LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-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-blame-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: 1.1098
- Rmse: 0.6801
- Rmse Blame::a La vittima: 0.6801
- Mae: 0.5617
- Mae Blame::a La vittima: 0.5617
- R2: -1.5910
- R2 Blame::a La vittima: -1.5910
- Cos: -0.1304
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3333
- 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 La vittima | Mae    | Mae Blame::a La vittima | R2      | R2 Blame::a La vittima | Cos     | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0422        | 1.0   | 15   | 0.4952          | 0.4542 | 0.4542                   | 0.4095 | 0.4095                  | -0.1560 | -0.1560                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
| 1.0434        | 2.0   | 30   | 0.4851          | 0.4496 | 0.4496                   | 0.4054 | 0.4054                  | -0.1324 | -0.1324                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
| 1.038         | 3.0   | 45   | 0.4513          | 0.4337 | 0.4337                   | 0.3885 | 0.3885                  | -0.0536 | -0.0536                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
| 1.0151        | 4.0   | 60   | 0.4395          | 0.4280 | 0.4280                   | 0.3840 | 0.3840                  | -0.0262 | -0.0262                | -0.1304 | 0.0  | 0.5  | 0.2715    | nan |
| 0.9727        | 5.0   | 75   | 0.4490          | 0.4325 | 0.4325                   | 0.3811 | 0.3811                  | -0.0482 | -0.0482                | 0.2174  | 0.0  | 0.5  | 0.3338    | nan |
| 0.9733        | 6.0   | 90   | 0.4540          | 0.4349 | 0.4349                   | 0.3860 | 0.3860                  | -0.0598 | -0.0598                | -0.2174 | 0.0  | 0.5  | 0.3248    | nan |
| 0.9396        | 7.0   | 105  | 0.4501          | 0.4331 | 0.4331                   | 0.3849 | 0.3849                  | -0.0508 | -0.0508                | 0.0435  | 0.0  | 0.5  | 0.2609    | nan |
| 0.8759        | 8.0   | 120  | 0.4597          | 0.4377 | 0.4377                   | 0.3849 | 0.3849                  | -0.0731 | -0.0731                | 0.3043  | 0.0  | 0.5  | 0.3898    | nan |
| 0.8768        | 9.0   | 135  | 0.4575          | 0.4366 | 0.4366                   | 0.3784 | 0.3784                  | -0.0680 | -0.0680                | 0.4783  | 0.0  | 0.5  | 0.4615    | nan |
| 0.8312        | 10.0  | 150  | 0.5363          | 0.4727 | 0.4727                   | 0.4071 | 0.4071                  | -0.2520 | -0.2520                | -0.0435 | 0.0  | 0.5  | 0.2733    | nan |
| 0.7296        | 11.0  | 165  | 0.5291          | 0.4696 | 0.4696                   | 0.4057 | 0.4057                  | -0.2353 | -0.2353                | 0.3043  | 0.0  | 0.5  | 0.3898    | nan |
| 0.7941        | 12.0  | 180  | 0.5319          | 0.4708 | 0.4708                   | 0.4047 | 0.4047                  | -0.2417 | -0.2417                | 0.1304  | 0.0  | 0.5  | 0.3381    | nan |
| 0.6486        | 13.0  | 195  | 0.6787          | 0.5318 | 0.5318                   | 0.4516 | 0.4516                  | -0.5846 | -0.5846                | 0.1304  | 0.0  | 0.5  | 0.3381    | nan |
| 0.6241        | 14.0  | 210  | 1.0146          | 0.6502 | 0.6502                   | 0.5580 | 0.5580                  | -1.3687 | -1.3687                | -0.1304 | 0.0  | 0.5  | 0.3509    | nan |
| 0.5868        | 15.0  | 225  | 0.7164          | 0.5464 | 0.5464                   | 0.4682 | 0.4682                  | -0.6725 | -0.6725                | -0.0435 | 0.0  | 0.5  | 0.3333    | nan |
| 0.5305        | 16.0  | 240  | 0.9064          | 0.6146 | 0.6146                   | 0.5173 | 0.5173                  | -1.1161 | -1.1161                | -0.0435 | 0.0  | 0.5  | 0.3333    | nan |
| 0.495         | 17.0  | 255  | 1.3860          | 0.7600 | 0.7600                   | 0.6433 | 0.6433                  | -2.2358 | -2.2358                | -0.0435 | 0.0  | 0.5  | 0.2935    | nan |
| 0.566         | 18.0  | 270  | 0.7618          | 0.5634 | 0.5634                   | 0.4730 | 0.4730                  | -0.7785 | -0.7785                | 0.0435  | 0.0  | 0.5  | 0.3225    | nan |
| 0.4305        | 19.0  | 285  | 0.8849          | 0.6072 | 0.6072                   | 0.5048 | 0.5048                  | -1.0659 | -1.0659                | -0.0435 | 0.0  | 0.5  | 0.3333    | nan |
| 0.5108        | 20.0  | 300  | 0.7376          | 0.5544 | 0.5544                   | 0.4716 | 0.4716                  | -0.7220 | -0.7220                | 0.0435  | 0.0  | 0.5  | 0.3225    | nan |
| 0.44          | 21.0  | 315  | 1.1611          | 0.6956 | 0.6956                   | 0.5921 | 0.5921                  | -1.7108 | -1.7108                | -0.1304 | 0.0  | 0.5  | 0.3333    | nan |
| 0.395         | 22.0  | 330  | 1.3004          | 0.7361 | 0.7361                   | 0.6078 | 0.6078                  | -2.0360 | -2.0360                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.3945        | 23.0  | 345  | 0.9376          | 0.6251 | 0.6251                   | 0.5272 | 0.5272                  | -1.1890 | -1.1890                | -0.2174 | 0.0  | 0.5  | 0.3188    | nan |
| 0.3093        | 24.0  | 360  | 1.3586          | 0.7524 | 0.7524                   | 0.6219 | 0.6219                  | -2.1719 | -2.1719                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.2676        | 25.0  | 375  | 1.2200          | 0.7130 | 0.7130                   | 0.5994 | 0.5994                  | -1.8484 | -1.8484                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.3257        | 26.0  | 390  | 1.2235          | 0.7140 | 0.7140                   | 0.5900 | 0.5900                  | -1.8564 | -1.8564                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.4004        | 27.0  | 405  | 1.0978          | 0.6763 | 0.6763                   | 0.5624 | 0.5624                  | -1.5629 | -1.5629                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.283         | 28.0  | 420  | 1.1454          | 0.6909 | 0.6909                   | 0.5697 | 0.5697                  | -1.6742 | -1.6742                | -0.2174 | 0.0  | 0.5  | 0.3587    | nan |
| 0.3326        | 29.0  | 435  | 1.1214          | 0.6836 | 0.6836                   | 0.5646 | 0.5646                  | -1.6181 | -1.6181                | -0.1304 | 0.0  | 0.5  | 0.3333    | nan |
| 0.2632        | 30.0  | 450  | 1.1098          | 0.6801 | 0.6801                   | 0.5617 | 0.5617                  | -1.5910 | -1.5910                | -0.1304 | 0.0  | 0.5  | 0.3333    | nan |


### Framework versions

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