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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-xlmr-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-xlmr-cause-object |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3069 |
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- Rmse: 0.8927 |
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- Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927 |
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- Mae: 0.5854 |
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- Mae Cause::a Causata da un oggetto (es. una pistola): 0.5854 |
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- R2: 0.5410 |
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- R2 Cause::a Causata da un oggetto (es. una pistola): 0.5410 |
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- Cos: 0.4783 |
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- Pair: 0.0 |
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- Rank: 0.5 |
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- Neighbors: 0.6177 |
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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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| 1.0329 | 1.0 | 15 | 0.8168 | 1.4564 | 1.4564 | 1.2947 | 1.2947 | -0.2216 | -0.2216 | -0.5652 | 0.0 | 0.5 | 0.5993 | nan | |
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| 1.0096 | 2.0 | 30 | 0.7432 | 1.3893 | 1.3893 | 1.1883 | 1.1883 | -0.1116 | -0.1116 | -0.3913 | 0.0 | 0.5 | 0.6499 | nan | |
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| 0.9323 | 3.0 | 45 | 0.6879 | 1.3366 | 1.3366 | 1.1054 | 1.1054 | -0.0289 | -0.0289 | -0.1304 | 0.0 | 0.5 | 0.5471 | nan | |
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| 0.8636 | 4.0 | 60 | 0.6378 | 1.2870 | 1.2870 | 1.0477 | 1.0477 | 0.0461 | 0.0461 | 0.2174 | 0.0 | 0.5 | 0.3007 | nan | |
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| 0.8041 | 5.0 | 75 | 0.5494 | 1.1945 | 1.1945 | 0.9499 | 0.9499 | 0.1783 | 0.1783 | 0.6522 | 0.0 | 0.5 | 0.6695 | nan | |
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| 0.7413 | 6.0 | 90 | 0.5526 | 1.1980 | 1.1980 | 0.9503 | 0.9503 | 0.1735 | 0.1735 | 0.5652 | 0.0 | 0.5 | 0.3898 | nan | |
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| 0.6397 | 7.0 | 105 | 0.4726 | 1.1078 | 1.1078 | 0.7826 | 0.7826 | 0.2932 | 0.2932 | 0.5652 | 0.0 | 0.5 | 0.3257 | nan | |
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| 0.5556 | 8.0 | 120 | 0.7728 | 1.4167 | 1.4167 | 1.1528 | 1.1528 | -0.1558 | -0.1558 | 0.1304 | 0.0 | 0.5 | 0.4027 | nan | |
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| 0.4972 | 9.0 | 135 | 0.4375 | 1.0659 | 1.0659 | 0.7577 | 0.7577 | 0.3457 | 0.3457 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan | |
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| 0.3691 | 10.0 | 150 | 0.4990 | 1.1383 | 1.1383 | 0.8272 | 0.8272 | 0.2537 | 0.2537 | 0.4783 | 0.0 | 0.5 | 0.4781 | nan | |
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| 0.3381 | 11.0 | 165 | 0.4401 | 1.0690 | 1.0690 | 0.7319 | 0.7319 | 0.3418 | 0.3418 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan | |
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| 0.2966 | 12.0 | 180 | 0.4794 | 1.1158 | 1.1158 | 0.7835 | 0.7835 | 0.2830 | 0.2830 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan | |
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| 0.2324 | 13.0 | 195 | 0.4013 | 1.0208 | 1.0208 | 0.6873 | 0.6873 | 0.3998 | 0.3998 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan | |
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| 0.1848 | 14.0 | 210 | 0.4305 | 1.0574 | 1.0574 | 0.7372 | 0.7372 | 0.3561 | 0.3561 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan | |
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| 0.1621 | 15.0 | 225 | 0.3652 | 0.9738 | 0.9738 | 0.6164 | 0.6164 | 0.4538 | 0.4538 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.1762 | 16.0 | 240 | 0.3335 | 0.9307 | 0.9307 | 0.6458 | 0.6458 | 0.5012 | 0.5012 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan | |
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| 0.1404 | 17.0 | 255 | 0.3420 | 0.9424 | 0.9424 | 0.6599 | 0.6599 | 0.4886 | 0.4886 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan | |
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| 0.1379 | 18.0 | 270 | 0.2853 | 0.8608 | 0.8608 | 0.6063 | 0.6063 | 0.5733 | 0.5733 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan | |
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| 0.1322 | 19.0 | 285 | 0.3261 | 0.9203 | 0.9203 | 0.6548 | 0.6548 | 0.5123 | 0.5123 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan | |
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| 0.1067 | 20.0 | 300 | 0.3328 | 0.9296 | 0.9296 | 0.5535 | 0.5535 | 0.5023 | 0.5023 | 0.6522 | 0.0 | 0.5 | 0.6695 | nan | |
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| 0.1038 | 21.0 | 315 | 0.3066 | 0.8924 | 0.8924 | 0.6266 | 0.6266 | 0.5414 | 0.5414 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan | |
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| 0.094 | 22.0 | 330 | 0.2924 | 0.8714 | 0.8714 | 0.5792 | 0.5792 | 0.5626 | 0.5626 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.1078 | 23.0 | 345 | 0.3161 | 0.9060 | 0.9060 | 0.6022 | 0.6022 | 0.5272 | 0.5272 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan | |
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| 0.0976 | 24.0 | 360 | 0.3118 | 0.8998 | 0.8998 | 0.6011 | 0.6011 | 0.5337 | 0.5337 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan | |
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| 0.0911 | 25.0 | 375 | 0.3123 | 0.9005 | 0.9005 | 0.5811 | 0.5811 | 0.5330 | 0.5330 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.1039 | 26.0 | 390 | 0.3122 | 0.9005 | 0.9005 | 0.5956 | 0.5956 | 0.5330 | 0.5330 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.0775 | 27.0 | 405 | 0.3191 | 0.9103 | 0.9103 | 0.6124 | 0.6124 | 0.5228 | 0.5228 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan | |
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| 0.0789 | 28.0 | 420 | 0.3135 | 0.9023 | 0.9023 | 0.5825 | 0.5825 | 0.5311 | 0.5311 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.0778 | 29.0 | 435 | 0.3075 | 0.8936 | 0.8936 | 0.5837 | 0.5837 | 0.5401 | 0.5401 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan | |
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| 0.082 | 30.0 | 450 | 0.3069 | 0.8927 | 0.8927 | 0.5854 | 0.5854 | 0.5410 | 0.5410 | 0.4783 | 0.0 | 0.5 | 0.6177 | 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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