readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-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-xlmr-blame-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: 0.9414
  - Rmse: 0.7875
  - Rmse Blame::a Un concetto astratto o un'emozione: 0.7875
  - Mae: 0.6165
  - Mae Blame::a Un concetto astratto o un'emozione: 0.6165
  - R2: 0.2291
  - R2 Blame::a Un concetto astratto o un'emozione: 0.2291
  - Cos: 0.1304
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.3509
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0549        | 1.0   | 15   | 1.2093          | 0.8925 | 0.8925                                           | 0.6659 | 0.6659                                          | 0.0097 | 0.0097                                         | -0.3043 | 0.0  | 0.5  | 0.4013    | nan |
  | 1.0085        | 2.0   | 30   | 1.2199          | 0.8964 | 0.8964                                           | 0.6494 | 0.6494                                          | 0.0010 | 0.0010                                         | -0.1304 | 0.0  | 0.5  | 0.4515    | nan |
  | 1.0131        | 3.0   | 45   | 1.1798          | 0.8815 | 0.8815                                           | 0.6412 | 0.6412                                          | 0.0339 | 0.0339                                         | -0.2174 | 0.0  | 0.5  | 0.2402    | nan |
  | 0.9931        | 4.0   | 60   | 1.1726          | 0.8788 | 0.8788                                           | 0.6370 | 0.6370                                          | 0.0397 | 0.0397                                         | -0.1304 | 0.0  | 0.5  | 0.2911    | nan |
  | 0.9668        | 5.0   | 75   | 1.1194          | 0.8587 | 0.8587                                           | 0.5925 | 0.5925                                          | 0.0833 | 0.0833                                         | 0.2174  | 0.0  | 0.5  | 0.3303    | nan |
  | 0.8759        | 6.0   | 90   | 1.0776          | 0.8425 | 0.8425                                           | 0.6265 | 0.6265                                          | 0.1175 | 0.1175                                         | 0.3043  | 0.0  | 0.5  | 0.4190    | nan |
  | 0.8787        | 7.0   | 105  | 1.0513          | 0.8321 | 0.8321                                           | 0.6087 | 0.6087                                          | 0.1391 | 0.1391                                         | 0.2174  | 0.0  | 0.5  | 0.3303    | nan |
  | 0.7637        | 8.0   | 120  | 1.0537          | 0.8331 | 0.8331                                           | 0.6265 | 0.6265                                          | 0.1372 | 0.1372                                         | 0.2174  | 0.0  | 0.5  | 0.3303    | nan |
  | 0.6568        | 9.0   | 135  | 0.9104          | 0.7744 | 0.7744                                           | 0.5887 | 0.5887                                          | 0.2544 | 0.2544                                         | 0.3043  | 0.0  | 0.5  | 0.3680    | nan |
  | 0.6354        | 10.0  | 150  | 0.9055          | 0.7723 | 0.7723                                           | 0.6222 | 0.6222                                          | 0.2585 | 0.2585                                         | 0.1304  | 0.0  | 0.5  | 0.3987    | nan |
  | 0.5107        | 11.0  | 165  | 1.0173          | 0.8186 | 0.8186                                           | 0.6168 | 0.6168                                          | 0.1669 | 0.1669                                         | 0.2174  | 0.0  | 0.5  | 0.3303    | nan |
  | 0.4598        | 12.0  | 180  | 0.9155          | 0.7765 | 0.7765                                           | 0.6284 | 0.6284                                          | 0.2503 