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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-focus-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-xlmr-focus-concept |
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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: 0.8296 |
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- Rmse: 1.0302 |
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- Rmse Focus::a Su un concetto astratto o un'emozione: 1.0302 |
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- Mae: 0.7515 |
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- Mae Focus::a Su un concetto astratto o un'emozione: 0.7515 |
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- R2: 0.1804 |
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- R2 Focus::a Su un concetto astratto o un'emozione: 0.1804 |
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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: 0.3415 |
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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 Focus::a Su un concetto astratto o un'emozione | Mae | Mae Focus::a Su un concetto astratto o un'emozione | R2 | R2 Focus::a Su un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------:|:------:|:--------------------------------------------------:|:-------:|:-------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:| |
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| 1.0355 | 1.0 | 15 | 0.9822 | 1.1209 | 1.1209 | 0.9649 | 0.9649 | 0.0296 | 0.0296 | 0.2174 | 0.0 | 0.5 | 0.3706 | nan | |
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| 1.0083 | 2.0 | 30 | 1.1378 | 1.2065 | 1.2065 | 0.9954 | 0.9954 | -0.1241 | -0.1241 | 0.2174 | 0.0 | 0.5 | 0.3309 | nan | |
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| 0.9823 | 3.0 | 45 | 0.9669 | 1.1121 | 1.1121 | 0.9315 | 0.9315 | 0.0448 | 0.0448 | 0.3043 | 0.0 | 0.5 | 0.3810 | nan | |
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| 0.9468 | 4.0 | 60 | 0.8856 | 1.0644 | 1.0644 | 0.8584 | 0.8584 | 0.1251 | 0.1251 | 0.3913 | 0.0 | 0.5 | 0.3803 | nan | |
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| 0.9294 | 5.0 | 75 | 0.8136 | 1.0202 | 1.0202 | 0.8396 | 0.8396 | 0.1963 | 0.1963 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan | |
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| 0.881 | 6.0 | 90 | 0.7634 | 0.9882 | 0.9882 | 0.8192 | 0.8192 | 0.2458 | 0.2458 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan | |
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| 0.7589 | 7.0 | 105 | 0.8139 | 1.0204 | 1.0204 | 0.8136 | 0.8136 | 0.1960 | 0.1960 | 0.5652 | 0.0 | 0.5 | 0.4120 | nan | |
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| 0.7217 | 8.0 | 120 | 0.9105 | 1.0792 | 1.0792 | 0.9394 | 0.9394 | 0.1005 | 0.1005 | 0.3913 | 0.0 | 0.5 | 0.4108 | nan | |
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| 0.8059 | 9.0 | 135 | 1.0322 | 1.1491 | 1.1491 | 0.9115 | 0.9115 | -0.0197 | -0.0197 | 0.5652 | 0.0 | 0.5 | 0.3738 | nan | |
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| 0.6483 | 10.0 | 150 | 0.7989 | 1.0109 | 1.0109 | 0.7899 | 0.7899 | 0.2108 | 0.2108 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan | |
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| 0.5725 | 11.0 | 165 | 0.7175 | 0.9581 | 0.9581 | 0.7011 | 0.7011 | 0.2912 | 0.2912 | 0.5652 | 0.0 | 0.5 | 0.3738 | nan | |
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| 0.5091 | 12.0 | 180 | 0.8818 | 1.0621 | 1.0621 | 0.8775 | 0.8775 | 0.1289 | 0.1289 | 0.5652 | 0.0 | 0.5 | 0.4063 | nan | |
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| 0.4526 | 13.0 | 195 | 0.8451 | 1.0398 | 1.0398 | 0.7990 | 0.7990 | 0.1651 | 0.1651 | 0.5652 | 0.0 | 0.5 | 0.4063 | nan | |
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| 0.361 | 14.0 | 210 | 0.8632 | 1.0508 | 1.0508 | 0.8124 | 0.8124 | 0.1472 | 0.1472 | 0.4783 | 0.0 | 0.5 | 0.3699 | nan | |
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| 0.3582 | 15.0 | 225 | 0.8461 | 1.0404 | 1.0404 | 0.7923 | 0.7923 | 0.1641 | 0.1641 | 0.3913 | 0.0 | 0.5 | 0.3672 | nan | |
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| 0.2945 | 16.0 | 240 | 0.9142 | 1.0814 | 1.0814 | 0.8125 | 0.8125 | 0.0968 | 0.0968 | 0.3913 | 0.0 | 0.5 | 0.3672 | nan | |
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| 0.2891 | 17.0 | 255 | 0.8377 | 1.0352 | 1.0352 | 0.7718 | 0.7718 | 0.1724 | 0.1724 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.2569 | 18.0 | 270 | 0.8106 | 1.0183 | 1.0183 | 0.7481 | 0.7481 | 0.1992 | 0.1992 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.2583 | 19.0 | 285 | 0.8239 | 1.0266 | 1.0266 | 0.7597 | 0.7597 | 0.1861 | 0.1861 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.2217 | 20.0 | 300 | 0.8485 | 1.0419 | 1.0419 | 0.7663 | 0.7663 | 0.1617 | 0.1617 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1927 | 21.0 | 315 | 0.8304 | 1.0307 | 1.0307 | 0.7536 | 0.7536 | 0.1797 | 0.1797 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.176 | 22.0 | 330 | 0.8321 | 1.0317 | 1.0317 | 0.7539 | 0.7539 | 0.1780 | 0.1780 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1639 | 23.0 | 345 | 0.7914 | 1.0062 | 1.0062 | 0.7460 | 0.7460 | 0.2182 | 0.2182 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.177 | 24.0 | 360 | 0.8619 | 1.0500 | 1.0500 | 0.7725 | 0.7725 | 0.1486 | 0.1486 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1473 | 25.0 | 375 | 0.8101 | 1.0180 | 1.0180 | 0.7587 | 0.7587 | 0.1997 | 0.1997 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.181 | 26.0 | 390 | 0.8038 | 1.0141 | 1.0141 | 0.7433 | 0.7433 | 0.2059 | 0.2059 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1679 | 27.0 | 405 | 0.7982 | 1.0105 | 1.0105 | 0.7248 | 0.7248 | 0.2115 | 0.2115 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1529 | 28.0 | 420 | 0.8282 | 1.0293 | 1.0293 | 0.7454 | 0.7454 | 0.1818 | 0.1818 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1822 | 29.0 | 435 | 0.8310 | 1.0311 | 1.0311 | 0.7512 | 0.7512 | 0.1790 | 0.1790 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan | |
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| 0.1442 | 30.0 | 450 | 0.8296 | 1.0302 | 1.0302 | 0.7515 | 0.7515 | 0.1804 | 0.1804 | 0.4783 | 0.0 | 0.5 | 0.3415 | 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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