readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-blame-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-blame-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: 0.5837
  - Rmse: 0.5589
  - Rmse Blame::a Un oggetto: 0.5589
  - Mae: 0.3862
  - Mae Blame::a Un oggetto: 0.3862
  - R2: 0.2884
  - R2 Blame::a Un oggetto: 0.2884
  - Cos: 0.3913
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.5024
  - 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 Un oggetto | Mae    | Mae Blame::a Un oggetto | R2      | R2 Blame::a Un oggetto | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0603        | 1.0   | 15   | 0.8503          | 0.6745 | 0.6745                   | 0.4386 | 0.4386                  | -0.0365 | -0.0365                | 0.1304 | 0.0  | 0.5  | 0.5197    | nan |
  | 0.9662        | 2.0   | 30   | 0.8510          | 0.6748 | 0.6748                   | 0.4548 | 0.4548                  | -0.0374 | -0.0374                | 0.0435 | 0.0  | 0.5  | 0.4840    | nan |
  | 0.9438        | 3.0   | 45   | 0.7622          | 0.6386 | 0.6386                   | 0.4541 | 0.4541                  | 0.0709  | 0.0709                 | 0.0435 | 0.0  | 0.5  | 0.4635    | nan |
  | 0.9096        | 4.0   | 60   | 0.8301          | 0.6665 | 0.6665                   | 0.4305 | 0.4305                  | -0.0119 | -0.0119                | 0.0435 | 0.0  | 0.5  | 0.3499    | nan |
  | 0.8383        | 5.0   | 75   | 0.7306          | 0.6252 | 0.6252                   | 0.3814 | 0.3814                  | 0.1094  | 0.1094                 | 0.3043 | 0.0  | 0.5  | 0.5098    | nan |
  | 0.7828        | 6.0   | 90   | 0.7434          | 0.6307 | 0.6307                   | 0.4005 | 0.4005                  | 0.0937  | 0.0937                 | 0.3043 | 0.0  | 0.5  | 0.4335    | nan |
  | 0.7028        | 7.0   | 105  | 0.7218          | 0.6214 | 0.6214                   | 0.4090 | 0.4090                  | 0.1202  | 0.1202                 | 0.3913 | 0.0  | 0.5  | 0.4470    | nan |
  | 0.6661        | 8.0   | 120  | 0.7434          | 0.6307 | 0.6307                   | 0.4042 | 0.4042                  | 0.0938  | 0.0938                 | 0.3913 | 0.0  | 0.5  | 0.4470    | nan |
  | 0.578         | 9.0   | 135  | 0.7719          | 0.6426 | 0.6426                   | 0.3975 | 0.3975                  | 0.0591  | 0.0591                 | 0.3913 | 0.0  | 0.5  | 0.4470    | nan |
  | 0.544         | 10.0  | 150  | 0.7117          | 0.6171 | 0.6171                   | 0.4126 | 0.4126                  | 0.1324  | 0.1324                 | 0.2174 | 0.0  | 0.5  | 0.3489    | nan |
  | 0.4638        | 11.0  | 165  | 0.6683          | 0.5980 | 0.5980                   | 0.3952 | 0.3952                  | 0.1853  | 0.1853                 | 0.3043 | 0.0  | 0.5  | 0.3989    | nan |
  | 0.3998        | 12.0  | 180  | 0.6772          | 0.6019 | 0.6019                   | 0.4201 | 0.4201                  | 0.1745  | 0.1745                 | 0.3043 | 0.0  | 0.5  | 0.3989    | nan |
  | 0.3403        | 13.0  | 195  | 0.6576          | 0.5932 | 0.5932                   | 0.4237 | 0.4237                  | 0.1984  | 0.1984                 | 0.2174 | 0.0  | 0.5  | 0.3491    | nan |
  | 0.2839        | 14.0  | 210  | 0.6281          | 0.5797 | 0.5797                   | 0.4208 | 0.4208                  | 0.2344  | 0.2344                 | 0.2174 | 0.0  | 0.5  | 0.3491    | nan |
  | 0.2619        | 15.0  | 225  | 0.6254          | 0.5785 | 0.5785                   | 0.3752 | 0.3752                  | 0.2376  | 0.2376                 | 0.3913 | 0.0  | 0.5  | 0.5756    | nan |
  | 0.2175        | 16.0  | 240  | 0.6074          | 0.5701 | 0.5701                   | 0.3985 | 0.3985                  | 0.2596  | 0.2596                 | 0.3043 | 0.0  | 0.5  | 0.4142    | nan |
  | 0.1884        | 17.0  | 255  | 0.6045          | 0.5687 | 0.5687                   | 0.4036 | 0.4036                  | 0.2631  | 0.2631                 | 0.3913