readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-focus-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-bert-focus-object
  
  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: 0.2271
  - Rmse: 0.5965
  - Rmse Focus::a Su un oggetto: 0.5965
  - Mae: 0.4372
  - Mae Focus::a Su un oggetto: 0.4372
  - R2: 0.4957
  - R2 Focus::a Su un oggetto: 0.4957
  - Cos: 0.6522
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.6622
  - 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 oggetto | Mae    | Mae Focus::a Su un oggetto | R2     | R2 Focus::a Su un oggetto | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0371        | 1.0   | 15   | 0.4358          | 0.8263 | 0.8263                      | 0.7132 | 0.7132                     | 0.0323 | 0.0323                    | 0.3043 | 0.0  | 0.5  | 0.3510    | nan |
  | 0.9574        | 2.0   | 30   | 0.4420          | 0.8321 | 0.8321                      | 0.7175 | 0.7175                     | 0.0186 | 0.0186                    | 0.3043 | 0.0  | 0.5  | 0.4627    | nan |
  | 0.9137        | 3.0   | 45   | 0.4208          | 0.8119 | 0.8119                      | 0.6955 | 0.6955                     | 0.0657 | 0.0657                    | 0.3913 | 0.0  | 0.5  | 0.3928    | nan |
  | 0.8465        | 4.0   | 60   | 0.3356          | 0.7251 | 0.7251                      | 0.6237 | 0.6237                     | 0.2548 | 0.2548                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
  | 0.6864        | 5.0   | 75   | 0.2876          | 0.6712 | 0.6712                      | 0.5624 | 0.5624                     | 0.3616 | 0.3616                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
  | 0.5804        | 6.0   | 90   | 0.3148          | 0.7022 | 0.7022                      | 0.5577 | 0.5577                     | 0.3011 | 0.3011                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
  | 0.4983        | 7.0   | 105  | 0.4068          | 0.7983 | 0.7983                      | 0.6606 | 0.6606                     | 0.0968 | 0.0968                    | 0.3913 | 0.0  | 0.5  | 0.4519    | nan |
  | 0.3584        | 8.0   | 120  | 0.2567          | 0.6342 | 0.6342                      | 0.4883 | 0.4883                     | 0.4300 | 0.4300                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
  | 0.2771        | 9.0   | 135  | 0.2130          | 0.5777 | 0.5777                      | 0.4193 | 0.4193                     | 0.5270 | 0.5270                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.2135        | 10.0  | 150  | 0.2522          | 0.6285 | 0.6285                      | 0.4572 | 0.4572                     | 0.4401 | 0.4401                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.1654        | 11.0  | 165  | 0.2662          | 0.6457 | 0.6457                      | 0.4603 | 0.4603                     | 0.4090 | 0.4090                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.1554        | 12.0  | 180  | 0.2459          | 0.6207 | 0.6207                      | 0.4778 | 0.4778                     | 0.4540 | 0.4540                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.1195        | 13.0  | 195  | 0.2385          | 0.6113 | 0.6113                      | 0.4618 | 0.4618                     | 0.4704 | 0.4704                    | 0.5652 | 0.0  | 0.5  | 0.5693    | nan |
  | 0.1046        | 14.0  | 210  | 0.2296          | 0.5997 | 0.5997                      | 0.4544 | 0.4544                     | 0.4903 | 0.4903                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.089         | 15.0  | 225  | 0.2520          | 0.6283 | 0.6283                      | 0.4974 | 0.4974                     | 0.4404 | 0.4404                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
  | 0.083         | 16.0  | 240  | 0.2297          | 0.5998 | 0.5998                      | 0.4635 | 0.4635                     | 0.4901 | 0.4901                    | 0.5652 | 0.0  | 0.5  | 0.5610    | nan |
  | 0.0701  