pytorch tensorboard xlm-roberta text-classification transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-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-xlmr-cause-concept

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3933
- Rmse: 0.5992
- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992
- Mae: 0.4566
- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4566
- R2: 0.5588
- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5588
- Cos: 0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4340
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0114        | 1.0   | 15   | 0.9088          | 0.9109 | 0.9109                                                      | 0.6455 | 0.6455                                                     | -0.0195 | -0.0195                                                   | -0.0435 | 0.0  | 0.5  | 0.4027    | nan |
| 1.0           | 2.0   | 30   | 0.8833          | 0.8980 | 0.8980                                                      | 0.6104 | 0.6104                                                     | 0.0090  | 0.0090                                                    | 0.2174  | 0.0  | 0.5  | 0.3681    | nan |
| 0.9533        | 3.0   | 45   | 0.8453          | 0.8785 | 0.8785                                                      | 0.6072 | 0.6072                                                     | 0.0517  | 0.0517                                                    | 0.1304  | 0.0  | 0.5  | 0.3748    | nan |
| 0.9113        | 4.0   | 60   | 0.7797          | 0.8437 | 0.8437                                                      | 0.6024 | 0.6024                                                     | 0.1253  | 0.1253                                                    | 0.0435  | 0.0  | 0.5  | 0.3028    | nan |
| 0.8312        | 5.0   | 75   | 0.5756          | 0.7249 | 0.7249                                                      | 0.5128 | 0.5128                                                     | 0.3542  | 0.3542                                                    | 0.4783  | 0.0  | 0.5  | 0.4572    | nan |
| 0.7224        | 6.0   | 90   | 0.4977          | 0.6741 | 0.6741                                                      | 0.5114 | 0.5114                                                     | 0.4416  | 0.4416                                                    | 0.2174  | 0.0  | 0.5  | 0.4009    | nan |
| 0.5789        | 7.0   | 105  | 0.6338          | 0.7607 | 0.7607                                                      | 0.5059 | 0.5059                                                     | 0.2889  | 0.2889                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.4978        | 8.0   | 120  | 0.3342          | 0.5524 | 0.5524                                                      | 0.4298 | 0.4298                                                     | 0.6250  | 0.6250                                                    | 0.2174  | 0.0  | 0.5  | 0.4274    | nan |
| 0.4572        | 9.0   | 135  | 0.3210          | 0.5413 | 0.5413                                                      | 0.4343 | 0.4343                                                     | 0.6399  | 0.6399                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.3346        | 10.0  | 150  | 0.3456          | 0.5617 | 0.5617                                                      | 0.4198 | 0.4198                                                     | 0.6123  | 0.6123                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.3046        | 11.0  | 165  | 0.3840          | 0.5921 | 0.5921                                                      | 0.4312 | 0.4312                                                     | 0.5692  | 0.5692                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.3035        | 12.0  | 180  | 0.3929          | 0.5989 | 0.5989                                                      | 0.4147 | 0.4147                                                     | 0.5592  | 0.5592                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.2199        | 13.0  | 195  | 0.3165          | 0.5376 | 0.5376                                                      | 0.4065 | 0.4065                                                     | 0.6449  | 0.6449                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.2376        | 14.0  | 210  | 0.3108          | 0.5326 | 0.5326                                                      | 0.3937 | 0.3937                                                     | 0.6514  | 0.6514                                                    | 0.3913  | 0.0  | 0.5  | 0.4286    | nan |
| 0.1639        | 15.0  | 225  | 0.3645          | 0.5769 | 0.5769                                                      | 0.4094 | 0.4094                                                     | 0.5911  | 0.5911                                                    | 0.3913  | 0.0  | 0.5  | 0.4286    | nan |
| 0.1884        | 16.0  | 240  | 0.3762          | 0.5860 | 0.5860                                                      | 0.4398 | 0.4398                                                     | 0.5779  | 0.5779                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1767        | 17.0  | 255  | 0.3805          | 0.5894 | 0.5894                                                      | 0.4540 | 0.4540                                                     | 0.5732  | 0.5732                                                    | 0.2174  | 0.0  | 0.5  | 0.4298    | nan |
| 0.1329        | 18.0  | 270  | 0.3555          | 0.5697 | 0.5697                                                      | 0.4281 | 0.4281                                                     | 0.6011  | 0.6011                                                    | 0.2174  | 0.0  | 0.5  | 0.4298    | nan |
| 0.1834        | 19.0  | 285  | 0.4337          | 0.6292 | 0.6292                                                      | 0.4402 | 0.4402                                                     | 0.5135  | 0.5135                                                    | 0.3913  | 0.0  | 0.5  | 0.4286    | nan |
| 0.1538        | 20.0  | 300  | 0.3554          | 0.5696 | 0.5696                                                      | 0.4236 | 0.4236                                                     | 0.6013  | 0.6013                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1459        | 21.0  | 315  | 0.3592          | 0.5726 | 0.5726                                                      | 0.4348 | 0.4348                                                     | 0.5971  | 0.5971                                                    | 0.3043  | 0.0  | 0.5  | 0.4066    | nan |
| 0.1038        | 22.0  | 330  | 0.3732          | 0.5837 | 0.5837                                                      | 0.4382 | 0.4382                                                     | 0.5813  | 0.5813                                                    | 0.3913  | 0.0  | 0.5  | 0.4664    | nan |
| 0.1432        | 23.0  | 345  | 0.3635          | 0.5760 | 0.5760                                                      | 0.4394 | 0.4394                                                     | 0.5922  | 0.5922                                                    | 0.3913  | 0.0  | 0.5  | 0.4664    | nan |
| 0.1354        | 24.0  | 360  | 0.4359          | 0.6308 | 0.6308                                                      | 0.4793 | 0.4793                                                     | 0.5110  | 0.5110                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1404        | 25.0  | 375  | 0.3919          | 0.5982 | 0.5982                                                      | 0.4650 | 0.4650                                                     | 0.5603  | 0.5603                                                    | 0.3913  | 0.0  | 0.5  | 0.4664    | nan |
| 0.103         | 26.0  | 390  | 0.4223          | 0.6209 | 0.6209                                                      | 0.4691 | 0.4691                                                     | 0.5263  | 0.5263                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1733        | 27.0  | 405  | 0.3972          | 0.6021 | 0.6021                                                      | 0.4591 | 0.4591                                                     | 0.5544  | 0.5544                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1019        | 28.0  | 420  | 0.3958          | 0.6011 | 0.6011                                                      | 0.4593 | 0.4593                                                     | 0.5559  | 0.5559                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.1076        | 29.0  | 435  | 0.4015          | 0.6054 | 0.6054                                                      | 0.4589 | 0.4589                                                     | 0.5496  | 0.5496                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |
| 0.0999        | 30.0  | 450  | 0.3933          | 0.5992 | 0.5992                                                      | 0.4566 | 0.4566                                                     | 0.5588  | 0.5588                                                    | 0.3043  | 0.0  | 0.5  | 0.4340    | nan |


### Framework versions

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