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-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-cause-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: 0.4044
- Rmse: 0.6076
- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.6076
- Mae: 0.4548
- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4548
- R2: 0.5463
- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5463
- Cos: 0.2174
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3931
- 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 concetto astratto (es. gelosia) | Mae    | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2      | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.08          | 1.0   | 15   | 0.9520          | 0.9323 | 0.9323                                                      | 0.6560 | 0.6560                                                     | -0.0680 | -0.0680                                                   | 0.0435 | 0.0  | 0.5  | 0.3188    | nan |
| 0.9974        | 2.0   | 30   | 0.8621          | 0.8872 | 0.8872                                                      | 0.5962 | 0.5962                                                     | 0.0328  | 0.0328                                                    | 0.1304 | 0.0  | 0.5  | 0.4066    | nan |
| 0.9337        | 3.0   | 45   | 0.9223          | 0.9176 | 0.9176                                                      | 0.6608 | 0.6608                                                     | -0.0347 | -0.0347                                                   | 0.2174 | 0.0  | 0.5  | 0.3632    | nan |
| 0.966         | 4.0   | 60   | 0.8273          | 0.8691 | 0.8691                                                      | 0.5874 | 0.5874                                                     | 0.0719  | 0.0719                                                    | 0.2174 | 0.0  | 0.5  | 0.3754    | nan |
| 0.8683        | 5.0   | 75   | 0.8741          | 0.8933 | 0.8933                                                      | 0.6136 | 0.6136                                                     | 0.0193  | 0.0193                                                    | 0.2174 | 0.0  | 0.5  | 0.3529    | nan |
| 0.8522        | 6.0   | 90   | 0.7781          | 0.8428 | 0.8428                                                      | 0.5732 | 0.5732                                                     | 0.1271  | 0.1271                                                    | 0.2174 | 0.0  | 0.5  | 0.4152    | nan |
| 0.7968        | 7.0   | 105  | 0.7257          | 0.8139 | 0.8139                                                      | 0.5519 | 0.5519                                                     | 0.1859  | 0.1859                                                    | 0.2174 | 0.0  | 0.5  | 0.4152    | nan |
| 0.7166        | 8.0   | 120  | 0.7122          | 0.8064 | 0.8064                                                      | 0.5792 | 0.5792                                                     | 0.2010  | 0.2010                                                    | 0.1304 | 0.0  | 0.5  | 0.3955    | nan |
| 0.6246        | 9.0   | 135  | 0.6771          | 0.7862 | 0.7862                                                      | 0.5701 | 0.5701                                                     | 0.2403  | 0.2403                                                    | 0.0435 | 0.0  | 0.5  | 0.3955    | nan |
| 0.5205        | 10.0  | 150  | 0.6704          | 0.7823 | 0.7823                                                      | 0.5735 | 0.5735                                                     | 0.2479  | 0.2479                                                    | 0.3913 | 0.0  | 0.5  | 0.4847    | nan |
| 0.4182        | 11.0  | 165  | 0.6852          | 0.7909 | 0.7909                                                      | 0.5987 | 0.5987                                                     | 0.2313  | 0.2313                                                    | 0.3913 | 0.0  | 0.5  | 0.4847    | nan |
| 0.3984        | 12.0  | 180  | 0.6106          | 0.7466 | 0.7466                                                      | 0.5696 | 0.5696                                                     | 0.3150  | 0.3150                                                    | 0.0435 | 0.0  | 0.5  | 0.2935    | nan |
| 0.3138        | 13.0  | 195  | 0.5867          | 0.7318 | 0.7318                                                      | 0.5209 | 0.5209                                                     | 0.3418  | 0.3418                                                    | 0.2174 | 0.0  | 0.5  | 0.3119    | nan |
| 0.2323        | 14.0  | 210  | 0.5120          | 0.6837 | 0.6837                                                      | 0.5007 | 0.5007                                                     | 0.4256  | 0.4256                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.2149        | 15.0  | 225  | 0.4789          | 0.6612 | 0.6612                                                      | 0.4883 | 0.4883                                                     | 0.4627  | 0.4627                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.1753        | 16.0  | 240  | 0.4526          | 0.6428 | 0.6428                                                      | 0.4775 | 0.4775                                                     | 0.4922  | 0.4922                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.1478        | 17.0  | 255  | 0.4383          | 0.6325 | 0.6325                                                      | 0.4616 | 0.4616                                                     | 0.5083  | 0.5083                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.1289        | 18.0  | 270  | 0.4141          | 0.6148 | 0.6148                                                      | 0.4478 | 0.4478                                                     | 0.5355  | 0.5355                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.1035        | 19.0  | 285  | 0.3952          | 0.6007 | 0.6007                                                      | 0.4407 | 0.4407                                                     | 0.5566  | 0.5566                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.1087        | 20.0  | 300  | 0.4217          | 0.6205 | 0.6205                                                      | 0.4505 | 0.4505                                                     | 0.5269  | 0.5269                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.1005        | 21.0  | 315  | 0.4065          | 0.6091 | 0.6091                                                      | 0.4508 | 0.4508                                                     | 0.5440  | 0.5440                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.0868        | 22.0  | 330  | 0.3937          | 0.5995 | 0.5995                                                      | 0.4470 | 0.4470                                                     | 0.5584  | 0.5584                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.0808        | 23.0  | 345  | 0.4132          | 0.6142 | 0.6142                                                      | 0.4617 | 0.4617                                                     | 0.5364  | 0.5364                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.0737        | 24.0  | 360  | 0.4214          | 0.6203 | 0.6203                                                      | 0.4659 | 0.4659                                                     | 0.5272  | 0.5272                                                    | 0.3043 | 0.0  | 0.5  | 0.4066    | nan |
| 0.0711        | 25.0  | 375  | 0.3863          | 0.5939 | 0.5939                                                      | 0.4470 | 0.4470                                                     | 0.5666  | 0.5666                                                    | 0.3043 | 0.0  | 0.5  | 0.3849    | nan |
| 0.066         | 26.0  | 390  | 0.4353          | 0.6304 | 0.6304                                                      | 0.4760 | 0.4760                                                     | 0.5117  | 0.5117                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.0681        | 27.0  | 405  | 0.4078          | 0.6101 | 0.6101                                                      | 0.4612 | 0.4612                                                     | 0.5426  | 0.5426                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.0543        | 28.0  | 420  | 0.4118          | 0.6132 | 0.6132                                                      | 0.4616 | 0.4616                                                     | 0.5380  | 0.5380                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.069         | 29.0  | 435  | 0.4041          | 0.6074 | 0.6074                                                      | 0.4551 | 0.4551                                                     | 0.5466  | 0.5466                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |
| 0.0604        | 30.0  | 450  | 0.4044          | 0.6076 | 0.6076                                                      | 0.4548 | 0.4548                                                     | 0.5463  | 0.5463                                                    | 0.2174 | 0.0  | 0.5  | 0.3931    | nan |


### Framework versions

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