readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-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-bert-focus-victim
  
  This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2466
  - Rmse: 0.6201
  - Rmse Focus::a Sulla vittima: 0.6201
  - Mae: 0.4936
  - Mae Focus::a Sulla vittima: 0.4936
  - R2: 0.7293
  - R2 Focus::a Sulla vittima: 0.7293
  - Cos: 0.8261
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.8155
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0247        | 1.0   | 15   | 1.0286          | 1.2665 | 1.2665                      | 1.0280 | 1.0280                     | -0.1292 | -0.1292                   | 0.1304 | 0.0  | 0.5  | 0.3685    | nan |
  | 0.9912        | 2.0   | 30   | 1.0039          | 1.2512 | 1.2512                      | 1.0347 | 1.0347                     | -0.1020 | -0.1020                   | 0.0435 | 0.0  | 0.5  | 0.3333    | nan |
  | 0.9147        | 3.0   | 45   | 0.9338          | 1.2067 | 1.2067                      | 0.9770 | 0.9770                     | -0.0251 | -0.0251                   | 0.1304 | 0.0  | 0.5  | 0.3685    | nan |
  | 0.8194        | 4.0   | 60   | 0.7641          | 1.0916 | 1.0916                      | 0.8476 | 0.8476                     | 0.1612  | 0.1612                    | 0.4783 | 0.0  | 0.5  | 0.5284    | nan |
  | 0.6636        | 5.0   | 75   | 0.6618          | 1.0159 | 1.0159                      | 0.8012 | 0.8012                     | 0.2735  | 0.2735                    | 0.6522 | 0.0  | 0.5  | 0.4741    | nan |
  | 0.523         | 6.0   | 90   | 0.5176          | 0.8984 | 0.8984                      | 0.7044 | 0.7044                     | 0.4318  | 0.4318                    | 0.6522 | 0.0  | 0.5  | 0.4741    | nan |
  | 0.402         | 7.0   | 105  | 0.3804          | 0.7702 | 0.7702                      | 0.6042 | 0.6042                     | 0.5824  | 0.5824                    | 0.6522 | 0.0  | 0.5  | 0.5395    | nan |
  | 0.3401        | 8.0   | 120  | 0.3594          | 0.7487 | 0.7487                      | 0.5703 | 0.5703                     | 0.6054  | 0.6054                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.2615        | 9.0   | 135  | 0.3429          | 0.7312 | 0.7312                      | 0.6049 | 0.6049                     | 0.6236  | 0.6236                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.1928        | 10.0  | 150  | 0.2889          | 0.6712 | 0.6712                      | 0.5487 | 0.5487                     | 0.6828  | 0.6828                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.1703        | 11.0  | 165  | 0.2675          | 0.6458 | 0.6458                      | 0.5188 | 0.5188                     | 0.7064  | 0.7064                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.1209        | 12.0  | 180  | 0.2826          | 0.6639 | 0.6639                      | 0.5475 | 0.5475                     | 0.6897  | 0.6897                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.1428        | 13.0  | 195  | 0.2978          | 0.6815 | 0.6815                      | 0.5777 | 0.5777                     | 0.6731  | 0.6731                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.1038        | 14.0  | 210  | 0.2924          | 0.6753 | 0.6753                      | 0.5865 | 0.5865                     | 0.6790  | 0.6790                    | 0.6522 | 0.0  | 0.5  | 0.2760    | nan |
  | 0.0951        | 15.0  | 225  | 0.2905          | 0.6731 | 0.6731                      | 0.5750 | 0.5750                     | 0.6811  | 0.6811                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
  | 0.0809        | 16.0  | 240  | 0.2676          | 0.6460 | 0.6460                      | 0.5552 | 0.5552                     | 0.7062  | 0.7062                    | 0.7391 | 0.0  | 0.5  | 0.6920    