readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-blame-object
    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-object
  
  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: 0.7219
  - Rmse: 0.6215
  - Rmse Blame::a Un oggetto: 0.6215
  - Mae: 0.4130
  - Mae Blame::a Un oggetto: 0.4130
  - R2: 0.1200
  - R2 Blame::a Un oggetto: 0.1200
  - Cos: 0.3043
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.4335
  - 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 Un oggetto | Mae    | Mae Blame::a Un oggetto | R2      | R2 Blame::a Un oggetto | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0279        | 1.0   | 15   | 0.8483          | 0.6737 | 0.6737                   | 0.4761 | 0.4761                  | -0.0341 | -0.0341                | -0.3043 | 0.0  | 0.5  | 0.5507    | nan |
  | 1.0676        | 2.0   | 30   | 0.7749          | 0.6439 | 0.6439                   | 0.4291 | 0.4291                  | 0.0554  | 0.0554                 | 0.0435  | 0.0  | 0.5  | 0.2614    | nan |
  | 0.9563        | 3.0   | 45   | 0.7765          | 0.6446 | 0.6446                   | 0.4349 | 0.4349                  | 0.0535  | 0.0535                 | -0.0435 | 0.0  | 0.5  | 0.4515    | nan |
  | 0.9622        | 4.0   | 60   | 0.7443          | 0.6311 | 0.6311                   | 0.4061 | 0.4061                  | 0.0927  | 0.0927                 | 0.1304  | 0.0  | 0.5  | 0.2933    | nan |
  | 0.948         | 5.0   | 75   | 0.8071          | 0.6571 | 0.6571                   | 0.3817 | 0.3817                  | 0.0162  | 0.0162                 | 0.3043  | 0.0  | 0.5  | 0.4207    | nan |
  | 0.9532        | 6.0   | 90   | 0.8007          | 0.6546 | 0.6546                   | 0.4585 | 0.4585                  | 0.0239  | 0.0239                 | -0.0435 | 0.0  | 0.5  | 0.5507    | nan |
  | 0.9101        | 7.0   | 105  | 0.7126          | 0.6175 | 0.6175                   | 0.3649 | 0.3649                  | 0.1313  | 0.1313                 | 0.4783  | 0.0  | 0.5  | 0.6012    | nan |
  | 0.8369        | 8.0   | 120  | 0.7194          | 0.6204 | 0.6204                   | 0.3896 | 0.3896                  | 0.1231  | 0.1231                 | 0.3913  | 0.0  | 0.5  | 0.3494    | nan |
  | 0.8062        | 9.0   | 135  | 0.7157          | 0.6188 | 0.6188                   | 0.4192 | 0.4192                  | 0.1275  | 0.1275                 | 0.0435  | 0.0  | 0.5  | 0.3182    | nan |
  | 0.7344        | 10.0  | 150  | 0.7161          | 0.6190 | 0.6190                   | 0.3612 | 0.3612                  | 0.1270  | 0.1270                 | 0.3043  | 0.0  | 0.5  | 0.6035    | nan |
  | 0.7439        | 11.0  | 165  | 0.5894          | 0.5616 | 0.5616                   | 0.3723 | 0.3723                  | 0.2816  | 0.2816                 | 0.3043  | 0.0  | 0.5  | 0.3846    | nan |
  | 0.6241        | 12.0  | 180  | 0.7087          | 0.6158 | 0.6158                   | 0.3972 | 0.3972                  | 0.1361  | 0.1361                 | 0.3043  | 0.0  | 0.5  | 0.3846    | nan |
  | 0.6123        | 13.0  | 195  | 0.6318          | 0.5814 | 0.5814                   | 0.3673 | 0.3673                  | 0.2298  | 0.2298                 | 0.3913  | 0.0  | 0.5  | 0.4413    | nan |
  | 0.5364        | 14.0  | 210  | 0.6504          | 0.5899 | 0.5899                   | 0.3674 | 0.3674                  | 0.2072  | 0.2072                 | 0.3043  | 0.0  | 0.5  | 0.3846    | nan |
  | 0.5586        | 15.0  | 225  | 0.7151          | 0.6186 | 0.6186                   | 0.3850 | 0.3850                  | 0.1283  | 0.1283                 | 0.3043  | 0.0  | 0.5  | 0.4335    | nan |
  | 0.5133        | 16.0  | 240  | 0.5572          | 0.5460 | 0.5460                   | 0.3540 | 0.3540                  | 0.3208  | 0.3208                 | 0.4783  | 0.0  | 0.5  | 0.5314    | nan |
  | 0.4193        | 17.0  | 255  | 0.6047          | 0.5688 | 0.5688                   | 0.3710 | 0.3710                  | 0.2629  | 0.2629                 | 0.3913  | 0.0  | 0.5  