LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-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-bert-blame-victim

This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5075
- Rmse: 0.4599
- Rmse Blame::a La vittima: 0.4599
- Mae: 0.3607
- Mae Blame::a La vittima: 0.3607
- R2: -0.1848
- R2 Blame::a La vittima: -0.1848
- Cos: 0.2174
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.2924
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0264        | 1.0   | 15   | 0.4334          | 0.4250 | 0.4250                   | 0.3666 | 0.3666                  | -0.0119 | -0.0119                | 0.1304  | 0.0  | 0.5  | 0.2703    | nan |
| 0.9814        | 2.0   | 30   | 0.4505          | 0.4333 | 0.4333                   | 0.3744 | 0.3744                  | -0.0517 | -0.0517                | 0.2174  | 0.0  | 0.5  | 0.2751    | nan |
| 0.9283        | 3.0   | 45   | 0.4349          | 0.4257 | 0.4257                   | 0.3627 | 0.3627                  | -0.0152 | -0.0152                | 0.1304  | 0.0  | 0.5  | 0.2779    | nan |
| 0.8904        | 4.0   | 60   | 0.4662          | 0.4408 | 0.4408                   | 0.3773 | 0.3773                  | -0.0884 | -0.0884                | -0.0435 | 0.0  | 0.5  | 0.2681    | nan |
| 0.836         | 5.0   | 75   | 0.4188          | 0.4177 | 0.4177                   | 0.3609 | 0.3609                  | 0.0223  | 0.0223                 | 0.2174  | 0.0  | 0.5  | 0.3051    | nan |
| 0.8293        | 6.0   | 90   | 0.4142          | 0.4155 | 0.4155                   | 0.3512 | 0.3512                  | 0.0330  | 0.0330                 | 0.2174  | 0.0  | 0.5  | 0.3220    | nan |
| 0.7629        | 7.0   | 105  | 0.3837          | 0.3999 | 0.3999                   | 0.3387 | 0.3387                  | 0.1041  | 0.1041                 | 0.2174  | 0.0  | 0.5  | 0.3051    | nan |
| 0.7266        | 8.0   | 120  | 0.3664          | 0.3907 | 0.3907                   | 0.3250 | 0.3250                  | 0.1446  | 0.1446                 | 0.3043  | 0.0  | 0.5  | 0.3409    | nan |
| 0.6121        | 9.0   | 135  | 0.3718          | 0.3936 | 0.3936                   | 0.3312 | 0.3312                  | 0.1320  | 0.1320                 | 0.3043  | 0.0  | 0.5  | 0.3983    | nan |
| 0.5694        | 10.0  | 150  | 0.3679          | 0.3915 | 0.3915                   | 0.3197 | 0.3197                  | 0.1411  | 0.1411                 | 0.3913  | 0.0  | 0.5  | 0.3518    | nan |
| 0.4647        | 11.0  | 165  | 0.3868          | 0.4015 | 0.4015                   | 0.3340 | 0.3340                  | 0.0970  | 0.0970                 | 0.2174  | 0.0  | 0.5  | 0.3285    | nan |
| 0.4212        | 12.0  | 180  | 0.3717          | 0.3936 | 0.3936                   | 0.3188 | 0.3188                  | 0.1322  | 0.1322                 | 0.3913  | 0.0  | 0.5  | 0.3518    | nan |
| 0.3605        | 13.0  | 195  | 0.3437          | 0.3784 | 0.3784                   | 0.3066 | 0.3066                  | 0.1976  | 0.1976                 | 0.3043  | 0.0  | 0.5  | 0.3423    | nan |
| 0.2759        | 14.0  | 210  | 0.3892          | 0.4027 | 0.4027                   | 0.3230 | 0.3230                  | 0.0914  | 0.0914                 | 0.3913  | 0.0  | 0.5  | 0.3518    | nan |
| 0.2868        | 15.0  | 225  | 0.3720          | 0.3937 | 0.3937                   | 0.3218 | 0.3218                  | 0.1315  | 0.1315                 | 0.3913  | 0.0  | 0.5  | 0.3440    | nan |
| 0.2467        | 16.0  | 240  | 0.3881          | 0.4022 | 0.4022                   | 0.3291 | 0.3291                  | 0.0939  | 0.0939                 | 0.3043  | 0.0  | 0.5  | 0.3363    | nan |
| 0.2013        | 17.0  | 255  | 0.4121          | 0.4144 | 0.4144                   | 0.3373 | 0.3373                  | 0.0380  | 0.0380                 | 0.3043  | 0.0  | 0.5  | 0.3363    | nan |
| 0.1966        | 18.0  | 270  | 0.4808          | 0.4476 | 0.4476                   | 0.3506 | 0.3506                  | -0.1224 | -0.1224                | 0.3913  | 0.0  | 0.5  | 0.3214    | nan |
| 0.177         | 19.0  | 285  | 0.4263          | 0.4215 | 0.4215                   | 0.3398 | 0.3398                  | 0.0046  | 0.0046                 | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1589        | 20.0  | 300  | 0.4274          | 0.4220 | 0.4220                   | 0.3363 | 0.3363                  | 0.0022  | 0.0022                 | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1488        | 21.0  | 315  | 0.4548          | 0.4353 | 0.4353                   | 0.3431 | 0.3431                  | -0.0618 | -0.0618                | 0.3043  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1428        | 22.0  | 330  | 0.4405          | 0.4285 | 0.4285                   | 0.3417 | 0.3417                  | -0.0285 | -0.0285                | 0.3043  | 0.0  | 0.5  | 0.3363    | nan |
| 0.1294        | 23.0  | 345  | 0.4955          | 0.4544 | 0.4544                   | 0.3565 | 0.3565                  | -0.1568 | -0.1568                | 0.3913  | 0.0  | 0.5  | 0.3440    | nan |
| 0.1291        | 24.0  | 360  | 0.4861          | 0.4501 | 0.4501                   | 0.3529 | 0.3529                  | -0.1348 | -0.1348                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1187        | 25.0  | 375  | 0.4752          | 0.4450 | 0.4450                   | 0.3518 | 0.3518                  | -0.1095 | -0.1095                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1141        | 26.0  | 390  | 0.5131          | 0.4624 | 0.4624                   | 0.3598 | 0.3598                  | -0.1978 | -0.1978                | 0.3043  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1094        | 27.0  | 405  | 0.4863          | 0.4502 | 0.4502                   | 0.3547 | 0.3547                  | -0.1353 | -0.1353                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.0925        | 28.0  | 420  | 0.4900          | 0.4519 | 0.4519                   | 0.3564 | 0.3564                  | -0.1439 | -0.1439                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.108         | 29.0  | 435  | 0.5019          | 0.4573 | 0.4573                   | 0.3590 | 0.3590                  | -0.1719 | -0.1719                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |
| 0.1054        | 30.0  | 450  | 0.5075          | 0.4599 | 0.4599                   | 0.3607 | 0.3607                  | -0.1848 | -0.1848                | 0.2174  | 0.0  | 0.5  | 0.2924    | nan |


### Framework versions

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