readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-cause-object
    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-object
  
  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: 0.3069
  - Rmse: 0.8927
  - Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927
  - Mae: 0.5854
  - Mae Cause::a Causata da un oggetto (es. una pistola): 0.5854
  - R2: 0.5410
  - R2 Cause::a Causata da un oggetto (es. una pistola): 0.5410
  - Cos: 0.4783
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.6177
  - 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 oggetto (es. una pistola) | Mae    | Mae Cause::a Causata da un oggetto (es. una pistola) | R2      | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0329        | 1.0   | 15   | 0.8168          | 1.4564 | 1.4564                                                | 1.2947 | 1.2947                                               | -0.2216 | -0.2216                                             | -0.5652 | 0.0  | 0.5  | 0.5993    | nan |
  | 1.0096        | 2.0   | 30   | 0.7432          | 1.3893 | 1.3893                                                | 1.1883 | 1.1883                                               | -0.1116 | -0.1116                                             | -0.3913 | 0.0  | 0.5  | 0.6499    | nan |
  | 0.9323        | 3.0   | 45   | 0.6879          | 1.3366 | 1.3366                                                | 1.1054 | 1.1054                                               | -0.0289 | -0.0289                                             | -0.1304 | 0.0  | 0.5  | 0.5471    | nan |
  | 0.8636        | 4.0   | 60   | 0.6378          | 1.2870 | 1.2870                                                | 1.0477 | 1.0477                                               | 0.0461  | 0.0461                                              | 0.2174  | 0.0  | 0.5  | 0.3007    | nan |
  | 0.8041        | 5.0   | 75   | 0.5494          | 1.1945 | 1.1945                                                | 0.9499 | 0.9499                                               | 0.1783  | 0.1783                                              | 0.6522  | 0.0  | 0.5  | 0.6695    | nan |
  | 0.7413        | 6.0   | 90   | 0.5526          | 1.1980 | 1.1980                                                | 0.9503 | 0.9503                                               | 0.1735  | 0.1735                                              | 0.5652  | 0.0  | 0.5  | 0.3898    | nan |
  | 0.6397        | 7.0   | 105  | 0.4726          | 1.1078 | 1.1078                                                | 0.7826 | 0.7826                                               | 0.2932  | 0.2932                                              | 0.5652  | 0.0  | 0.5  | 0.3257    | nan |
  | 0.5556        | 8.0   | 120  | 0.7728          | 1.4167 | 1.4167                                                | 1.1528 | 1.1528                                               | -0.1558 | -0.1558                                             | 0.1304  | 0.0  | 0.5  | 0.4027    | nan |
  | 0.4972        | 9.0   | 135  | 0.4375          | 1.0659 | 1.0659                                                | 0.7577 | 0.7577                                               | 0.3457  | 0.3457                                              | 0.5652  | 0.0  | 0.5  | 0.5683    | nan |
  | 0.3691        | 10.0  | 150  | 0.4990          | 1.1383 | 1.1383                                                | 0.8272 | 0.8272                                               | 0.2537  | 0.2537                                              | 0.4783  | 0.0  | 0.5  | 0.4781    | nan |
  | 0.3381        | 11.0  | 165  | 0.4401          | 1.0690 | 1.0690                                                | 0.7319 | 0.7319                                               | 0.3418  | 0.3418                                              