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-cause-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-cause-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: 0.4120
- Rmse: 1.0345
- Rmse Cause::a Causata da un oggetto (es. una pistola): 1.0345
- Mae: 0.6181
- Mae Cause::a Causata da un oggetto (es. una pistola): 0.6181
- R2: 0.3837
- R2 Cause::a Causata da un oggetto (es. una pistola): 0.3837
- Cos: 0.9130
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.8986
- 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 Cause::a Causata da un oggetto (es. una pistola) | Mae    | Mae Cause::a Causata da un oggetto (es. una pistola) | R2      | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0824        | 1.0   | 15   | 0.6651          | 1.3143 | 1.3143                                                | 1.0930 | 1.0930                                               | 0.0052  | 0.0052                                              | 0.3043 | 0.0  | 0.5  | 0.4393    | nan |
| 0.9574        | 2.0   | 30   | 0.7088          | 1.3568 | 1.3568                                                | 1.1945 | 1.1945                                               | -0.0601 | -0.0601                                             | 0.0435 | 0.0  | 0.5  | 0.3380    | nan |
| 0.8151        | 3.0   | 45   | 0.6300          | 1.2791 | 1.2791                                                | 1.0206 | 1.0206                                               | 0.0577  | 0.0577                                              | 0.3043 | 0.0  | 0.5  | 0.3613    | nan |
| 0.6401        | 4.0   | 60   | 0.4871          | 1.1247 | 1.1247                                                | 0.7285 | 0.7285                                               | 0.2715  | 0.2715                                              | 0.5652 | 0.0  | 0.5  | 0.6424    | nan |
| 0.448         | 5.0   | 75   | 0.5005          | 1.1401 | 1.1401                                                | 0.7216 | 0.7216                                               | 0.2514  | 0.2514                                              | 0.4783 | 0.0  | 0.5  | 0.6077    | nan |
| 0.2893        | 6.0   | 90   | 0.4761          | 1.1119 | 1.1119                                                | 0.7237 | 0.7237                                               | 0.2879  | 0.2879                                              | 0.5652 | 0.0  | 0.5  | 0.6348    | nan |
| 0.174         | 7.0   | 105  | 0.4771          | 1.1131 | 1.1131                                                | 0.6836 | 0.6836                                               | 0.2865  | 0.2865                                              | 0.6522 | 0.0  | 0.5  | 0.6785    | nan |
| 0.1383        | 8.0   | 120  | 0.4313          | 1.0583 | 1.0583                                                | 0.6462 | 0.6462                                               | 0.3550  | 0.3550                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.1105        | 9.0   | 135  | 0.4660          | 1.1001 | 1.1001                                                | 0.6737 | 0.6737                                               | 0.3030  | 0.3030                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0903        | 10.0  | 150  | 0.4866          | 1.1241 | 1.1241                                                | 0.7192 | 0.7192                                               | 0.2723  | 0.2723                                              | 0.7391 | 0.0  | 0.5  | 0.6833    | nan |
| 0.0571        | 11.0  | 165  | 0.4361          | 1.0642 | 1.0642                                                | 0.6130 | 0.6130                                               | 0.3478  | 0.3478                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0623        | 12.0  | 180  | 0.4578          | 1.0904 | 1.0904                                                | 0.6844 | 0.6844                                               | 0.3152  | 0.3152                                              | 0.6522 | 0.0  | 0.5  | 0.6785    | nan |
| 0.0526        | 13.0  | 195  | 0.4605          | 1.0936 | 1.0936                                                | 0.6697 | 0.6697                                               | 0.3112  | 0.3112                                              | 0.6522 | 0.0  | 0.5  | 0.6785    | nan |
| 0.0472        | 14.0  | 210  | 0.4440          | 1.0738 | 1.0738                                                | 0.6589 | 0.6589                                               | 0.3360  | 0.3360                                              | 0.7391 | 0.0  | 0.5  | 0.7327    | nan |
| 0.0492        | 15.0  | 225  | 0.4593          | 1.0922 | 1.0922                                                | 0.6812 | 0.6812                                               | 0.3130  | 0.3130                                              | 0.7391 | 0.0  | 0.5  | 0.6833    | nan |
| 0.0389        | 16.0  | 240  | 0.4195          | 1.0437 | 1.0437                                                | 0.6252 | 0.6252                                               | 0.3726  | 0.3726                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0396        | 17.0  | 255  | 0.4087          | 1.0302 | 1.0302                                                | 0.6119 | 0.6119                                               | 0.3888  | 0.3888                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0328        | 18.0  | 270  | 0.4274          | 1.0535 | 1.0535                                                | 0.6457 | 0.6457                                               | 0.3608  | 0.3608                                              | 0.8261 | 0.0  | 0.5  | 0.7431    | nan |
| 0.0345        | 19.0  | 285  | 0.4306          | 1.0574 | 1.0574                                                | 0.6576 | 0.6576                                               | 0.3560  | 0.3560                                              | 0.8261 | 0.0  | 0.5  | 0.7431    | nan |
| 0.0328        | 20.0  | 300  | 0.4067          | 1.0277 | 1.0277                                                | 0.6160 | 0.6160                                               | 0.3918  | 0.3918                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0344        | 21.0  | 315  | 0.4056          | 1.0263 | 1.0263                                                | 0.5948 | 0.5948                                               | 0.3934  | 0.3934                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0312        | 22.0  | 330  | 0.4236          | 1.0488 | 1.0488                                                | 0.6277 | 0.6277                                               | 0.3665  | 0.3665                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0241        | 23.0  | 345  | 0.4272          | 1.0533 | 1.0533                                                | 0.6444 | 0.6444                                               | 0.3610  | 0.3610                                              | 0.8261 | 0.0  | 0.5  | 0.7431    | nan |
| 0.0302        | 24.0  | 360  | 0.4046          | 1.0250 | 1.0250                                                | 0.6030 | 0.6030                                               | 0.3949  | 0.3949                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0244        | 25.0  | 375  | 0.4194          | 1.0436 | 1.0436                                                | 0.6320 | 0.6320                                               | 0.3728  | 0.3728                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0259        | 26.0  | 390  | 0.4025          | 1.0224 | 1.0224                                                | 0.6009 | 0.6009                                               | 0.3980  | 0.3980                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0265        | 27.0  | 405  | 0.4103          | 1.0323 | 1.0323                                                | 0.6180 | 0.6180                                               | 0.3863  | 0.3863                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0184        | 28.0  | 420  | 0.4059          | 1.0268 | 1.0268                                                | 0.6046 | 0.6046                                               | 0.3929  | 0.3929                                              | 0.8261 | 0.0  | 0.5  | 0.7586    | nan |
| 0.0257        | 29.0  | 435  | 0.4088          | 1.0304 | 1.0304                                                | 0.6122 | 0.6122                                               | 0.3885  | 0.3885                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |
| 0.0262        | 30.0  | 450  | 0.4120          | 1.0345 | 1.0345                                                | 0.6181 | 0.6181                                               | 0.3837  | 0.3837                                              | 0.9130 | 0.0  | 0.5  | 0.8986    | nan |


### Framework versions

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