LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-victim
  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-victim

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: 0.2546
- Rmse: 0.6301
- Rmse Focus::a Sulla vittima: 0.6301
- Mae: 0.5441
- Mae Focus::a Sulla vittima: 0.5441
- R2: 0.7205
- R2 Focus::a Sulla vittima: 0.7205
- Cos: 0.8261
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.7802
- 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 Sulla vittima | Mae    | Mae Focus::a Sulla vittima | R2      | R2 Focus::a Sulla vittima | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0607        | 1.0   | 15   | 0.9261          | 1.2017 | 1.2017                      | 0.9557 | 0.9557                     | -0.0166 | -0.0166                   | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
| 1.0107        | 2.0   | 30   | 0.9481          | 1.2159 | 1.2159                      | 0.9861 | 0.9861                     | -0.0408 | -0.0408                   | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
| 0.9921        | 3.0   | 45   | 0.9068          | 1.1892 | 1.1892                      | 0.9548 | 0.9548                     | 0.0045  | 0.0045                    | 0.4783 | 0.0  | 0.5  | 0.6332    | nan |
| 0.7769        | 4.0   | 60   | 0.5014          | 0.8842 | 0.8842                      | 0.7121 | 0.7121                     | 0.4496  | 0.4496                    | 0.7391 | 0.0  | 0.5  | 0.6232    | nan |
| 0.5763        | 5.0   | 75   | 0.4019          | 0.7917 | 0.7917                      | 0.6737 | 0.6737                     | 0.5588  | 0.5588                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.4378        | 6.0   | 90   | 0.3594          | 0.7486 | 0.7486                      | 0.5957 | 0.5957                     | 0.6055  | 0.6055                    | 0.7391 | 0.0  | 0.5  | 0.4442    | nan |
| 0.3595        | 7.0   | 105  | 0.3452          | 0.7337 | 0.7337                      | 0.6333 | 0.6333                     | 0.6210  | 0.6210                    | 0.5652 | 0.0  | 0.5  | 0.2649    | nan |
| 0.3192        | 8.0   | 120  | 0.3275          | 0.7147 | 0.7147                      | 0.6205 | 0.6205                     | 0.6405  | 0.6405                    | 0.7391 | 0.0  | 0.5  | 0.6561    | nan |
| 0.2482        | 9.0   | 135  | 0.2978          | 0.6815 | 0.6815                      | 0.5754 | 0.5754                     | 0.6731  | 0.6731                    | 0.7391 | 0.0  | 0.5  | 0.6715    | nan |
| 0.2416        | 10.0  | 150  | 0.3018          | 0.6860 | 0.6860                      | 0.5954 | 0.5954                     | 0.6687  | 0.6687                    | 0.5652 | 0.0  | 0.5  | 0.2553    | nan |
| 0.2292        | 11.0  | 165  | 0.2764          | 0.6565 | 0.6565                      | 0.5522 | 0.5522                     | 0.6966  | 0.6966                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
| 0.1752        | 12.0  | 180  | 0.3070          | 0.6920 | 0.6920                      | 0.5680 | 0.5680                     | 0.6629  | 0.6629                    | 0.7391 | 0.0  | 0.5  | 0.6715    | nan |
| 0.1956        | 13.0  | 195  | 0.2923          | 0.6752 | 0.6752                      | 0.5499 | 0.5499                     | 0.6791  | 0.6791                    | 0.8261 | 0.0  | 0.5  | 0.7843    | nan |
| 0.1424        | 14.0  | 210  | 0.3163          | 0.7023 | 0.7023                      | 0.6060 | 0.6060                     | 0.6528  | 0.6528                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
| 0.152         | 15.0  | 225  | 0.2436          | 0.6164 | 0.6164                      | 0.5127 | 0.5127                     | 0.7326  | 0.7326                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
| 0.1277        | 16.0  | 240  | 0.2471          | 0.6208 | 0.6208                      | 0.5367 | 0.5367                     | 0.7287  | 0.7287                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.1269        | 17.0  | 255  | 0.2573          | 0.6334 | 0.6334                      | 0.5329 | 0.5329                     | 0.7175  | 0.7175                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.1058        | 18.0  | 270  | 0.2538          | 0.6291 | 0.6291                      | 0.5530 | 0.5530                     | 0.7214  | 0.7214                    | 0.7391 | 0.0  | 0.5  | 0.2347    | nan |
| 0.107         | 19.0  | 285  | 0.2568          | 0.6328 | 0.6328                      | 0.5464 | 0.5464                     | 0.7181  | 0.7181                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.1185        | 20.0  | 300  | 0.2452          | 0.6183 | 0.6183                      | 0.5317 | 0.5317                     | 0.7309  | 0.7309                    | 0.7391 | 0.0  | 0.5  | 0.2347    | nan |
| 0.1029        | 21.0  | 315  | 0.2419          | 0.6142 | 0.6142                      | 0.5415 | 0.5415                     | 0.7344  | 0.7344                    | 0.7391 | 0.0  | 0.5  | 0.2347    | nan |
| 0.0908        | 22.0  | 330  | 0.2462          | 0.6196 | 0.6196                      | 0.5261 | 0.5261                     | 0.7297  | 0.7297                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.0901        | 23.0  | 345  | 0.2528          | 0.6279 | 0.6279                      | 0.5330 | 0.5330                     | 0.7225  | 0.7225                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.0979        | 24.0  | 360  | 0.2800          | 0.6607 | 0.6607                      | 0.5682 | 0.5682                     | 0.6927  | 0.6927                    | 0.9130 | 0.0  | 0.5  | 0.8408    | nan |
| 0.0992        | 25.0  | 375  | 0.2502          | 0.6246 | 0.6246                      | 0.5517 | 0.5517                     | 0.7254  | 0.7254                    | 0.6522 | 0.0  | 0.5  | 0.2372    | nan |
| 0.0846        | 26.0  | 390  | 0.2570          | 0.6331 | 0.6331                      | 0.5524 | 0.5524                     | 0.7178  | 0.7178                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.0717        | 27.0  | 405  | 0.2562          | 0.6321 | 0.6321                      | 0.5456 | 0.5456                     | 0.7187  | 0.7187                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.0739        | 28.0  | 420  | 0.2570          | 0.6330 | 0.6330                      | 0.5471 | 0.5471                     | 0.7179  | 0.7179                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.0828        | 29.0  | 435  | 0.2553          | 0.6309 | 0.6309                      | 0.5446 | 0.5446                     | 0.7198  | 0.7198                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |
| 0.086         | 30.0  | 450  | 0.2546          | 0.6301 | 0.6301                      | 0.5441 | 0.5441                     | 0.7205  | 0.7205                    | 0.8261 | 0.0  | 0.5  | 0.7802    | nan |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
