LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-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-xlmr-focus-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: 0.1927
- Rmse: 0.5495
- Rmse Focus::a Su un oggetto: 0.5495
- Mae: 0.4174
- Mae Focus::a Su un oggetto: 0.4174
- R2: 0.5721
- R2 Focus::a Su un oggetto: 0.5721
- Cos: 0.5652
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5518
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0316        | 1.0   | 15   | 0.6428          | 1.0035 | 1.0035                      | 0.8806 | 0.8806                     | -0.4272 | -0.4272                   | -0.4783 | 0.0  | 0.5  | 0.5302    | nan |
| 1.0005        | 2.0   | 30   | 0.4564          | 0.8456 | 0.8456                      | 0.7078 | 0.7078                     | -0.0134 | -0.0134                   | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.9519        | 3.0   | 45   | 0.4151          | 0.8063 | 0.8063                      | 0.6797 | 0.6797                     | 0.0784  | 0.0784                    | 0.1304  | 0.0  | 0.5  | 0.4888    | nan |
| 0.92          | 4.0   | 60   | 0.3982          | 0.7898 | 0.7898                      | 0.6516 | 0.6516                     | 0.1159  | 0.1159                    | 0.2174  | 0.0  | 0.5  | 0.5036    | nan |
| 0.8454        | 5.0   | 75   | 0.2739          | 0.6550 | 0.6550                      | 0.5292 | 0.5292                     | 0.3919  | 0.3919                    | 0.6522  | 0.0  | 0.5  | 0.4160    | nan |
| 0.7247        | 6.0   | 90   | 0.2413          | 0.6148 | 0.6148                      | 0.5347 | 0.5347                     | 0.4642  | 0.4642                    | 0.4783  | 0.0  | 0.5  | 0.3453    | nan |
| 0.6055        | 7.0   | 105  | 0.3109          | 0.6978 | 0.6978                      | 0.6115 | 0.6115                     | 0.3098  | 0.3098                    | 0.4783  | 0.0  | 0.5  | 0.4154    | nan |
| 0.5411        | 8.0   | 120  | 0.3932          | 0.7848 | 0.7848                      | 0.6712 | 0.6712                     | 0.1271  | 0.1271                    | 0.4783  | 0.0  | 0.5  | 0.4154    | nan |
| 0.4784        | 9.0   | 135  | 0.1316          | 0.4540 | 0.4540                      | 0.3750 | 0.3750                     | 0.7079  | 0.7079                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.4039        | 10.0  | 150  | 0.2219          | 0.5896 | 0.5896                      | 0.4954 | 0.4954                     | 0.5074  | 0.5074                    | 0.5652  | 0.0  | 0.5  | 0.4838    | nan |
| 0.3415        | 11.0  | 165  | 0.1935          | 0.5505 | 0.5505                      | 0.4443 | 0.4443                     | 0.5704  | 0.5704                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.3369        | 12.0  | 180  | 0.2118          | 0.5761 | 0.5761                      | 0.4554 | 0.4554                     | 0.5296  | 0.5296                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.3083        | 13.0  | 195  | 0.1928          | 0.5496 | 0.5496                      | 0.4368 | 0.4368                     | 0.5718  | 0.5718                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.2678        | 14.0  | 210  | 0.2205          | 0.5877 | 0.5877                      | 0.4472 | 0.4472                     | 0.5105  | 0.5105                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.2199        | 15.0  | 225  | 0.2118          | 0.5760 | 0.5760                      | 0.4689 | 0.4689                     | 0.5297  | 0.5297                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.2238        | 16.0  | 240  | 0.2461          | 0.6209 | 0.6209                      | 0.5047 | 0.5047                     | 0.4537  | 0.4537                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.2233        | 17.0  | 255  | 0.2307          | 0.6011 | 0.6011                      | 0.4618 | 0.4618                     | 0.4879  | 0.4879                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.1903        | 18.0  | 270  | 0.2207          | 0.5880 | 0.5880                      | 0.4432 | 0.4432                     | 0.5100  | 0.5100                    | 0.6522  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1714        | 19.0  | 285  | 0.2146          | 0.5798 | 0.5798                      | 0.4368 | 0.4368                     | 0.5236  | 0.5236                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.1759        | 20.0  | 300  | 0.1745          | 0.5228 | 0.5228                      | 0.4152 | 0.4152                     | 0.6126  | 0.6126                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.1505        | 21.0  | 315  | 0.1944          | 0.5519 | 0.5519                      | 0.4170 | 0.4170                     | 0.5684  | 0.5684                    | 0.5652  | 0.0  | 0.5  | 0.6247    | nan |
| 0.1467        | 22.0  | 330  | 0.1802          | 0.5313 | 0.5313                      | 0.3910 | 0.3910                     | 0.5999  | 0.5999                    | 0.6522  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1441        | 23.0  | 345  | 0.2360          | 0.6081 | 0.6081                      | 0.4755 | 0.4755                     | 0.4760  | 0.4760                    | 0.4783  | 0.0  | 0.5  | 0.4938    | nan |
| 0.1553        | 24.0  | 360  | 0.2129          | 0.5774 | 0.5774                      | 0.4539 | 0.4539                     | 0.5274  | 0.5274                    | 0.5652  | 0.0  | 0.5  | 0.5518    | nan |
| 0.1163        | 25.0  | 375  | 0.1780          | 0.5281 | 0.5281                      | 0.3952 | 0.3952                     | 0.6048  | 0.6048                    | 0.6522  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1266        | 26.0  | 390  | 0.2163          | 0.5821 | 0.5821                      | 0.4569 | 0.4569                     | 0.5198  | 0.5198                    | 0.5652  | 0.0  | 0.5  | 0.5518    | nan |
| 0.1416        | 27.0  | 405  | 0.1829          | 0.5352 | 0.5352                      | 0.4082 | 0.4082                     | 0.5939  | 0.5939                    | 0.5652  | 0.0  | 0.5  | 0.5518    | nan |
| 0.1576        | 28.0  | 420  | 0.1930          | 0.5498 | 0.5498                      | 0.4126 | 0.4126                     | 0.5716  | 0.5716                    | 0.6522  | 0.0  | 0.5  | 0.6622    | nan |
| 0.118         | 29.0  | 435  | 0.2070          | 0.5694 | 0.5694                      | 0.4378 | 0.4378                     | 0.5405  | 0.5405                    | 0.5652  | 0.0  | 0.5  | 0.5518    | nan |
| 0.1179        | 30.0  | 450  | 0.1927          | 0.5495 | 0.5495                      | 0.4174 | 0.4174                     | 0.5721  | 0.5721                    | 0.5652  | 0.0  | 0.5  | 0.5518    | nan |


### Framework versions

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