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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-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-cause-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: 0.3933 |
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- Rmse: 0.5992 |
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- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992 |
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- Mae: 0.4566 |
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- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4566 |
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- R2: 0.5588 |
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- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5588 |
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- Cos: 0.3043 |
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- Pair: 0.0 |
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- Rank: 0.5 |
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- Neighbors: 0.4340 |
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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 concetto astratto (es. gelosia) | Mae | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2 | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos | Pair | Rank | Neighbors | Rsa | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:| |
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| 1.0114 | 1.0 | 15 | 0.9088 | 0.9109 | 0.9109 | 0.6455 | 0.6455 | -0.0195 | -0.0195 | -0.0435 | 0.0 | 0.5 | 0.4027 | nan | |
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| 1.0 | 2.0 | 30 | 0.8833 | 0.8980 | 0.8980 | 0.6104 | 0.6104 | 0.0090 | 0.0090 | 0.2174 | 0.0 | 0.5 | 0.3681 | nan | |
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| 0.9533 | 3.0 | 45 | 0.8453 | 0.8785 | 0.8785 | 0.6072 | 0.6072 | 0.0517 | 0.0517 | 0.1304 | 0.0 | 0.5 | 0.3748 | nan | |
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| 0.9113 | 4.0 | 60 | 0.7797 | 0.8437 | 0.8437 | 0.6024 | 0.6024 | 0.1253 | 0.1253 | 0.0435 | 0.0 | 0.5 | 0.3028 | nan | |
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| 0.8312 | 5.0 | 75 | 0.5756 | 0.7249 | 0.7249 | 0.5128 | 0.5128 | 0.3542 | 0.3542 | 0.4783 | 0.0 | 0.5 | 0.4572 | nan | |
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| 0.7224 | 6.0 | 90 | 0.4977 | 0.6741 | 0.6741 | 0.5114 | 0.5114 | 0.4416 | 0.4416 | 0.2174 | 0.0 | 0.5 | 0.4009 | nan | |
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| 0.5789 | 7.0 | 105 | 0.6338 | 0.7607 | 0.7607 | 0.5059 | 0.5059 | 0.2889 | 0.2889 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.4978 | 8.0 | 120 | 0.3342 | 0.5524 | 0.5524 | 0.4298 | 0.4298 | 0.6250 | 0.6250 | 0.2174 | 0.0 | 0.5 | 0.4274 | nan | |
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| 0.4572 | 9.0 | 135 | 0.3210 | 0.5413 | 0.5413 | 0.4343 | 0.4343 | 0.6399 | 0.6399 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.3346 | 10.0 | 150 | 0.3456 | 0.5617 | 0.5617 | 0.4198 | 0.4198 | 0.6123 | 0.6123 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.3046 | 11.0 | 165 | 0.3840 | 0.5921 | 0.5921 | 0.4312 | 0.4312 | 0.5692 | 0.5692 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.3035 | 12.0 | 180 | 0.3929 | 0.5989 | 0.5989 | 0.4147 | 0.4147 | 0.5592 | 0.5592 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.2199 | 13.0 | 195 | 0.3165 | 0.5376 | 0.5376 | 0.4065 | 0.4065 | 0.6449 | 0.6449 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.2376 | 14.0 | 210 | 0.3108 | 0.5326 | 0.5326 | 0.3937 | 0.3937 | 0.6514 | 0.6514 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan | |
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| 0.1639 | 15.0 | 225 | 0.3645 | 0.5769 | 0.5769 | 0.4094 | 0.4094 | 0.5911 | 0.5911 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan | |
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| 0.1884 | 16.0 | 240 | 0.3762 | 0.5860 | 0.5860 | 0.4398 | 0.4398 | 0.5779 | 0.5779 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1767 | 17.0 | 255 | 0.3805 | 0.5894 | 0.5894 | 0.4540 | 0.4540 | 0.5732 | 0.5732 | 0.2174 | 0.0 | 0.5 | 0.4298 | nan | |
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| 0.1329 | 18.0 | 270 | 0.3555 | 0.5697 | 0.5697 | 0.4281 | 0.4281 | 0.6011 | 0.6011 | 0.2174 | 0.0 | 0.5 | 0.4298 | nan | |
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| 0.1834 | 19.0 | 285 | 0.4337 | 0.6292 | 0.6292 | 0.4402 | 0.4402 | 0.5135 | 0.5135 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan | |
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| 0.1538 | 20.0 | 300 | 0.3554 | 0.5696 | 0.5696 | 0.4236 | 0.4236 | 0.6013 | 0.6013 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1459 | 21.0 | 315 | 0.3592 | 0.5726 | 0.5726 | 0.4348 | 0.4348 | 0.5971 | 0.5971 | 0.3043 | 0.0 | 0.5 | 0.4066 | nan | |
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| 0.1038 | 22.0 | 330 | 0.3732 | 0.5837 | 0.5837 | 0.4382 | 0.4382 | 0.5813 | 0.5813 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan | |
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| 0.1432 | 23.0 | 345 | 0.3635 | 0.5760 | 0.5760 | 0.4394 | 0.4394 | 0.5922 | 0.5922 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan | |
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| 0.1354 | 24.0 | 360 | 0.4359 | 0.6308 | 0.6308 | 0.4793 | 0.4793 | 0.5110 | 0.5110 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1404 | 25.0 | 375 | 0.3919 | 0.5982 | 0.5982 | 0.4650 | 0.4650 | 0.5603 | 0.5603 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan | |
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| 0.103 | 26.0 | 390 | 0.4223 | 0.6209 | 0.6209 | 0.4691 | 0.4691 | 0.5263 | 0.5263 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1733 | 27.0 | 405 | 0.3972 | 0.6021 | 0.6021 | 0.4591 | 0.4591 | 0.5544 | 0.5544 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1019 | 28.0 | 420 | 0.3958 | 0.6011 | 0.6011 | 0.4593 | 0.4593 | 0.5559 | 0.5559 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.1076 | 29.0 | 435 | 0.4015 | 0.6054 | 0.6054 | 0.4589 | 0.4589 | 0.5496 | 0.5496 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan | |
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| 0.0999 | 30.0 | 450 | 0.3933 | 0.5992 | 0.5992 | 0.4566 | 0.4566 | 0.5588 | 0.5588 | 0.3043 | 0.0 | 0.5 | 0.4340 | 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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