readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-blame-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-blame-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: 0.5128
  - Rmse: 1.0287
  - Rmse Blame::a L'assassino: 1.0287
  - Mae: 0.8883
  - Mae Blame::a L'assassino: 0.8883
  - R2: 0.5883
  - R2 Blame::a L'assassino: 0.5883
  - Cos: 0.6522
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.5795
  - 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 L'assassino | Mae    | Mae Blame::a L'assassino | R2     | R2 Blame::a L'assassino | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.0184        | 1.0   | 15   | 1.2219          | 1.5879 | 1.5879                    | 1.4308 | 1.4308                   | 0.0191 | 0.0191                  | 0.3913 | 0.0  | 0.5  | 0.3781    | nan |
  | 0.9214        | 2.0   | 30   | 1.0927          | 1.5017 | 1.5017                    | 1.3634 | 1.3634                   | 0.1227 | 0.1227                  | 0.5652 | 0.0  | 0.5  | 0.4512    | nan |
  | 0.7809        | 3.0   | 45   | 0.8206          | 1.3013 | 1.3013                    | 1.1808 | 1.1808                   | 0.3412 | 0.3412                  | 0.4783 | 0.0  | 0.5  | 0.3819    | nan |
  | 0.6593        | 4.0   | 60   | 0.5894          | 1.1029 | 1.1029                    | 1.0145 | 1.0145                   | 0.5268 | 0.5268                  | 0.7391 | 0.0  | 0.5  | 0.6408    | nan |
  | 0.4672        | 5.0   | 75   | 0.4759          | 0.9910 | 0.9910                    | 0.8868 | 0.8868                   | 0.6180 | 0.6180                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.3356        | 6.0   | 90   | 0.4220          | 0.9332 | 0.9332                    | 0.8083 | 0.8083                   | 0.6612 | 0.6612                  | 0.6522 | 0.0  | 0.5  | 0.4249    | nan |
  | 0.2782        | 7.0   | 105  | 0.4477          | 0.9612 | 0.9612                    | 0.8046 | 0.8046                   | 0.6406 | 0.6406                  | 0.6522 | 0.0  | 0.5  | 0.6101    | nan |
  | 0.2075        | 8.0   | 120  | 0.4389          | 0.9518 | 0.9518                    | 0.8050 | 0.8050                   | 0.6476 | 0.6476                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
  | 0.1725        | 9.0   | 135  | 0.4832          | 0.9985 | 0.9985                    | 0.8356 | 0.8356                   | 0.6121 | 0.6121                  | 0.7391 | 0.0  | 0.5  | 0.6616    | nan |
  | 0.1642        | 10.0  | 150  | 0.4368          | 0.9494 | 0.9494                    | 0.8060 | 0.8060                   | 0.6493 | 0.6493                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
  | 0.1172        | 11.0  | 165  | 0.4538          | 0.9677 | 0.9677                    | 0.8174 | 0.8174                   | 0.6357 | 0.6357                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.104         | 12.0  | 180  | 0.4672          | 0.9819 | 0.9819                    | 0.8384 | 0.8384                   | 0.6249 | 0.6249                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.0822        | 13.0  | 195  | 0.4401          | 0.9530 | 0.9530                    | 0.8107 | 0.8107                   | 0.6467 | 0.6467                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.0755        | 14.0  | 210  | 0.4464          | 0.9598 | 0.9598                    | 0.8251 | 0.8251                   | 0.6416 | 0.6416                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.0801        | 15.0  | 225  | 0.4834          | 0.9988 | 0.9988                    | 0.8604 | 0.8604                   | 0.6119 | 0.6119                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.053         | 16.0  | 240  | 0.4846          | 1.0001 | 1.0001                    | 0.8651 | 0.8651                   | 0.6109 | 0.6109                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
  | 0.0573        | 17.0  | 255  | 0.4970          | 1.0128 | 1.0128                    | 0.8743 | 0.8743                 