readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-cause-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-xlmr-cause-concept
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3933
  - Rmse: 0.5992
  - Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992
  - Mae: 0.4566
  - Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4566
  - R2: 0.5588
  - R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5588
  - Cos: 0.3043
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.4340
  - 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 concetto astratto (es. gelosia) | Mae    | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2      | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0114        | 1.0   | 15   | 0.9088          | 0.9109 | 0.9109                                                      | 0.6455 | 0.6455                                                     | -0.0195 | -0.0195                                                   | -0.0435 | 0.0  | 0.5  | 0.4027    | nan |
  | 1.0           | 2.0   | 30   | 0.8833          | 0.8980 | 0.8980                                                      | 0.6104 | 0.6104                                                     | 0.0090  | 0.0090                                                    | 0.2174  | 0.0  | 0.5  | 0.3681    | nan |
  | 0.9533        | 3.0   | 45   | 0.8453          | 0.8785 | 0.8785                                                      | 0.6072 | 0.6072                                                     | 0.0517  | 0.0517                                                    | 0.1304  | 0.0  | 0.5  | 0.3748    | nan |
  | 0.9113        | 4.0   | 60   | 0.7797          | 0.8437 | 0.8437                                                      | 0.6024 | 0.6024                                                     | 0.1253  | 0.1253                                                    | 0.0435  | 0.0  | 0.5  | 0.3028    | nan |
  | 0.8312        | 5.0   | 75   | 0.5756          | 0.7249 | 0.7249                                                      | 0.5128 | 0.5128                                                     | 0.3542  | 0.3542                                                    | 0.4783  | 0.0  | 0.5  | 0.4572    | nan |
  | 0.7224        | 6.0   | 90   | 0.4977          | 0.6741 | 0.6741                                                      | 0.5114 | 0.5114                                                     | 0.4416  | 0.4416                                                    | 0.2174  | 0.0  | 0.5  | 0.4009    | nan |
  | 0.5789        | 7.0   | 105  | 0.6338          | 0.7607 | 0.7607                                                      | 0.5059 | 0.5059                                                     | 0.2889  | 0.2889                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
  | 0.4978        | 8.0   | 120  | 0.3342          | 0.5524 | 0.5524                                                      | 0.4298 | 0.4298                                                     | 0.6250  | 0.6250                                                    | 0.2174  | 0.0  | 0.5  | 0.4274    | nan |
  | 0.4572        | 9.0   | 135  | 0.3210          | 0.5413 | 0.5413                                                      | 0.4343 | 0.4343                                                     | 0.6399  | 0.6399                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
  | 0.3346        | 10.0  | 150  | 0.3456          | 0.5617 | 0.5617                                                      | 0.4198 | 0.4198                                                     | 0.6123  | 0.6123                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
  | 0.30