readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-blame-assassin
    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-blame-assassin
  
  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: 0.4439
  - Rmse: 0.9571
  - Rmse Blame::a L'assassino: 0.9571
  - Mae: 0.7260
  - Mae Blame::a L'assassino: 0.7260
  - R2: 0.6437
  - R2 Blame::a L'assassino: 0.6437
  - Cos: 0.7391
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.6287
  - 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 Blame::a L'assassino | Mae    | Mae Blame::a L'assassino | R2     | R2 Blame::a L'assassino | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0317        | 1.0   | 15   | 1.1311          | 1.5278 | 1.5278                    | 1.3893 | 1.3893                   | 0.0919 | 0.0919                  | 0.5652 | 0.0  | 0.5  | 0.4512    | nan |
  | 0.9475        | 2.0   | 30   | 1.0795          | 1.4926 | 1.4926                    | 1.3387 | 1.3387                   | 0.1334 | 0.1334                  | 0.8261 | 0.0  | 0.5  | 0.6184    | nan |
  | 0.9146        | 3.0   | 45   | 1.1092          | 1.5130 | 1.5130                    | 1.4078 | 1.4078                   | 0.1095 | 0.1095                  | 0.4783 | 0.0  | 0.5  | 0.3116    | nan |
  | 0.9539        | 4.0   | 60   | 1.1734          | 1.5561 | 1.5561                    | 1.4238 | 1.4238                   | 0.0580 | 0.0580                  | 0.3913 | 0.0  | 0.5  | 0.3614    | nan |
  | 0.8665        | 5.0   | 75   | 0.8910          | 1.3560 | 1.3560                    | 1.2350 | 1.2350                   | 0.2847 | 0.2847                  | 0.5652 | 0.0  | 0.5  | 0.4136    | nan |
  | 0.6564        | 6.0   | 90   | 0.8469          | 1.3220 | 1.3220                    | 1.1570 | 1.1570                   | 0.3201 | 0.3201                  | 0.3913 | 0.0  | 0.5  | 0.3931    | nan |
  | 0.5241        | 7.0   | 105  | 0.6429          | 1.1519 | 1.1519                    | 0.9757 | 0.9757                   | 0.4838 | 0.4838                  | 0.5652 | 0.0  | 0.5  | 0.4222    | nan |
  | 0.4589        | 8.0   | 120  | 0.5781          | 1.0923 | 1.0923                    | 0.8714 | 0.8714                   | 0.5359 | 0.5359                  | 0.6522 | 0.0  | 0.5  | 0.4641    | nan |
  | 0.4043        | 9.0   | 135  | 0.4525          | 0.9664 | 0.9664                    | 0.8257 | 0.8257                   | 0.6367 | 0.6367                  | 0.5652 | 0.0  | 0.5  | 0.4263    | nan |
  | 0.3498        | 10.0  | 150  | 0.4490          | 0.9627 | 0.9627                    | 0.8272 | 0.8272                   | 0.6395 | 0.6395                  | 0.6522 | 0.0  | 0.5  | 0.5144    | nan |
  | 0.3505        | 11.0  | 165  | 0.3721          | 0.8763 | 0.8763                    | 0.7471 | 0.7471                   | 0.7013 | 0.7013                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.3426        | 12.0  | 180  | 0.4117          | 0.9218 | 0.9218                    | 0.7477 | 0.7477                   | 0.6695 | 0.6695                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.3074        | 13.0  | 195  | 0.3761          | 0.8810 | 0.8810                    | 0.7109 | 0.7109                   | 0.6981 | 0.6981                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.2261        | 14.0  | 210  | 0.3818          | 0.8877 | 0.8877                    | 0.7042 | 0.7042                   | 0.6935 | 0.6935                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.2399        | 15.0  | 225  | 0.3893          | 0.8964 | 0.8964                    | 0.7108 | 0.7108                   | 0.6874 | 0.6874                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.2014        | 16.0  | 240  | 0.4606          | 0.9750 | 0.9750                    | 0.8046 | 0.8046                   | 0.6302 | 0.6302                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
  | 0.1937        | 17.0  | 255  | 0.4549          | 0.9689 | 0.9689                    | 0.7679 | 0.7679                   | 0.6348 | 0.6348               