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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-cause-object |
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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-cause-object |
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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: 0.4120 |
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- Rmse: 1.0345 |
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- Rmse Cause::a Causata da un oggetto (es. una pistola): 1.0345 |
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- Mae: 0.6181 |
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- Mae Cause::a Causata da un oggetto (es. una pistola): 0.6181 |
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- R2: 0.3837 |
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- R2 Cause::a Causata da un oggetto (es. una pistola): 0.3837 |
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- Cos: 0.9130 |
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- Pair: 0.0 |
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- Rank: 0.5 |
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- Neighbors: 0.8986 |
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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 Cause::a Causata da un oggetto (es. una pistola) | Mae | Mae Cause::a Causata da un oggetto (es. una pistola) | R2 | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos | Pair | Rank | Neighbors | Rsa | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:| |
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| 1.0824 | 1.0 | 15 | 0.6651 | 1.3143 | 1.3143 | 1.0930 | 1.0930 | 0.0052 | 0.0052 | 0.3043 | 0.0 | 0.5 | 0.4393 | nan | |
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| 0.9574 | 2.0 | 30 | 0.7088 | 1.3568 | 1.3568 | 1.1945 | 1.1945 | -0.0601 | -0.0601 | 0.0435 | 0.0 | 0.5 | 0.3380 | nan | |
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| 0.8151 | 3.0 | 45 | 0.6300 | 1.2791 | 1.2791 | 1.0206 | 1.0206 | 0.0577 | 0.0577 | 0.3043 | 0.0 | 0.5 | 0.3613 | nan | |
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| 0.6401 | 4.0 | 60 | 0.4871 | 1.1247 | 1.1247 | 0.7285 | 0.7285 | 0.2715 | 0.2715 | 0.5652 | 0.0 | 0.5 | 0.6424 | nan | |
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| 0.448 | 5.0 | 75 | 0.5005 | 1.1401 | 1.1401 | 0.7216 | 0.7216 | 0.2514 | 0.2514 | 0.4783 | 0.0 | 0.5 | 0.6077 | nan | |
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| 0.2893 | 6.0 | 90 | 0.4761 | 1.1119 | 1.1119 | 0.7237 | 0.7237 | 0.2879 | 0.2879 | 0.5652 | 0.0 | 0.5 | 0.6348 | nan | |
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| 0.174 | 7.0 | 105 | 0.4771 | 1.1131 | 1.1131 | 0.6836 | 0.6836 | 0.2865 | 0.2865 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan | |
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| 0.1383 | 8.0 | 120 | 0.4313 | 1.0583 | 1.0583 | 0.6462 | 0.6462 | 0.3550 | 0.3550 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.1105 | 9.0 | 135 | 0.4660 | 1.1001 | 1.1001 | 0.6737 | 0.6737 | 0.3030 | 0.3030 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0903 | 10.0 | 150 | 0.4866 | 1.1241 | 1.1241 | 0.7192 | 0.7192 | 0.2723 | 0.2723 | 0.7391 | 0.0 | 0.5 | 0.6833 | nan | |
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| 0.0571 | 11.0 | 165 | 0.4361 | 1.0642 | 1.0642 | 0.6130 | 0.6130 | 0.3478 | 0.3478 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0623 | 12.0 | 180 | 0.4578 | 1.0904 | 1.0904 | 0.6844 | 0.6844 | 0.3152 | 0.3152 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan | |
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| 0.0526 | 13.0 | 195 | 0.4605 | 1.0936 | 1.0936 | 0.6697 | 0.6697 | 0.3112 | 0.3112 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan | |
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| 0.0472 | 14.0 | 210 | 0.4440 | 1.0738 | 1.0738 | 0.6589 | 0.6589 | 0.3360 | 0.3360 | 0.7391 | 0.0 | 0.5 | 0.7327 | nan | |
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| 0.0492 | 15.0 | 225 | 0.4593 | 1.0922 | 1.0922 | 0.6812 | 0.6812 | 0.3130 | 0.3130 | 0.7391 | 0.0 | 0.5 | 0.6833 | nan | |
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| 0.0389 | 16.0 | 240 | 0.4195 | 1.0437 | 1.0437 | 0.6252 | 0.6252 | 0.3726 | 0.3726 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0396 | 17.0 | 255 | 0.4087 | 1.0302 | 1.0302 | 0.6119 | 0.6119 | 0.3888 | 0.3888 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0328 | 18.0 | 270 | 0.4274 | 1.0535 | 1.0535 | 0.6457 | 0.6457 | 0.3608 | 0.3608 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan | |
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| 0.0345 | 19.0 | 285 | 0.4306 | 1.0574 | 1.0574 | 0.6576 | 0.6576 | 0.3560 | 0.3560 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan | |
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| 0.0328 | 20.0 | 300 | 0.4067 | 1.0277 | 1.0277 | 0.6160 | 0.6160 | 0.3918 | 0.3918 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0344 | 21.0 | 315 | 0.4056 | 1.0263 | 1.0263 | 0.5948 | 0.5948 | 0.3934 | 0.3934 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0312 | 22.0 | 330 | 0.4236 | 1.0488 | 1.0488 | 0.6277 | 0.6277 | 0.3665 | 0.3665 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0241 | 23.0 | 345 | 0.4272 | 1.0533 | 1.0533 | 0.6444 | 0.6444 | 0.3610 | 0.3610 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan | |
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| 0.0302 | 24.0 | 360 | 0.4046 | 1.0250 | 1.0250 | 0.6030 | 0.6030 | 0.3949 | 0.3949 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0244 | 25.0 | 375 | 0.4194 | 1.0436 | 1.0436 | 0.6320 | 0.6320 | 0.3728 | 0.3728 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0259 | 26.0 | 390 | 0.4025 | 1.0224 | 1.0224 | 0.6009 | 0.6009 | 0.3980 | 0.3980 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0265 | 27.0 | 405 | 0.4103 | 1.0323 | 1.0323 | 0.6180 | 0.6180 | 0.3863 | 0.3863 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0184 | 28.0 | 420 | 0.4059 | 1.0268 | 1.0268 | 0.6046 | 0.6046 | 0.3929 | 0.3929 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan | |
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| 0.0257 | 29.0 | 435 | 0.4088 | 1.0304 | 1.0304 | 0.6122 | 0.6122 | 0.3885 | 0.3885 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan | |
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| 0.0262 | 30.0 | 450 | 0.4120 | 1.0345 | 1.0345 | 0.6181 | 0.6181 | 0.3837 | 0.3837 | 0.9130 | 0.0 | 0.5 | 0.8986 | 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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