LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-object
  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-object

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: 0.2271
- Rmse: 0.5965
- Rmse Focus::a Su un oggetto: 0.5965
- Mae: 0.4372
- Mae Focus::a Su un oggetto: 0.4372
- R2: 0.4957
- R2 Focus::a Su un oggetto: 0.4957
- Cos: 0.6522
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6622
- 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 Su un oggetto | Mae    | Mae Focus::a Su un oggetto | R2     | R2 Focus::a Su un oggetto | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0371        | 1.0   | 15   | 0.4358          | 0.8263 | 0.8263                      | 0.7132 | 0.7132                     | 0.0323 | 0.0323                    | 0.3043 | 0.0  | 0.5  | 0.3510    | nan |
| 0.9574        | 2.0   | 30   | 0.4420          | 0.8321 | 0.8321                      | 0.7175 | 0.7175                     | 0.0186 | 0.0186                    | 0.3043 | 0.0  | 0.5  | 0.4627    | nan |
| 0.9137        | 3.0   | 45   | 0.4208          | 0.8119 | 0.8119                      | 0.6955 | 0.6955                     | 0.0657 | 0.0657                    | 0.3913 | 0.0  | 0.5  | 0.3928    | nan |
| 0.8465        | 4.0   | 60   | 0.3356          | 0.7251 | 0.7251                      | 0.6237 | 0.6237                     | 0.2548 | 0.2548                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
| 0.6864        | 5.0   | 75   | 0.2876          | 0.6712 | 0.6712                      | 0.5624 | 0.5624                     | 0.3616 | 0.3616                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
| 0.5804        | 6.0   | 90   | 0.3148          | 0.7022 | 0.7022                      | 0.5577 | 0.5577                     | 0.3011 | 0.3011                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
| 0.4983        | 7.0   | 105  | 0.4068          | 0.7983 | 0.7983                      | 0.6606 | 0.6606                     | 0.0968 | 0.0968                    | 0.3913 | 0.0  | 0.5  | 0.4519    | nan |
| 0.3584        | 8.0   | 120  | 0.2567          | 0.6342 | 0.6342                      | 0.4883 | 0.4883                     | 0.4300 | 0.4300                    | 0.5652 | 0.0  | 0.5  | 0.6247    | nan |
| 0.2771        | 9.0   | 135  | 0.2130          | 0.5777 | 0.5777                      | 0.4193 | 0.4193                     | 0.5270 | 0.5270                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.2135        | 10.0  | 150  | 0.2522          | 0.6285 | 0.6285                      | 0.4572 | 0.4572                     | 0.4401 | 0.4401                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.1654        | 11.0  | 165  | 0.2662          | 0.6457 | 0.6457                      | 0.4603 | 0.4603                     | 0.4090 | 0.4090                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.1554        | 12.0  | 180  | 0.2459          | 0.6207 | 0.6207                      | 0.4778 | 0.4778                     | 0.4540 | 0.4540                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.1195        | 13.0  | 195  | 0.2385          | 0.6113 | 0.6113                      | 0.4618 | 0.4618                     | 0.4704 | 0.4704                    | 0.5652 | 0.0  | 0.5  | 0.5693    | nan |
| 0.1046        | 14.0  | 210  | 0.2296          | 0.5997 | 0.5997                      | 0.4544 | 0.4544                     | 0.4903 | 0.4903                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.089         | 15.0  | 225  | 0.2520          | 0.6283 | 0.6283                      | 0.4974 | 0.4974                     | 0.4404 | 0.4404                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.083         | 16.0  | 240  | 0.2297          | 0.5998 | 0.5998                      | 0.4635 | 0.4635                     | 0.4901 | 0.4901                    | 0.5652 | 0.0  | 0.5  | 0.5610    | nan |
| 0.0701        | 17.0  | 255  | 0.2207          | 0.5879 | 0.5879                      | 0.4442 | 0.4442                     | 0.5101 | 0.5101                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0585        | 18.0  | 270  | 0.2397          | 0.6128 | 0.6128                      | 0.4617 | 0.4617                     | 0.4678 | 0.4678                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0652        | 19.0  | 285  | 0.2284          | 0.5981 | 0.5981                      | 0.4449 | 0.4449                     | 0.4929 | 0.4929                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.059         | 20.0  | 300  | 0.2491          | 0.6247 | 0.6247                      | 0.4599 | 0.4599                     | 0.4469 | 0.4469                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0464        | 21.0  | 315  | 0.2306          | 0.6010 | 0.6010                      | 0.4373 | 0.4373                     | 0.4880 | 0.4880                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0529        | 22.0  | 330  | 0.2370          | 0.6093 | 0.6093                      | 0.4480 | 0.4480                     | 0.4738 | 0.4738                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0555        | 23.0  | 345  | 0.2361          | 0.6082 | 0.6082                      | 0.4474 | 0.4474                     | 0.4757 | 0.4757                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0447        | 24.0  | 360  | 0.2283          | 0.5980 | 0.5980                      | 0.4399 | 0.4399                     | 0.4932 | 0.4932                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.046         | 25.0  | 375  | 0.2259          | 0.5948 | 0.5948                      | 0.4413 | 0.4413                     | 0.4985 | 0.4985                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0379        | 26.0  | 390  | 0.2263          | 0.5953 | 0.5953                      | 0.4402 | 0.4402                     | 0.4977 | 0.4977                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0438        | 27.0  | 405  | 0.2270          | 0.5963 | 0.5963                      | 0.4378 | 0.4378                     | 0.4961 | 0.4961                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0354        | 28.0  | 420  | 0.2211          | 0.5886 | 0.5886                      | 0.4379 | 0.4379                     | 0.5090 | 0.5090                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0363        | 29.0  | 435  | 0.2269          | 0.5962 | 0.5962                      | 0.4362 | 0.4362                     | 0.4961 | 0.4961                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |
| 0.0451        | 30.0  | 450  | 0.2271          | 0.5965 | 0.5965                      | 0.4372 | 0.4372                     | 0.4957 | 0.4957                    | 0.6522 | 0.0  | 0.5  | 0.6622    | nan |


### Framework versions

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