pytorch tensorboard bert text-classification transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-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-bert-focus-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: 0.2466
- Rmse: 0.6201
- Rmse Focus::a Sulla vittima: 0.6201
- Mae: 0.4936
- Mae Focus::a Sulla vittima: 0.4936
- R2: 0.7293
- R2 Focus::a Sulla vittima: 0.7293
- Cos: 0.8261
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.8155
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0247        | 1.0   | 15   | 1.0286          | 1.2665 | 1.2665                      | 1.0280 | 1.0280                     | -0.1292 | -0.1292                   | 0.1304 | 0.0  | 0.5  | 0.3685    | nan |
| 0.9912        | 2.0   | 30   | 1.0039          | 1.2512 | 1.2512                      | 1.0347 | 1.0347                     | -0.1020 | -0.1020                   | 0.0435 | 0.0  | 0.5  | 0.3333    | nan |
| 0.9147        | 3.0   | 45   | 0.9338          | 1.2067 | 1.2067                      | 0.9770 | 0.9770                     | -0.0251 | -0.0251                   | 0.1304 | 0.0  | 0.5  | 0.3685    | nan |
| 0.8194        | 4.0   | 60   | 0.7641          | 1.0916 | 1.0916                      | 0.8476 | 0.8476                     | 0.1612  | 0.1612                    | 0.4783 | 0.0  | 0.5  | 0.5284    | nan |
| 0.6636        | 5.0   | 75   | 0.6618          | 1.0159 | 1.0159                      | 0.8012 | 0.8012                     | 0.2735  | 0.2735                    | 0.6522 | 0.0  | 0.5  | 0.4741    | nan |
| 0.523         | 6.0   | 90   | 0.5176          | 0.8984 | 0.8984                      | 0.7044 | 0.7044                     | 0.4318  | 0.4318                    | 0.6522 | 0.0  | 0.5  | 0.4741    | nan |
| 0.402         | 7.0   | 105  | 0.3804          | 0.7702 | 0.7702                      | 0.6042 | 0.6042                     | 0.5824  | 0.5824                    | 0.6522 | 0.0  | 0.5  | 0.5395    | nan |
| 0.3401        | 8.0   | 120  | 0.3594          | 0.7487 | 0.7487                      | 0.5703 | 0.5703                     | 0.6054  | 0.6054                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.2615        | 9.0   | 135  | 0.3429          | 0.7312 | 0.7312                      | 0.6049 | 0.6049                     | 0.6236  | 0.6236                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.1928        | 10.0  | 150  | 0.2889          | 0.6712 | 0.6712                      | 0.5487 | 0.5487                     | 0.6828  | 0.6828                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.1703        | 11.0  | 165  | 0.2675          | 0.6458 | 0.6458                      | 0.5188 | 0.5188                     | 0.7064  | 0.7064                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.1209        | 12.0  | 180  | 0.2826          | 0.6639 | 0.6639                      | 0.5475 | 0.5475                     | 0.6897  | 0.6897                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.1428        | 13.0  | 195  | 0.2978          | 0.6815 | 0.6815                      | 0.5777 | 0.5777                     | 0.6731  | 0.6731                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.1038        | 14.0  | 210  | 0.2924          | 0.6753 | 0.6753                      | 0.5865 | 0.5865                     | 0.6790  | 0.6790                    | 0.6522 | 0.0  | 0.5  | 0.2760    | nan |
| 0.0951        | 15.0  | 225  | 0.2905          | 0.6731 | 0.6731                      | 0.5750 | 0.5750                     | 0.6811  | 0.6811                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.0809        | 16.0  | 240  | 0.2676          | 0.6460 | 0.6460                      | 0.5552 | 0.5552                     | 0.7062  | 0.7062                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.0811        | 17.0  | 255  | 0.2770          | 0.6572 | 0.6572                      | 0.5543 | 0.5543                     | 0.6959  | 0.6959                    | 0.7391 | 0.0  | 0.5  | 0.6920    | nan |
| 0.0703        | 18.0  | 270  | 0.2634          | 0.6409 | 0.6409                      | 0.5251 | 0.5251                     | 0.7108  | 0.7108                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0595        | 19.0  | 285  | 0.2638          | 0.6413 | 0.6413                      | 0.5196 | 0.5196                     | 0.7104  | 0.7104                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0651        | 20.0  | 300  | 0.2520          | 0.6268 | 0.6268                      | 0.4970 | 0.4970                     | 0.7234  | 0.7234                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0637        | 21.0  | 315  | 0.2668          | 0.6451 | 0.6451                      | 0.4965 | 0.4965                     | 0.7071  | 0.7071                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0582        | 22.0  | 330  | 0.2455          | 0.6188 | 0.6188                      | 0.4759 | 0.4759                     | 0.7305  | 0.7305                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0616        | 23.0  | 345  | 0.2509          | 0.6255 | 0.6255                      | 0.5084 | 0.5084                     | 0.7246  | 0.7246                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0492        | 24.0  | 360  | 0.2510          | 0.6256 | 0.6256                      | 0.4985 | 0.4985                     | 0.7244  | 0.7244                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0504        | 25.0  | 375  | 0.2512          | 0.6259 | 0.6259                      | 0.4849 | 0.4849                     | 0.7242  | 0.7242                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0501        | 26.0  | 390  | 0.2585          | 0.6350 | 0.6350                      | 0.5140 | 0.5140                     | 0.7162  | 0.7162                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0411        | 27.0  | 405  | 0.2544          | 0.6299 | 0.6299                      | 0.5148 | 0.5148                     | 0.7207  | 0.7207                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.044         | 28.0  | 420  | 0.2466          | 0.6201 | 0.6201                      | 0.4964 | 0.4964                     | 0.7293  | 0.7293                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.042         | 29.0  | 435  | 0.2466          | 0.6201 | 0.6201                      | 0.4836 | 0.4836                     | 0.7293  | 0.7293                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |
| 0.0446        | 30.0  | 450  | 0.2466          | 0.6201 | 0.6201                      | 0.4936 | 0.4936                     | 0.7293  | 0.7293                    | 0.8261 | 0.0  | 0.5  | 0.8155    | nan |


### Framework versions

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