readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-blame-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-blame-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: 1.1098
  - Rmse: 0.6801
  - Rmse Blame::a La vittima: 0.6801
  - Mae: 0.5617
  - Mae Blame::a La vittima: 0.5617
  - R2: -1.5910
  - R2 Blame::a La vittima: -1.5910
  - Cos: -0.1304
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.3333
  - 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 La vittima | Mae    | Mae Blame::a La vittima | R2      | R2 Blame::a La vittima | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0422        | 1.0   | 15   | 0.4952          | 0.4542 | 0.4542                   | 0.4095 | 0.4095                  | -0.1560 | -0.1560                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
  | 1.0434        | 2.0   | 30   | 0.4851          | 0.4496 | 0.4496                   | 0.4054 | 0.4054                  | -0.1324 | -0.1324                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
  | 1.038         | 3.0   | 45   | 0.4513          | 0.4337 | 0.4337                   | 0.3885 | 0.3885                  | -0.0536 | -0.0536                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
  | 1.0151        | 4.0   | 60   | 0.4395          | 0.4280 | 0.4280                   | 0.3840 | 0.3840                  | -0.0262 | -0.0262                | -0.1304 | 0.0  | 0.5  | 0.2715    | nan |
  | 0.9727        | 5.0   | 75   | 0.4490          | 0.4325 | 0.4325                   | 0.3811 | 0.3811                  | -0.0482 | -0.0482                | 0.2174  | 0.0  | 0.5  | 0.3338    | nan |
  | 0.9733        | 6.0   | 90   | 0.4540          | 0.4349 | 0.4349                   | 0.3860 | 0.3860                  | -0.0598 | -0.0598                | -0.2174 | 0.0  | 0.5  | 0.3248    | nan |
  | 0.9396        | 7.0   | 105  | 0.4501          | 0.4331 | 0.4331                   | 0.3849 | 0.3849                  | -0.0508 | -0.0508                | 0.0435  | 0.0  | 0.5  | 0.2609    | nan |
  | 0.8759        | 8.0   | 120  | 0.4597          | 0.4377 | 0.4377                   | 0.3849 | 0.3849                  | -0.0731 | -0.0731                | 0.3043  | 0.0  | 0.5  | 0.3898    | nan |
  | 0.8768        | 9.0   | 135  | 0.4575          | 0.4366 | 0.4366                   | 0.3784 | 0.3784                  | -0.0680 | -0.0680                | 0.4783  | 0.0  | 0.5  | 0.4615    | nan |
  | 0.8312        | 10.0  | 150  | 0.5363          | 0.4727 | 0.4727                   | 0.4071 | 0.4071                  | -0.2520 | -0.2520                | -0.0435 | 0.0  | 0.5  | 0.2733    | nan |
  | 0.7296        | 11.0  | 165  | 0.5291          | 0.4696 | 0.4696                   | 0.4057 | 0.4057                  | -0.2353 | -0.2353                | 0.3043  | 0.0  | 0.5  | 0.3898    | nan |
  | 0.7941        | 12.0  | 180  | 0.5319          | 0.4708 | 0.4708                   | 0.4047 | 0.4047                  | -0.2417 | -0.2417                | 0.1304  | 0.0  | 0.5  | 0.3381    | nan |
  | 0.6486        | 13.0  | 195  | 0.6787          | 0.5318 | 0.5318                   | 0.4516 | 0.4516                  | -0.5846 | -0.5846                | 0.1304  | 0.0  | 0.5  | 0.3381    | nan |
  | 0.6241        | 14.0  | 210  | 1.0146          | 0.6502 | 0.6502                   | 0.5580 | 0.5580                  | -1.3687 | -1.3687                | -0.1304 | 0.0  | 0.5  | 0.3509    | nan |
  | 0.5868        | 15.0  | 225  | 0.7164          | 0.5464 | 0.5464                   | 0.4682 | 0.4682                  | -0.6725 | -0.6725                | -0.0435 | 0.0  | 0.5  | 0.3333    | nan |
  | 0.5305        | 16.0  | 240  | 0.9064          | 0.6146 | 0.6146                   | 0.5173 | 0.5173                  | -1.1161 | -1.1161                | -0.0435 | 0.0  | 0.5  | 0.3333    | nan |
  | 0.495         | 17.0  | 255  | 1.3860          | 0.7600 | 0.7600                   | 0.6433 | 0.6433                  | -2.2358 | -2.2358                | -0.0435 | 0.0  | 0.