readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-bert-blame-none
    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-none
  
  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: 0.8646
  - Rmse: 1.1072
  - Rmse Blame::a Nessuno: 1.1072
  - Mae: 0.8721
  - Mae Blame::a Nessuno: 0.8721
  - R2: 0.3083
  - R2 Blame::a Nessuno: 0.3083
  - Cos: 0.5652
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.5070
  - 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 Nessuno | Mae    | Mae Blame::a Nessuno | R2      | R2 Blame::a Nessuno | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.007         | 1.0   | 15   | 1.2585          | 1.3358 | 1.3358                | 1.1752 | 1.1752               | -0.0068 | -0.0068             | -0.0435 | 0.0  | 0.5  | 0.2970    | nan |
  | 0.927         | 2.0   | 30   | 1.1310          | 1.2663 | 1.2663                | 1.0633 | 1.0633               | 0.0952  | 0.0952              | 0.4783  | 0.0  | 0.5  | 0.4012    | nan |
  | 0.8376        | 3.0   | 45   | 1.0603          | 1.2261 | 1.2261                | 1.0574 | 1.0574               | 0.1518  | 0.1518              | 0.1304  | 0.0  | 0.5  | 0.2970    | nan |
  | 0.7154        | 4.0   | 60   | 0.8347          | 1.0879 | 1.0879                | 0.8854 | 0.8854               | 0.3323  | 0.3323              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
  | 0.5766        | 5.0   | 75   | 0.7426          | 1.0261 | 1.0261                | 0.8340 | 0.8340               | 0.4059  | 0.4059              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
  | 0.4632        | 6.0   | 90   | 0.6671          | 0.9725 | 0.9725                | 0.7932 | 0.7932               | 0.4663  | 0.4663              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
  | 0.3854        | 7.0   | 105  | 0.6447          | 0.9561 | 0.9561                | 0.7424 | 0.7424               | 0.4842  | 0.4842              | 0.6522  | 0.0  | 0.5  | 0.4307    | nan |
  | 0.3154        | 8.0   | 120  | 0.7198          | 1.0102 | 1.0102                | 0.8113 | 0.8113               | 0.4241  | 0.4241              | 0.6522  | 0.0  | 0.5  | 0.4307    | nan |
  | 0.2637        | 9.0   | 135  | 0.7221          | 1.0118 | 1.0118                | 0.8319 | 0.8319               | 0.4223  | 0.4223              | 0.5652  | 0.0  | 0.5  | 0.4150    | nan |
  | 0.1962        | 10.0  | 150  | 0.6999          | 0.9962 | 0.9962                | 0.7945 | 0.7945               | 0.4401  | 0.4401              | 0.4783  | 0.0  | 0.5  | 0.4056    | nan |
  | 0.1784        | 11.0  | 165  | 0.7335          | 1.0198 | 1.0198                | 0.7969 | 0.7969               | 0.4132  | 0.4132              | 0.5652  | 0.0  | 0.5  | 0.4150    | nan |
  | 0.1531        | 12.0  | 180  | 0.8277          | 1.0833 | 1.0833                | 0.8839 | 0.8839               | 0.3378  | 0.3378              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
  | 0.1425        | 13.0  | 195  | 0.8644          | 1.1070 | 1.1070                | 0.8726 | 0.8726               | 0.3085  | 0.3085              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
  | 0.0921        | 14.0  | 210  | 0.8874          | 1.1217 | 1.1217                | 0.9024 | 0.9024               | 0.2900  | 0.2900              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
  | 0.0913        | 15.0  | 225  | 0.8663          | 1.1083 | 1.1083                | 0.8914 | 0.8914               | 0.3070  | 0.3070              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
  | 0.08          | 16.0  | 240  | 0.8678          | 1.1093 | 1.1093                | 0.8762 | 0.8762               | 0.3057  | 0.3057              | 0.6522  | 0.0  | 0.5  | 0.5931    | nan |
  | 0.0725        | 17.0  | 255  | 0.8497          | 1.0976 | 1.0976                | 0.8868 | 0.8868               | 0.3202  | 0.3202              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
  | 0.0696        | 18.0  | 270  | 0.8533          | 1.1000 | 1.1000                | 0.8796 | 0.8796               | 0.3173  | 0.31