readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-cause-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-cause-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: 0.4120
  - Rmse: 1.0345
  - Rmse Cause::a Causata da un oggetto (es. una pistola): 1.0345
  - Mae: 0.6181
  - Mae Cause::a Causata da un oggetto (es. una pistola): 0.6181
  - R2: 0.3837
  - R2 Cause::a Causata da un oggetto (es. una pistola): 0.3837
  - Cos: 0.9130
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.8986
  - 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 Cause::a Causata da un oggetto (es. una pistola) | Mae    | Mae Cause::a Causata da un oggetto (es. una pistola) | R2      | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0824        | 1.0   | 15   | 0.6651          | 1.3143 | 1.3143                                                | 1.0930 | 1.0930                                               | 0.0052  | 0.0052                                              | 0.3043 | 0.0  | 0.5  | 0.4393    | nan |
  | 0.9574        | 2.0   | 30   | 0.7088          | 1.3568 | 1.3568                                                | 1.1945 | 1.1945                                               | -0.0601 | -0.0601                                             | 0.0435 | 0.0  | 0.5  | 0.3380    | nan |
  | 0.8151        | 3.0   | 45   | 0.6300          | 1.2791 | 1.2791                                                | 1.0206 | 1.0206                                               | 0.0577  | 0.0577                                              | 0.3043 | 0.0  | 0.5  | 0.3613    | nan |
  | 0.6401        | 4.0   | 60   | 0.4871          | 1.1247 | 1.1247                                                | 0.7285 | 0.7285                                               | 0.2715  | 0.2715                                              | 0.5652 | 0.0  | 0.5  | 0.6424    | nan |
  | 0.448         | 5.0   | 75   | 0.5005          | 1.1401 | 1.1401                                                | 0.7216 | 0.7216                                               | 0.2514  | 0.2514                                              | 0.4783 | 0.0  | 0.5  | 0.6077    | nan |
  | 0.2893        | 6.0   | 90   | 0.4761          | 1.1119 | 1.1119                                                | 0.7237 | 0.7237                                               | 0.2879  | 0.2879                                              | 0.5652 | 0.0  | 0.5  | 0.6348    | nan |
  | 0.174         | 7.0   | 105  | 0.4771          | 1.1131 | 1.1131                                                | 0.6836 | 0.6836                                               | 0.2865  | 0.2865                                              | 0.6522 | 0.0  | 0.5  | 0.6785    | nan |
  | 0.1383        | 8.0   | 120  | 0.4313          | 1.0583 | 1.0583                                                | 0.6462 | 0.6462                                               | 0.3550  | 0.3550                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
  | 0.1105        | 9.0   | 135  | 0.4660          | 1.1001 | 1.1001                                                | 0.6737 | 0.6737                                               | 0.3030  | 0.3030                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
  | 0.0903        | 10.0  | 150  | 0.4866          | 1.1241 | 1.1241                                                | 0.7192 | 0.7192                                               | 0.2723  | 0.2723                                              | 0.7391 | 0.0  | 0.5  | 0.6833    | nan |
  | 0.0571        | 11.0  | 165  | 0.4361          | 1.0642 | 1.0642                                                | 0.6130 | 0.6130                                               | 0.3478  | 0.3478                        