.2863 | 0.2863 | 0.4783 | 0.0 | 0.5 | 0.5017 | nan | |
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| 0.1924 | 18.0 | 270 | 0.7944 | 0.7234 | 0.7234 | 0.5276 | 0.5276 | 0.3495 | 0.3495 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan | |
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| 0.1984 | 19.0 | 285 | 0.7885 | 0.7207 | 0.7207 | 0.5208 | 0.5208 | 0.3543 | 0.3543 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.1623 | 20.0 | 300 | 0.7682 | 0.7113 | 0.7113 | 0.5132 | 0.5132 | 0.3709 | 0.3709 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan | |
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| 0.1409 | 21.0 | 315 | 0.7653 | 0.7100 | 0.7100 | 0.5215 | 0.5215 | 0.3733 | 0.3733 | 0.3043 | 0.0 | 0.5 | 0.5415 | nan | |
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| 0.1386 | 22.0 | 330 | 0.7688 | 0.7116 | 0.7116 | 0.5124 | 0.5124 | 0.3704 | 0.3704 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.123 | 23.0 | 345 | 0.7756 | 0.7148 | 0.7148 | 0.5144 | 0.5144 | 0.3648 | 0.3648 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.1175 | 24.0 | 360 | 0.7423 | 0.6993 | 0.6993 | 0.5015 | 0.5015 | 0.3921 | 0.3921 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.1188 | 25.0 | 375 | 0.7255 | 0.6913 | 0.6913 | 0.5063 | 0.5063 | 0.4059 | 0.4059 | 0.2174 | 0.0 | 0.5 | 0.4604 | nan | |
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| 0.1155 | 26.0 | 390 | 0.7635 | 0.7091 | 0.7091 | 0.5083 | 0.5083 | 0.3748 | 0.3748 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan | |
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| 0.0981 | 27.0 | 405 | 0.7128 | 0.6852 | 0.6852 | 0.5020 | 0.5020 | 0.4163 | 0.4163 | 0.3043 | 0.0 | 0.5 | 0.5415 | nan | |
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| 0.1109 | 28.0 | 420 | 0.7430 | 0.6996 | 0.6996 | 0.5023 | 0.5023 | 0.3915 | 0.3915 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.1081 | 29.0 | 435 | 0.7367 | 0.6966 | 0.6966 | 0.5007 | 0.5007 | 0.3967 | 0.3967 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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| 0.0953 | 30.0 | 450 | 0.7359 | 0.6962 | 0.6962 | 0.5010 | 0.5010 | 0.3974 | 0.3974 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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