readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-blame-concept
    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-concept
  
  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: 0.7359
  - Rmse: 0.6962
  - Rmse Blame::a Un concetto astratto o un'emozione: 0.6962
  - Mae: 0.5010
  - Mae Blame::a Un concetto astratto o un'emozione: 0.5010
  - R2: 0.3974
  - R2 Blame::a Un concetto astratto o un'emozione: 0.3974
  - Cos: 0.3913
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.5507
  - 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 concetto astratto o un'emozione | Mae    | Mae Blame::a Un concetto astratto o un'emozione | R2      | R2 Blame::a Un concetto astratto o un'emozione | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:-------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0979        | 1.0   | 15   | 1.2387          | 0.9033 | 0.9033                                           | 0.6603 | 0.6603                                          | -0.0144 | -0.0144                                        | 0.0435  | 0.0  | 0.5  | 0.3432    | nan |
  | 1.0172        | 2.0   | 30   | 1.1498          | 0.8703 | 0.8703                                           | 0.5964 | 0.5964                                          | 0.0584  | 0.0584                                         | 0.0435  | 0.0  | 0.5  | 0.2935    | nan |
  | 0.9879        | 3.0   | 45   | 1.2139          | 0.8942 | 0.8942                                           | 0.6197 | 0.6197                                          | 0.0060  | 0.0060                                         | 0.2174  | 0.0  | 0.5  | 0.4582    | nan |
  | 0.9723        | 4.0   | 60   | 1.1152          | 0.8571 | 0.8571                                           | 0.5982 | 0.5982                                          | 0.0867  | 0.0867                                         | 0.2174  | 0.0  | 0.5  | 0.3921    | nan |
  | 0.9584        | 5.0   | 75   | 1.0607          | 0.8358 | 0.8358                                           | 0.5959 | 0.5959                                          | 0.1314  | 0.1314                                         | 0.0435  | 0.0  | 0.5  | 0.4165    | nan |
  | 0.9023        | 6.0   | 90   | 1.0031          | 0.8128 | 0.8128                                           | 0.5827 | 0.5827                                          | 0.1786  | 0.1786                                         | -0.0435 | 0.0  | 0.5  | 0.3862    | nan |
  | 0.8745        | 7.0   | 105  | 0.9715          | 0.7999 | 0.7999                                           | 0.5796 | 0.5796                                          | 0.2044  | 0.2044                                         | 0.3043  | 0.0  | 0.5  | 0.3665    | nan |
  | 0.8082        | 8.0   | 120  | 0.8984          | 0.7692 | 0.7692                                           | 0.5699 | 0.5699                                          | 0.2643  | 0.2643                                         | 0.1304  | 0.0  | 0.5  | 0.3390    | nan |
  | 0.7475        | 9.0   | 135  | 0.8532          | 0.7497 | 0.7497                                           | 0.5849 | 0.5849                                          | 0.3013  | 0.3013                                         | 0.0435  | 0.0  | 0.5  | 0.3100    | nan |
  | 0.6599        | 10.0  | 150  | 0.8737          | 0.7586 | 0.7586                                           | 0.5822 | 0.5822                                          | 0.2846  | 0.2846                                         | 0.3043  | 0.0  | 0.5  | 0.3830    | nan |
  | 0.5867        | 11.0  | 165  | 0.8159          | 0.7331 | 0.7331                                           | 0.5752 | 0.5752                                          | 0.3318  | 0.3318                                         | 0.2174  | 0.0  | 0.5  | 0.4439    | nan |
  | 0.5081        | 12.0  | 180  | 0.8367          | 0.7424 | 0.7424                                           | 0.6071 | 0.6071    