readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-cause-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-xlmr-cause-none
  
  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: 1.8639
  - Rmse: 1.3661
  - Rmse Cause::a Spontanea, priva di un agente scatenante: 1.3661
  - Mae: 1.0795
  - Mae Cause::a Spontanea, priva di un agente scatenante: 1.0795
  - R2: -1.7872
  - R2 Cause::a Spontanea, priva di un agente scatenante: -1.7872
  - Cos: -0.3043
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.3501
  - 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 Spontanea, priva di un agente scatenante | Mae    | Mae Cause::a Spontanea, priva di un agente scatenante | R2      | R2 Cause::a Spontanea, priva di un agente scatenante | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------------:|:------:|:-----------------------------------------------------:|:-------:|:----------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0626        | 1.0   | 15   | 0.6787          | 0.8244 | 0.8244                                                 | 0.7453 | 0.7453                                                | -0.0149 | -0.0149                                              | 0.0435  | 0.0  | 0.5  | 0.2515    | nan |
  | 1.0186        | 2.0   | 30   | 0.6769          | 0.8233 | 0.8233                                                 | 0.7457 | 0.7457                                                | -0.0122 | -0.0122                                              | 0.0435  | 0.0  | 0.5  | 0.2515    | nan |
  | 1.0346        | 3.0   | 45   | 0.6812          | 0.8259 | 0.8259                                                 | 0.7489 | 0.7489                                                | -0.0187 | -0.0187                                              | 0.0435  | 0.0  | 0.5  | 0.2515    | nan |
  | 0.9481        | 4.0   | 60   | 1.0027          | 1.0020 | 1.0020                                                 | 0.8546 | 0.8546                                                | -0.4994 | -0.4994                                              | -0.3043 | 0.0  | 0.5  | 0.2579    | nan |
  | 0.8838        | 5.0   | 75   | 0.9352          | 0.9677 | 0.9677                                                 | 0.8463 | 0.8463                                                | -0.3985 | -0.3985                                              | -0.2174 | 0.0  | 0.5  | 0.2966    | nan |
  | 0.7971        | 6.0   | 90   | 0.9396          | 0.9700 | 0.9700                                                 | 0.8608 | 0.8608                                                | -0.4050 | -0.4050                                              | -0.2174 | 0.0  | 0.5  | 0.3156    | nan |
  | 0.8182        | 7.0   | 105  | 0.9485          | 0.9746 | 0.9746                                                 | 0.8509 | 0.8509                                                | -0.4184 | -0.4184                                              | -0.1304 | 0.0  | 0.5  | 0.2788    | nan |
  | 0.696         | 8.0   | 120  | 1.1396          | 1.0682 | 1.0682                                                 | 0.9309 | 0.9309                                                | -0.7041 | -0.7041                                              | -0.1304 | 0.0  | 0.5  | 0.2899    | nan |
  | 0.6337        | 9.0   | 135  | 1.3064          | 1.1437 | 1.1437                                                 | 0.9612 | 0.9612                                                | -0.9536 | -0.9536                                              | -0.3913 | 0.0  | 0.5  | 0.4018    | nan |
  | 0.5308        | 10.0  | 150  | 1.2403          | 1.1144 | 1.1144                                                 | 0.9359 | 0.9359                                                | -0.8547 | -0.8547                                              | -0.3913 | 0.0  | 0.5  | 0.4018    | nan |
  | 0.5226        | 11.0  | 165  | 1.3433          | 1.1597 | 1.1597                                                 | 0.9542 | 0.9542                                                | -1.0087 | -1.0087     