readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-focus-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-bert-focus-assassin
  
  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: 0.2964
  - Rmse: 0.8992
  - Rmse Focus::a Sull'assassino: 0.8992
  - Mae: 0.7331
  - Mae Focus::a Sull'assassino: 0.7331
  - R2: 0.6500
  - R2 Focus::a Sull'assassino: 0.6500
  - Cos: 0.7391
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.6131
  - 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 Sull'assassino | Mae    | Mae Focus::a Sull'assassino | R2      | R2 Focus::a Sull'assassino | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0674        | 1.0   | 15   | 0.9851          | 1.6393 | 1.6393                       | 1.5316 | 1.5316                      | -0.1633 | -0.1633                    | 0.1304 | 0.0  | 0.5  | 0.2457    | nan |
  | 1.0099        | 2.0   | 30   | 0.8921          | 1.5601 | 1.5601                       | 1.4317 | 1.4317                      | -0.0535 | -0.0535                    | 0.5652 | 0.0  | 0.5  | 0.4734    | nan |
  | 0.9295        | 3.0   | 45   | 0.7345          | 1.4155 | 1.4155                       | 1.3113 | 1.3113                      | 0.1327  | 0.1327                     | 0.5652 | 0.0  | 0.5  | 0.3596    | nan |
  | 0.8485        | 4.0   | 60   | 0.7282          | 1.4094 | 1.4094                       | 1.2678 | 1.2678                      | 0.1401  | 0.1401                     | 0.7391 | 0.0  | 0.5  | 0.5367    | nan |
  | 0.7551        | 5.0   | 75   | 0.5966          | 1.2758 | 1.2758                       | 1.1144 | 1.1144                      | 0.2955  | 0.2955                     | 0.6522 | 0.0  | 0.5  | 0.3911    | nan |
  | 0.5563        | 6.0   | 90   | 0.4578          | 1.1175 | 1.1175                       | 0.9105 | 0.9105                      | 0.4594  | 0.4594                     | 0.6522 | 0.0  | 0.5  | 0.3911    | nan |
  | 0.4048        | 7.0   | 105  | 0.3539          | 0.9826 | 0.9826                       | 0.7770 | 0.7770                      | 0.5821  | 0.5821                     | 0.6522 | 0.0  | 0.5  | 0.5522    | nan |
  | 0.3319        | 8.0   | 120  | 0.2938          | 0.8953 | 0.8953                       | 0.7110 | 0.7110                      | 0.6530  | 0.6530                     | 0.6522 | 0.0  | 0.5  | 0.6021    | nan |
  | 0.2224        | 9.0   | 135  | 0.3455          | 0.9708 | 0.9708                       | 0.7607 | 0.7607                      | 0.5921  | 0.5921                     | 0.6522 | 0.0  | 0.5  | 0.3911    | nan |
  | 0.1794        | 10.0  | 150  | 0.2719          | 0.8612 | 0.8612                       | 0.6768 | 0.6768                      | 0.6790  | 0.6790                     | 0.7391 | 0.0  | 0.5  | 0.6131    | nan |
  | 0.1553        | 11.0  | 165  | 0.2855          | 0.8826 | 0.8826                       | 0.7053 | 0.7053                      | 0.6628  | 0.6628                     | 0.7391 | 0.0  | 0.5  | 0.6131    | nan |
  | 0.1008        | 12.0  | 180  | 0.3000          | 0.9046 | 0.9046                       | 0.7255 | 0.7255                      | 0.6458  | 0.6458                     | 0.6522 | 0.0  | 0.5  | 0.5261    | nan |
  | 0.1121        | 13.0  | 195  | 0.2817          | 0.8766 | 0.8766                       | 0.7236 | 0.7236                      | 0.6674  | 0.6674                     | 0.7391 | 0.0  | 0.5  | 0.6131    | nan |
  | 0.08          | 14.0  | 210  | 0.3504          | 0.9777 | 0.9777                       | 0.7631 | 0.7631                      | 0.5863  | 0.5863                     | 0.7391 | 0.0  | 0.5  | 0.6131    | nan |
  | 0.0802        | 15.0  | 225  | 0.3031          | 0.9094 | 0.9094                       | 0.7565 | 0.7565                      | 0.6420  | 0.6420                     | 0.7391 | 0.0  | 0.5  | 0.6131    | nan |
  | 0.0685        | 16.0  | 240  | 0.3041          | 0.9109 | 0.9109                       | 0.7409 | 0.7409                      | 0.6408  | 0