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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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base_model: xlm-roberta-base |
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model-index: |
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- name: predict-perception-xlmr-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-xlmr-blame-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: 0.9414 |
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- Rmse: 0.7875 |
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- Rmse Blame::a Un concetto astratto o un'emozione: 0.7875 |
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- Mae: 0.6165 |
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- Mae Blame::a Un concetto astratto o un'emozione: 0.6165 |
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- R2: 0.2291 |
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- R2 Blame::a Un concetto astratto o un'emozione: 0.2291 |
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- Cos: 0.1304 |
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- Pair: 0.0 |
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- Rank: 0.5 |
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- Neighbors: 0.3509 |
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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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| 1.0549 | 1.0 | 15 | 1.2093 | 0.8925 | 0.8925 | 0.6659 | 0.6659 | 0.0097 | 0.0097 | -0.3043 | 0.0 | 0.5 | 0.4013 | nan | |
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| 1.0085 | 2.0 | 30 | 1.2199 | 0.8964 | 0.8964 | 0.6494 | 0.6494 | 0.0010 | 0.0010 | -0.1304 | 0.0 | 0.5 | 0.4515 | nan | |
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| 1.0131 | 3.0 | 45 | 1.1798 | 0.8815 | 0.8815 | 0.6412 | 0.6412 | 0.0339 | 0.0339 | -0.2174 | 0.0 | 0.5 | 0.2402 | nan | |
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| 0.9931 | 4.0 | 60 | 1.1726 | 0.8788 | 0.8788 | 0.6370 | 0.6370 | 0.0397 | 0.0397 | -0.1304 | 0.0 | 0.5 | 0.2911 | nan | |
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| 0.9668 | 5.0 | 75 | 1.1194 | 0.8587 | 0.8587 | 0.5925 | 0.5925 | 0.0833 | 0.0833 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.8759 | 6.0 | 90 | 1.0776 | 0.8425 | 0.8425 | 0.6265 | 0.6265 | 0.1175 | 0.1175 | 0.3043 | 0.0 | 0.5 | 0.4190 | nan | |
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| 0.8787 | 7.0 | 105 | 1.0513 | 0.8321 | 0.8321 | 0.6087 | 0.6087 | 0.1391 | 0.1391 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.7637 | 8.0 | 120 | 1.0537 | 0.8331 | 0.8331 | 0.6265 | 0.6265 | 0.1372 | 0.1372 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.6568 | 9.0 | 135 | 0.9104 | 0.7744 | 0.7744 | 0.5887 | 0.5887 | 0.2544 | 0.2544 | 0.3043 | 0.0 | 0.5 | 0.3680 | nan | |
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| 0.6354 | 10.0 | 150 | 0.9055 | 0.7723 | 0.7723 | 0.6222 | 0.6222 | 0.2585 | 0.2585 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan | |
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| 0.5107 | 11.0 | 165 | 1.0173 | 0.8186 | 0.8186 | 0.6168 | 0.6168 | 0.1669 | 0.1669 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.4598 | 12.0 | 180 | 0.9155 | 0.7765 | 0.7765 | 0.6284 | 0.6284 | 0.2503 | 0.2503 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan | |
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| 0.3815 | 13.0 | 195 | 0.9255 | 0.7808 | 0.7808 | 0.6140 | 0.6140 | 0.2421 | 0.2421 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan | |
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| 0.3303 | 14.0 | 210 | 0.8506 | 0.7485 | 0.7485 | 0.6076 | 0.6076 | 0.3035 | 0.3035 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2799 | 15.0 | 225 | 1.0272 | 0.8226 | 0.8226 | 0.6699 | 0.6699 | 0.1588 | 0.1588 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2998 | 16.0 | 240 | 0.9969 | 0.8103 | 0.8103 | 0.6461 | 0.6461 | 0.1836 | 0.1836 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.3131 | 17.0 | 255 | 0.9066 | 0.7727 | 0.7727 | 0.5849 | 0.5849 | 0.2576 | 0.2576 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.2234 | 18.0 | 270 | 0.8741 | 0.7588 | 0.7588 | 0.5953 | 0.5953 | 0.2842 | 0.2842 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan | |
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| 0.2481 | 19.0 | 285 | 1.0022 | 0.8125 | 0.8125 | 0.6549 | 0.6549 | 0.1793 | 0.1793 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2333 | 20.0 | 300 | 0.9238 | 0.7801 | 0.7801 | 0.6180 | 0.6180 | 0.2435 | 0.2435 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2407 | 21.0 | 315 | 0.9868 | 0.8062 | 0.8062 | 0.6457 | 0.6457 | 0.1919 | 0.1919 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2122 | 22.0 | 330 | 0.9514 | 0.7916 | 0.7916 | 0.6204 | 0.6204 | 0.2209 | 0.2209 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2162 | 23.0 | 345 | 0.9227 | 0.7796 | 0.7796 | 0.6053 | 0.6053 | 0.2444 | 0.2444 | 0.1304 | 0.0 | 0.5 | 0.3509 | nan | |
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| 0.1739 | 24.0 | 360 | 0.9147 | 0.7762 | 0.7762 | 0.5979 | 0.5979 | 0.2510 | 0.2510 | 0.1304 | 0.0 | 0.5 | 0.3509 | nan | |
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| 0.2084 | 25.0 | 375 | 0.9645 | 0.7970 | 0.7970 | 0.6296 | 0.6296 | 0.2102 | 0.2102 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.1702 | 26.0 | 390 | 0.9587 | 0.7946 | 0.7946 | 0.6279 | 0.6279 | 0.2149 | 0.2149 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2146 | 27.0 | 405 | 0.9519 | 0.7918 | 0.7918 | 0.6273 | 0.6273 | 0.2205 | 0.2205 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.1645 | 28.0 | 420 | 0.9398 | 0.7868 | 0.7868 | 0.6181 | 0.6181 | 0.2304 | 0.2304 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.2052 | 29.0 | 435 | 0.9492 | 0.7907 | 0.7907 | 0.6228 | 0.6228 | 0.2227 | 0.2227 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan | |
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| 0.147 | 30.0 | 450 | 0.9414 | 0.7875 | 0.7875 | 0.6165 | 0.6165 | 0.2291 | 0.2291 | 0.1304 | 0.0 | 0.5 | 0.3509 | 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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