readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: predict-perception-xlmr-focus-assassin
    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-assassin
  
  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: 0.3264
  - Rmse: 0.9437
  - Rmse Focus::a Sull'assassino: 0.9437
  - Mae: 0.7093
  - Mae Focus::a Sull'assassino: 0.7093
  - R2: 0.6145
  - R2 Focus::a Sull'assassino: 0.6145
  - Cos: 0.7391
  - Pair: 0.0
  - Rank: 0.5
  - Neighbors: 0.6131
  - 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 Sull'assassino | Mae    | Mae Focus::a Sull'assassino | R2      | R2 Focus::a Sull'assassino | Cos     | Pair | Rank | Neighbors | Rsa |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:-------:|:----:|:----:|:---------:|:---:|
  | 1.0403        | 1.0   | 15   | 1.1576          | 1.7771 | 1.7771                       | 1.6028 | 1.6028                      | -0.3670 | -0.3670                    | -0.2174 | 0.0  | 0.5  | 0.2379    | nan |
  | 0.9818        | 2.0   | 30   | 0.8916          | 1.5596 | 1.5596                       | 1.4136 | 1.4136                      | -0.0529 | -0.0529                    | 0.3913  | 0.0  | 0.5  | 0.3793    | nan |
  | 0.9276        | 3.0   | 45   | 0.9277          | 1.5909 | 1.5909                       | 1.4560 | 1.4560                      | -0.0955 | -0.0955                    | 0.3913  | 0.0  | 0.5  | 0.3742    | nan |
  | 0.8395        | 4.0   | 60   | 0.7958          | 1.4734 | 1.4734                       | 1.3032 | 1.3032                      | 0.0603  | 0.0603                     | 0.5652  | 0.0  | 0.5  | 0.4598    | nan |
  | 0.7587        | 5.0   | 75   | 0.4647          | 1.1259 | 1.1259                       | 0.9316 | 0.9316                      | 0.4513  | 0.4513                     | 0.6522  | 0.0  | 0.5  | 0.5087    | nan |
  | 0.696         | 6.0   | 90   | 0.5368          | 1.2101 | 1.2101                       | 1.0847 | 1.0847                      | 0.3661  | 0.3661                     | 0.7391  | 0.0  | 0.5  | 0.5302    | nan |
  | 0.548         | 7.0   | 105  | 0.3110          | 0.9211 | 0.9211                       | 0.7896 | 0.7896                      | 0.6328  | 0.6328                     | 0.6522  | 0.0  | 0.5  | 0.5261    | nan |
  | 0.4371        | 8.0   | 120  | 0.3392          | 0.9619 | 0.9619                       | 0.8132 | 0.8132                      | 0.5995  | 0.5995                     | 0.6522  | 0.0  | 0.5  | 0.5261    | nan |
  | 0.355         | 9.0   | 135  | 0.3938          | 1.0366 | 1.0366                       | 0.8153 | 0.8153                      | 0.5349  | 0.5349                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.2919        | 10.0  | 150  | 0.3484          | 0.9749 | 0.9749                       | 0.7487 | 0.7487                      | 0.5886  | 0.5886                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.2595        | 11.0  | 165  | 0.2812          | 0.8759 | 0.8759                       | 0.6265 | 0.6265                      | 0.6679  | 0.6679                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.2368        | 12.0  | 180  | 0.2534          | 0.8314 | 0.8314                       | 0.6402 | 0.6402                      | 0.7008  | 0.7008                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.227         | 13.0  | 195  | 0.2878          | 0.8861 | 0.8861                       | 0.6769 | 0.6769                      | 0.6601  | 0.6601                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.1979        | 14.0  | 210  | 0.2405          | 0.8100 | 0.8100                       | 0.6113 | 0.6113                      | 0.7160  | 0.7160                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
  | 0.1622        | 15.0  | 225  | 0.2575          | 0.8382 | 0.8382                       | 0.6017 | 0.6017                      | 0.6959  | 0.6959                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
  | 0.1575        | 16.0  | 240  | 0.2945          | 0.8963 | 0.8963                       | 0.6741 | 0.6741                      | 0.6523  | 0.6523            