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

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: 0.7359
- Rmse: 0.6962
- Rmse Blame::a Un concetto astratto o un'emozione: 0.6962
- Mae: 0.5010
- Mae Blame::a Un concetto astratto o un'emozione: 0.5010
- R2: 0.3974
- R2 Blame::a Un concetto astratto o un'emozione: 0.3974
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5507
- 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 Un concetto astratto o un'emozione | Mae    | Mae Blame::a Un concetto astratto o un'emozione | R2      | R2 Blame::a Un concetto astratto o un'emozione | Cos     | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:-------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0979        | 1.0   | 15   | 1.2387          | 0.9033 | 0.9033                                           | 0.6603 | 0.6603                                          | -0.0144 | -0.0144                                        | 0.0435  | 0.0  | 0.5  | 0.3432    | nan |
| 1.0172        | 2.0   | 30   | 1.1498          | 0.8703 | 0.8703                                           | 0.5964 | 0.5964                                          | 0.0584  | 0.0584                                         | 0.0435  | 0.0  | 0.5  | 0.2935    | nan |
| 0.9879        | 3.0   | 45   | 1.2139          | 0.8942 | 0.8942                                           | 0.6197 | 0.6197                                          | 0.0060  | 0.0060                                         | 0.2174  | 0.0  | 0.5  | 0.4582    | nan |
| 0.9723        | 4.0   | 60   | 1.1152          | 0.8571 | 0.8571                                           | 0.5982 | 0.5982                                          | 0.0867  | 0.0867                                         | 0.2174  | 0.0  | 0.5  | 0.3921    | nan |
| 0.9584        | 5.0   | 75   | 1.0607          | 0.8358 | 0.8358                                           | 0.5959 | 0.5959                                          | 0.1314  | 0.1314                                         | 0.0435  | 0.0  | 0.5  | 0.4165    | nan |
| 0.9023        | 6.0   | 90   | 1.0031          | 0.8128 | 0.8128                                           | 0.5827 | 0.5827                                          | 0.1786  | 0.1786                                         | -0.0435 | 0.0  | 0.5  | 0.3862    | nan |
| 0.8745        | 7.0   | 105  | 0.9715          | 0.7999 | 0.7999                                           | 0.5796 | 0.5796                                          | 0.2044  | 0.2044                                         | 0.3043  | 0.0  | 0.5  | 0.3665    | nan |
| 0.8082        | 8.0   | 120  | 0.8984          | 0.7692 | 0.7692                                           | 0.5699 | 0.5699                                          | 0.2643  | 0.2643                                         | 0.1304  | 0.0  | 0.5  | 0.3390    | nan |
| 0.7475        | 9.0   | 135  | 0.8532          | 0.7497 | 0.7497                                           | 0.5849 | 0.5849                                          | 0.3013  | 0.3013                                         | 0.0435  | 0.0  | 0.5  | 0.3100    | nan |
| 0.6599        | 10.0  | 150  | 0.8737          | 0.7586 | 0.7586                                           | 0.5822 | 0.5822                                          | 0.2846  | 0.2846                                         | 0.3043  | 0.0  | 0.5  | 0.3830    | nan |
| 0.5867        | 11.0  | 165  | 0.8159          | 0.7331 | 0.7331                                           | 0.5752 | 0.5752                                          | 0.3318  | 0.3318                                         | 0.2174  | 0.0  | 0.5  | 0.4439    | nan |
| 0.5081        | 12.0  | 180  | 0.8367          | 0.7424 | 0.7424                                           | 0.6071 | 0.6071                                          | 0.3148  | 0.3148                                         | 0.0435  | 0.0  | 0.5  | 0.3561    | nan |
| 0.4801        | 13.0  | 195  | 0.8353          | 0.7417 | 0.7417                                           | 0.5567 | 0.5567                                          | 0.3160  | 0.3160                                         | 0.3913  | 0.0  | 0.5  | 0.5850    | nan |
| 0.3714        | 14.0  | 210  | 0.8050          | 0.7282 | 0.7282                                           | 0.5824 | 0.5824                                          | 0.3408  | 0.3408                                         | 0.1304  | 0.0  | 0.5  | 0.3975    | nan |
| 0.3306        | 15.0  | 225  | 0.7833          | 0.7183 | 0.7183                                           | 0.5570 | 0.5570                                          | 0.3585  | 0.3585                                         | 0.2174  | 0.0  | 0.5  | 0.4604    | nan |
| 0.2674        | 16.0  | 240  | 0.8148          | 0.7326 | 0.7326                                           | 0.5475 | 0.5475                                          | 0.3328  | 0.3328                                         | 0.3043  | 0.0  | 0.5  | 0.4891    | nan |
| 0.2129        | 17.0  | 255  | 0.8715          | 0.7576 | 0.7576                                           | 0.5537 | 0.5537                                          | 0.2863  | 0.2863                                         | 0.4783  | 0.0  | 0.5  | 0.5017    | nan |
| 0.1924        | 18.0  | 270  | 0.7944          | 0.7234 | 0.7234                                           | 0.5276 | 0.5276                                          | 0.3495  | 0.3495                                         | 0.4783  | 0.0  | 0.5  | 0.5797    | nan |
| 0.1984        | 19.0  | 285  | 0.7885          | 0.7207 | 0.7207                                           | 0.5208 | 0.5208                                          | 0.3543  | 0.3543                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.1623        | 20.0  | 300  | 0.7682          | 0.7113 | 0.7113                                           | 0.5132 | 0.5132                                          | 0.3709  | 0.3709                                         | 0.4783  | 0.0  | 0.5  | 0.5797    | nan |
| 0.1409        | 21.0  | 315  | 0.7653          | 0.7100 | 0.7100                                           | 0.5215 | 0.5215                                          | 0.3733  | 0.3733                                         | 0.3043  | 0.0  | 0.5  | 0.5415    | nan |
| 0.1386        | 22.0  | 330  | 0.7688          | 0.7116 | 0.7116                                           | 0.5124 | 0.5124                                          | 0.3704  | 0.3704                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.123         | 23.0  | 345  | 0.7756          | 0.7148 | 0.7148                                           | 0.5144 | 0.5144                                          | 0.3648  | 0.3648                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.1175        | 24.0  | 360  | 0.7423          | 0.6993 | 0.6993                                           | 0.5015 | 0.5015                                          | 0.3921  | 0.3921                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.1188        | 25.0  | 375  | 0.7255          | 0.6913 | 0.6913                                           | 0.5063 | 0.5063                                          | 0.4059  | 0.4059                                         | 0.2174  | 0.0  | 0.5  | 0.4604    | nan |
| 0.1155        | 26.0  | 390  | 0.7635          | 0.7091 | 0.7091                                           | 0.5083 | 0.5083                                          | 0.3748  | 0.3748                                         | 0.4783  | 0.0  | 0.5  | 0.5797    | nan |
| 0.0981        | 27.0  | 405  | 0.7128          | 0.6852 | 0.6852                                           | 0.5020 | 0.5020                                          | 0.4163  | 0.4163                                         | 0.3043  | 0.0  | 0.5  | 0.5415    | nan |
| 0.1109        | 28.0  | 420  | 0.7430          | 0.6996 | 0.6996                                           | 0.5023 | 0.5023                                          | 0.3915  | 0.3915                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.1081        | 29.0  | 435  | 0.7367          | 0.6966 | 0.6966                                           | 0.5007 | 0.5007                                          | 0.3967  | 0.3967                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |
| 0.0953        | 30.0  | 450  | 0.7359          | 0.6962 | 0.6962                                           | 0.5010 | 0.5010                                          | 0.3974  | 0.3974                                         | 0.3913  | 0.0  | 0.5  | 0.5507    | nan |


### Framework versions

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