readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-focus-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-focus-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: 0.8129
  - Rmse: 1.0197
  - Rmse Focus::a Su un concetto astratto o un'emozione: 1.0197
  - Mae: 0.7494
  - Mae Focus::a Su un concetto astratto o un'emozione: 0.7494
  - R2: 0.1970
  - R2 Focus::a Su un concetto astratto o un'emozione: 0.1970
  - Cos: 0.4783
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.4667
  - 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 concetto astratto o un'emozione | Mae    | Mae Focus::a Su un concetto astratto o un'emozione | R2      | R2 Focus::a Su un concetto astratto o un'emozione | Cos    | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------:|:------:|:--------------------------------------------------:|:-------:|:-------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
  | 1.047         | 1.0   | 15   | 1.0199          | 1.1422 | 1.1422                                              | 0.9321 | 0.9321                                             | -0.0075 | -0.0075                                           | 0.1304 | 0.0  | 0.5  | 0.3199    | nan |
  | 0.9914        | 2.0   | 30   | 0.9724          | 1.1153 | 1.1153                                              | 0.9407 | 0.9407                                             | 0.0393  | 0.0393                                            | 0.2174 | 0.0  | 0.5  | 0.3954    | nan |
  | 0.9049        | 3.0   | 45   | 0.9406          | 1.0969 | 1.0969                                              | 0.9170 | 0.9170                                             | 0.0708  | 0.0708                                            | 0.2174 | 0.0  | 0.5  | 0.3632    | nan |
  | 0.8826        | 4.0   | 60   | 0.8553          | 1.0460 | 1.0460                                              | 0.8570 | 0.8570                                             | 0.1551  | 0.1551                                            | 0.2174 | 0.0  | 0.5  | 0.3230    | nan |
  | 0.7837        | 5.0   | 75   | 0.8324          | 1.0319 | 1.0319                                              | 0.8683 | 0.8683                                             | 0.1776  | 0.1776                                            | 0.2174 | 0.0  | 0.5  | 0.3419    | nan |
  | 0.7013        | 6.0   | 90   | 0.7737          | 0.9949 | 0.9949                                              | 0.8150 | 0.8150                                             | 0.2356  | 0.2356                                            | 0.5652 | 0.0  | 0.5  | 0.5023    | nan |
  | 0.6429        | 7.0   | 105  | 0.7832          | 1.0010 | 1.0010                                              | 0.8005 | 0.8005                                             | 0.2262  | 0.2262                                            | 0.3913 | 0.0  | 0.5  | 0.4446    | nan |
  | 0.5526        | 8.0   | 120  | 0.7734          | 0.9946 | 0.9946                                              | 0.7704 | 0.7704                                             | 0.2360  | 0.2360                                            | 0.3043 | 0.0  | 0.5  | 0.2923    | nan |
  | 0.5194        | 9.0   | 135  | 0.6624          | 0.9205 | 0.9205                                              | 0.7013 | 0.7013                                             | 0.3456  | 0.3456                                            | 0.3913 | 0.0  | 0.5  | 0.3523    | nan |
  | 0.4278        | 10.0  | 150  | 0.8255          | 1.0276 | 1.0276                                              | 0.7351 | 0.7351                                             | 0.1845  | 0.1845                                            | 0.3043 | 0.0  | 0.5  | 0.4349    | nan |
  | 0.3522        | 11.0  | 165  | 0.9340          | 1.0931 | 1.0931                                              | 0.8069 | 0.8069                                             | 0.0773  | 0.0773                                            | 0.3913 | 0.0  | 0.5  | 0.4059    | nan |
  | 0.314         |