LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-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-blame-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: 0.4439
- Rmse: 0.9571
- Rmse Blame::a L'assassino: 0.9571
- Mae: 0.7260
- Mae Blame::a L'assassino: 0.7260
- R2: 0.6437
- R2 Blame::a L'assassino: 0.6437
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6287
- 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 Blame::a L'assassino | Mae    | Mae Blame::a L'assassino | R2     | R2 Blame::a L'assassino | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0317        | 1.0   | 15   | 1.1311          | 1.5278 | 1.5278                    | 1.3893 | 1.3893                   | 0.0919 | 0.0919                  | 0.5652 | 0.0  | 0.5  | 0.4512    | nan |
| 0.9475        | 2.0   | 30   | 1.0795          | 1.4926 | 1.4926                    | 1.3387 | 1.3387                   | 0.1334 | 0.1334                  | 0.8261 | 0.0  | 0.5  | 0.6184    | nan |
| 0.9146        | 3.0   | 45   | 1.1092          | 1.5130 | 1.5130                    | 1.4078 | 1.4078                   | 0.1095 | 0.1095                  | 0.4783 | 0.0  | 0.5  | 0.3116    | nan |
| 0.9539        | 4.0   | 60   | 1.1734          | 1.5561 | 1.5561                    | 1.4238 | 1.4238                   | 0.0580 | 0.0580                  | 0.3913 | 0.0  | 0.5  | 0.3614    | nan |
| 0.8665        | 5.0   | 75   | 0.8910          | 1.3560 | 1.3560                    | 1.2350 | 1.2350                   | 0.2847 | 0.2847                  | 0.5652 | 0.0  | 0.5  | 0.4136    | nan |
| 0.6564        | 6.0   | 90   | 0.8469          | 1.3220 | 1.3220                    | 1.1570 | 1.1570                   | 0.3201 | 0.3201                  | 0.3913 | 0.0  | 0.5  | 0.3931    | nan |
| 0.5241        | 7.0   | 105  | 0.6429          | 1.1519 | 1.1519                    | 0.9757 | 0.9757                   | 0.4838 | 0.4838                  | 0.5652 | 0.0  | 0.5  | 0.4222    | nan |
| 0.4589        | 8.0   | 120  | 0.5781          | 1.0923 | 1.0923                    | 0.8714 | 0.8714                   | 0.5359 | 0.5359                  | 0.6522 | 0.0  | 0.5  | 0.4641    | nan |
| 0.4043        | 9.0   | 135  | 0.4525          | 0.9664 | 0.9664                    | 0.8257 | 0.8257                   | 0.6367 | 0.6367                  | 0.5652 | 0.0  | 0.5  | 0.4263    | nan |
| 0.3498        | 10.0  | 150  | 0.4490          | 0.9627 | 0.9627                    | 0.8272 | 0.8272                   | 0.6395 | 0.6395                  | 0.6522 | 0.0  | 0.5  | 0.5144    | nan |
| 0.3505        | 11.0  | 165  | 0.3721          | 0.8763 | 0.8763                    | 0.7471 | 0.7471                   | 0.7013 | 0.7013                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.3426        | 12.0  | 180  | 0.4117          | 0.9218 | 0.9218                    | 0.7477 | 0.7477                   | 0.6695 | 0.6695                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.3074        | 13.0  | 195  | 0.3761          | 0.8810 | 0.8810                    | 0.7109 | 0.7109                   | 0.6981 | 0.6981                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.2261        | 14.0  | 210  | 0.3818          | 0.8877 | 0.8877                    | 0.7042 | 0.7042                   | 0.6935 | 0.6935                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.2399        | 15.0  | 225  | 0.3893          | 0.8964 | 0.8964                    | 0.7108 | 0.7108                   | 0.6874 | 0.6874                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.2014        | 16.0  | 240  | 0.4606          | 0.9750 | 0.9750                    | 0.8046 | 0.8046                   | 0.6302 | 0.6302                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1937        | 17.0  | 255  | 0.4549          | 0.9689 | 0.9689                    | 0.7679 | 0.7679                   | 0.6348 | 0.6348                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1831        | 18.0  | 270  | 0.4113          | 0.9213 | 0.9213                    | 0.6746 | 0.6746                   | 0.6698 | 0.6698                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1758        | 19.0  | 285  | 0.4154          | 0.9259 | 0.9259                    | 0.7053 | 0.7053                   | 0.6665 | 0.6665                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1577        | 20.0  | 300  | 0.3970          | 0.9051 | 0.9051                    | 0.7163 | 0.7163                   | 0.6813 | 0.6813                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1597        | 21.0  | 315  | 0.4199          | 0.9309 | 0.9309                    | 0.7270 | 0.7270                   | 0.6629 | 0.6629                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1145        | 22.0  | 330  | 0.4250          | 0.9365 | 0.9365                    | 0.6971 | 0.6971                   | 0.6588 | 0.6588                  | 0.8261 | 0.0  | 0.5  | 0.6594    | nan |
| 0.1349        | 23.0  | 345  | 0.4168          | 0.9275 | 0.9275                    | 0.7126 | 0.7126                   | 0.6654 | 0.6654                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1481        | 24.0  | 360  | 0.4421          | 0.9552 | 0.9552                    | 0.7441 | 0.7441                   | 0.6451 | 0.6451                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1188        | 25.0  | 375  | 0.4356          | 0.9481 | 0.9481                    | 0.7444 | 0.7444                   | 0.6503 | 0.6503                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1119        | 26.0  | 390  | 0.4456          | 0.9590 | 0.9590                    | 0.7139 | 0.7139                   | 0.6422 | 0.6422                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1282        | 27.0  | 405  | 0.4456          | 0.9589 | 0.9589                    | 0.7637 | 0.7637                   | 0.6423 | 0.6423                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.142         | 28.0  | 420  | 0.4501          | 0.9637 | 0.9637                    | 0.7146 | 0.7146                   | 0.6387 | 0.6387                  | 0.8261 | 0.0  | 0.5  | 0.6594    | nan |
| 0.126         | 29.0  | 435  | 0.4442          | 0.9575 | 0.9575                    | 0.7189 | 0.7189                   | 0.6433 | 0.6433                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |
| 0.1308        | 30.0  | 450  | 0.4439          | 0.9571 | 0.9571                    | 0.7260 | 0.7260                   | 0.6437 | 0.6437                  | 0.7391 | 0.0  | 0.5  | 0.6287    | nan |


### Framework versions

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