readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-focus-victim
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # predict-perception-xlmr-focus-victim
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2546
  - Rmse: 0.6301
  - Rmse Focus::a Sulla vittima: 0.6301
  - Mae: 0.5441
  - Mae Focus::a Sulla vittima: 0.5441
  - R2: 0.7205
  - R2 Focus::a Sulla vittima: 0.7205
  - Cos: 0.8261
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.7802
  - Rsa: nan
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 20
  - eval_batch_size: 8
  - seed: 1996
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rmse   | Rmse Focus::a Sulla vittima | Mae    | Mae Focus::a Sulla vittima | R2      | R2 Focus::a Sulla vittima | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0607        | 1.0   | 15   | 0.9261          | 1.2017 | 1.2017                      | 0.9557 | 0.9557                     | -0.0166 | -0.0166                   | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
  | 1.0107        | 2.0   | 30   | 0.9481          | 1.2159 | 1.2159                      | 0.9861 | 0.9861                     | -0.0408 | -0.0408                   | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
  | 0.9921        | 3.0   | 45   | 0.9068          | 1.1892 | 1.1892                      | 0.9548 | 0.9548                     | 0.0045  | 0.0045                    | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
  | 0.7769        | 4.0   | 60   | 0.5014          | 0.8842 | 0.8842                      | 0.7121 | 0.7121                     | 0.4496  | 0.4496                    | 0.7391 | 0.0  | 0.5  | 0.6232    | nan |
  | 0.5763        | 5.0   | 75   | 0.4019          | 0.7917 | 0.7917                      | 0.6737 | 0.6737                     | 0.5588  | 0.5588                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
  | 0.4378        | 6.0   | 90   | 0.3594          | 0.7486 | 0.7486                      | 0.5957 | 0.5957                     | 0.6055  | 0.6055                    | 0.7391 | 0.0  | 0.5  | 0.4442    | nan |
  | 0.3595        | 7.0   | 105  | 0.3452          | 0.7337 | 0.7337                      | 0.6333 | 0.6333                     | 0.6210  | 0.6210                    | 0.5652 | 0.0  | 0.5  | 0.2649    | nan |
  | 0.3192        | 8.0   | 120  | 0.3275          | 0.7147 | 0.7147                      | 0.6205 | 0.6205                     | 0.6405  | 0.6405                    | 0.7391 | 0.0  | 0.5  | 0.6561    | nan |
  | 0.2482        | 9.0   | 135  | 0.2978          | 0.6815 | 0.6815                      | 0.5754 | 0.5754                     | 0.6731  | 0.6731                    | 0.7391 | 0.0  | 0.5  | 0.6715    | nan |
  | 0.2416        | 10.0  | 150  | 0.3018          | 0.6860 | 0.6860                      | 0.5954 | 0.5954                     | 0.6687  | 0.6687                    | 0.5652 | 0.0  | 0.5  | 0.2553    | nan |
  | 0.2292        | 11.0  | 165  | 0.2764          | 0.6565 | 0.6565                      | 0.5522 | 0.5522                     | 0.6966  | 0.6966                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
  | 0.1752        | 12.0  | 180  | 0.3070          | 0.6920 | 0.6920                      | 0.5680 | 0.5680                     | 0.6629  | 0.6629                    | 0.7391 | 0.0  | 0.5  | 0.6715    | nan |
  | 0.1956        | 13.0  | 195  | 0.2923          | 0.6752 | 0.6752                      | 0.5499 | 0.5499                     | 0.6791  | 0.6791                    | 0.8261 | 0.0  | 0.5  | 0.7843    | nan |
  | 0.1424        | 14.0  | 210  | 0.3163          | 0.7023 | 0.7023                      | 0.6060 | 0.6060                     | 0.6528  | 0.6528                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
  | 0.152         | 15.0  | 225  | 0.2436          | 0.6164 | 0.6164                      | 0.5127 | 0.5127                     | 0.7326  | 0.7326                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
  | 0.1277        | 16.0  | 240  | 0.2471          | 0.6208 | 0.6208                      | 0.5367 | 0.5367                     | 0.7287  | 0.7287                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
  | 0.1269        | 17.0  | 