readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-focus-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-focus-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: 0.1927
  - Rmse: 0.5495
  - Rmse Focus::a Su un oggetto: 0.5495
  - Mae: 0.4174
  - Mae Focus::a Su un oggetto: 0.4174
  - R2: 0.5721
  - R2 Focus::a Su un oggetto: 0.5721
  - Cos: 0.5652
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.5518
  - 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 Focus::a Su un oggetto | Mae    | Mae Focus::a Su un oggetto | R2      | R2 Focus::a Su un oggetto | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0316        | 1.0   | 15   | 0.6428          | 1.0035 | 1.0035                      | 0.8806 | 0.8806                     | -0.4272 | -0.4272                   | -0.4783 | 0.0  | 0.5  | 0.5302    | nan |
  | 1.0005        | 2.0   | 30   | 0.4564          | 0.8456 | 0.8456                      | 0.7078 | 0.7078                     | -0.0134 | -0.0134                   | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
  | 0.9519        | 3.0   | 45   | 0.4151          | 0.8063 | 0.8063                      | 0.6797 | 0.6797                     | 0.0784  | 0.0784                    | 0.1304  | 0.0  | 0.5  | 0.4888    | nan |
  | 0.92          | 4.0   | 60   | 0.3982          | 0.7898 | 0.7898                      | 0.6516 | 0.6516                     | 0.1159  | 0.1159                    | 0.2174  | 0.0  | 0.5  | 0.5036    | nan |
  | 0.8454        | 5.0   | 75   | 0.2739          | 0.6550 | 0.6550                      | 0.5292 | 0.5292                     | 0.3919  | 0.3919                    | 0.6522  | 0.0  | 0.5  | 0.4160    | nan |
  | 0.7247        | 6.0   | 90   | 0.2413          | 0.6148 | 0.6148                      | 0.5347 | 0.5347                     | 0.4642  | 0.4642                    | 0.4783  | 0.0  | 0.5  | 0.3453    | nan |
  | 0.6055        | 7.0   | 105  | 0.3109          | 0.6978 | 0.6978                      | 0.6115 | 0.6115                     | 0.3098  | 0.3098                    | 0.4783  | 0.0  | 0.5  | 0.4154    | nan |
  | 0.5411        | 8.0   | 120  | 0.3932          | 0.7848 | 0.7848                      | 0.6712 | 0.6712                     | 0.1271  | 0.1271                    | 0.4783  | 0.0  | 0.5  | 0.4154    | nan |
  | 0.4784        | 9.0   | 135  | 0.1316          | 0.4540 | 0.4540                      | 0.3750 | 0.3750                     | 0.7079  | 0.7079                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.4039        | 10.0  | 150  | 0.2219          | 0.5896 | 0.5896                      | 0.4954 | 0.4954                     | 0.5074  | 0.5074                    | 0.5652  | 0.0  | 0.5  | 0.4838    | nan |
  | 0.3415        | 11.0  | 165  | 0.1935          | 0.5505 | 0.5505                      | 0.4443 | 0.4443                     | 0.5704  | 0.5704                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.3369        | 12.0  | 180  | 0.2118          | 0.5761 | 0.5761                      | 0.4554 | 0.4554                     | 0.5296  | 0.5296                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.3083        | 13.0  | 195  | 0.1928          | 0.5496 | 0.5496                      | 0.4368 | 0.4368                     | 0.5718  | 0.5718                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.2678        | 14.0  | 210  | 0.2205          | 0.5877 | 0.5877                      | 0.4472 | 0.4472                     | 0.5105  | 0.5105                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.2199        | 15.0  | 225  | 0.2118          | 0.5760 | 0.5760                      | 0.4689 | 0.4689                     | 0.5297  | 0.5297                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.2238        | 16.0  | 240  | 0.2461          | 0.6209 | 0.6209                      | 0.5047 | 0.5047                     | 0.4537  | 0.4537                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
  | 0.2233