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-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-xlmr-cause-object

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: 0.3069
- Rmse: 0.8927
- Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927
- Mae: 0.5854
- Mae Cause::a Causata da un oggetto (es. una pistola): 0.5854
- R2: 0.5410
- R2 Cause::a Causata da un oggetto (es. una pistola): 0.5410
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6177
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0329        | 1.0   | 15   | 0.8168          | 1.4564 | 1.4564                                                | 1.2947 | 1.2947                                               | -0.2216 | -0.2216                                             | -0.5652 | 0.0  | 0.5  | 0.5993    | nan |
| 1.0096        | 2.0   | 30   | 0.7432          | 1.3893 | 1.3893                                                | 1.1883 | 1.1883                                               | -0.1116 | -0.1116                                             | -0.3913 | 0.0  | 0.5  | 0.6499    | nan |
| 0.9323        | 3.0   | 45   | 0.6879          | 1.3366 | 1.3366                                                | 1.1054 | 1.1054                                               | -0.0289 | -0.0289                                             | -0.1304 | 0.0  | 0.5  | 0.5471    | nan |
| 0.8636        | 4.0   | 60   | 0.6378          | 1.2870 | 1.2870                                                | 1.0477 | 1.0477                                               | 0.0461  | 0.0461                                              | 0.2174  | 0.0  | 0.5  | 0.3007    | nan |
| 0.8041        | 5.0   | 75   | 0.5494          | 1.1945 | 1.1945                                                | 0.9499 | 0.9499                                               | 0.1783  | 0.1783                                              | 0.6522  | 0.0  | 0.5  | 0.6695    | nan |
| 0.7413        | 6.0   | 90   | 0.5526          | 1.1980 | 1.1980                                                | 0.9503 | 0.9503                                               | 0.1735  | 0.1735                                              | 0.5652  | 0.0  | 0.5  | 0.3898    | nan |
| 0.6397        | 7.0   | 105  | 0.4726          | 1.1078 | 1.1078                                                | 0.7826 | 0.7826                                               | 0.2932  | 0.2932                                              | 0.5652  | 0.0  | 0.5  | 0.3257    | nan |
| 0.5556        | 8.0   | 120  | 0.7728          | 1.4167 | 1.4167                                                | 1.1528 | 1.1528                                               | -0.1558 | -0.1558                                             | 0.1304  | 0.0  | 0.5  | 0.4027    | nan |
| 0.4972        | 9.0   | 135  | 0.4375          | 1.0659 | 1.0659                                                | 0.7577 | 0.7577                                               | 0.3457  | 0.3457                                              | 0.5652  | 0.0  | 0.5  | 0.5683    | nan |
| 0.3691        | 10.0  | 150  | 0.4990          | 1.1383 | 1.1383                                                | 0.8272 | 0.8272                                               | 0.2537  | 0.2537                                              | 0.4783  | 0.0  | 0.5  | 0.4781    | nan |
| 0.3381        | 11.0  | 165  | 0.4401          | 1.0690 | 1.0690                                                | 0.7319 | 0.7319                                               | 0.3418  | 0.3418                                              | 0.5652  | 0.0  | 0.5  | 0.5683    | nan |
| 0.2966        | 12.0  | 180  | 0.4794          | 1.1158 | 1.1158                                                | 0.7835 | 0.7835                                               | 0.2830  | 0.2830                                              | 0.5652  | 0.0  | 0.5  | 0.5683    | nan |
| 0.2324        | 13.0  | 195  | 0.4013          | 1.0208 | 1.0208                                                | 0.6873 | 0.6873                                               | 0.3998  | 0.3998                                              | 0.4783  | 0.0  | 0.5  | 0.5796    | nan |
| 0.1848        | 14.0  | 210  | 0.4305          | 1.0574 | 1.0574                                                | 0.7372 | 0.7372                                               | 0.3561  | 0.3561                                              | 0.4783  | 0.0  | 0.5  | 0.5796    | nan |
| 0.1621        | 15.0  | 225  | 0.3652          | 0.9738 | 0.9738                                                | 0.6164 | 0.6164                                               | 0.4538  | 0.4538                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.1762        | 16.0  | 240  | 0.3335          | 0.9307 | 0.9307                                                | 0.6458 | 0.6458                                               | 0.5012  | 0.5012                                              | 0.4783  | 0.0  | 0.5  | 0.5796    | nan |
| 0.1404        | 17.0  | 255  | 0.3420          | 0.9424 | 0.9424                                                | 0.6599 | 0.6599                                               | 0.4886  | 0.4886                                              | 0.3913  | 0.0  | 0.5  | 0.5831    | nan |
| 0.1379        | 18.0  | 270  | 0.2853          | 0.8608 | 0.8608                                                | 0.6063 | 0.6063                                               | 0.5733  | 0.5733                                              | 0.3913  | 0.0  | 0.5  | 0.5831    | nan |
| 0.1322        | 19.0  | 285  | 0.3261          | 0.9203 | 0.9203                                                | 0.6548 | 0.6548                                               | 0.5123  | 0.5123                                              | 0.4783  | 0.0  | 0.5  | 0.5796    | nan |
| 0.1067        | 20.0  | 300  | 0.3328          | 0.9296 | 0.9296                                                | 0.5535 | 0.5535                                               | 0.5023  | 0.5023                                              | 0.6522  | 0.0  | 0.5  | 0.6695    | nan |
| 0.1038        | 21.0  | 315  | 0.3066          | 0.8924 | 0.8924                                                | 0.6266 | 0.6266                                               | 0.5414  | 0.5414                                              | 0.4783  | 0.0  | 0.5  | 0.5796    | nan |
| 0.094         | 22.0  | 330  | 0.2924          | 0.8714 | 0.8714                                                | 0.5792 | 0.5792                                               | 0.5626  | 0.5626                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.1078        | 23.0  | 345  | 0.3161          | 0.9060 | 0.9060                                                | 0.6022 | 0.6022                                               | 0.5272  | 0.5272                                              | 0.3913  | 0.0  | 0.5  | 0.5831    | nan |
| 0.0976        | 24.0  | 360  | 0.3118          | 0.8998 | 0.8998                                                | 0.6011 | 0.6011                                               | 0.5337  | 0.5337                                              | 0.3913  | 0.0  | 0.5  | 0.5831    | nan |
| 0.0911        | 25.0  | 375  | 0.3123          | 0.9005 | 0.9005                                                | 0.5811 | 0.5811                                               | 0.5330  | 0.5330                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.1039        | 26.0  | 390  | 0.3122          | 0.9005 | 0.9005                                                | 0.5956 | 0.5956                                               | 0.5330  | 0.5330                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.0775        | 27.0  | 405  | 0.3191          | 0.9103 | 0.9103                                                | 0.6124 | 0.6124                                               | 0.5228  | 0.5228                                              | 0.3913  | 0.0  | 0.5  | 0.5831    | nan |
| 0.0789        | 28.0  | 420  | 0.3135          | 0.9023 | 0.9023                                                | 0.5825 | 0.5825                                               | 0.5311  | 0.5311                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.0778        | 29.0  | 435  | 0.3075          | 0.8936 | 0.8936                                                | 0.5837 | 0.5837                                               | 0.5401  | 0.5401                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |
| 0.082         | 30.0  | 450  | 0.3069          | 0.8927 | 0.8927                                                | 0.5854 | 0.5854                                               | 0.5410  | 0.5410                                              | 0.4783  | 0.0  | 0.5  | 0.6177    | nan |


### Framework versions

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