readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-focus-concept
    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-concept
  
  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: 0.8296
  - Rmse: 1.0302
  - Rmse Focus::a Su un concetto astratto o un'emozione: 1.0302
  - Mae: 0.7515
  - Mae Focus::a Su un concetto astratto o un'emozione: 0.7515
  - R2: 0.1804
  - R2 Focus::a Su un concetto astratto o un'emozione: 0.1804
  - Cos: 0.4783
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.3415
  - 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 Su un concetto astratto o un'emozione | Mae    | Mae Focus::a Su un concetto astratto o un'emozione | R2      | R2 Focus::a Su un concetto astratto o un'emozione | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------:|:------:|:--------------------------------------------------:|:-------:|:-------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0355        | 1.0   | 15   | 0.9822          | 1.1209 | 1.1209                                              | 0.9649 | 0.9649                                             | 0.0296  | 0.0296                                            | 0.2174 | 0.0  | 0.5  | 0.3706    | nan |
  | 1.0083        | 2.0   | 30   | 1.1378          | 1.2065 | 1.2065                                              | 0.9954 | 0.9954                                             | -0.1241 | -0.1241                                           | 0.2174 | 0.0  | 0.5  | 0.3309    | nan |
  | 0.9823        | 3.0   | 45   | 0.9669          | 1.1121 | 1.1121                                              | 0.9315 | 0.9315                                             | 0.0448  | 0.0448                                            | 0.3043 | 0.0  | 0.5  | 0.3810    | nan |
  | 0.9468        | 4.0   | 60   | 0.8856          | 1.0644 | 1.0644                                              | 0.8584 | 0.8584                                             | 0.1251  | 0.1251                                            | 0.3913 | 0.0  | 0.5  | 0.3803    | nan |
  | 0.9294        | 5.0   | 75   | 0.8136          | 1.0202 | 1.0202                                              | 0.8396 | 0.8396                                             | 0.1963  | 0.1963                                            | 0.6522 | 0.0  | 0.5  | 0.4727    | nan |
  | 0.881         | 6.0   | 90   | 0.7634          | 0.9882 | 0.9882                                              | 0.8192 | 0.8192                                             | 0.2458  | 0.2458                                            | 0.6522 | 0.0  | 0.5  | 0.4727    | nan |
  | 0.7589        | 7.0   | 105  | 0.8139          | 1.0204 | 1.0204                                              | 0.8136 | 0.8136                                             | 0.1960  | 0.1960                                            | 0.5652 | 0.0  | 0.5  | 0.4120    | nan |
  | 0.7217        | 8.0   | 120  | 0.9105          | 1.0792 | 1.0792                                              | 0.9394 | 0.9394                                             | 0.1005  | 0.1005                                            | 0.3913 | 0.0  | 0.5  | 0.4108    | nan |
  | 0.8059        | 9.0   | 135  | 1.0322          | 1.1491 | 1.1491                                              | 0.9115 | 0.9115                                             | -0.0197 | -0.0197                                           | 0.5652 | 0.0  | 0.5  | 0.3738    | nan |
  | 0.6483        | 10.0  | 150  | 0.7989          | 1.0109 | 1.0109                                              | 0.7899 | 0.7899                                             | 0.2108  | 0.2108                                            | 0.6522 | 0.0  | 0.5  | 0.4727    | nan |
  | 0.5725        | 11.0  | 165  | 0.7175          | 0.9581 | 0.9581                                              | 0.7011 | 0.7011                                             | 0.2912  | 0.2912                                            | 0.5652 | 0.0  | 0.5  | 0.3738    | nan |
  | 0.5091        | 12.0  | 180  | 0.8818          | 