LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-human
  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-human

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: 0.7632
- Rmse: 1.2675
- Rmse Cause::a Causata da un essere umano: 1.2675
- Mae: 0.9299
- Mae Cause::a Causata da un essere umano: 0.9299
- R2: 0.4188
- R2 Cause::a Causata da un essere umano: 0.4188
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4082
- 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 essere umano | Mae    | Mae Cause::a Causata da un essere umano | R2      | R2 Cause::a Causata da un essere umano | Cos     | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:-------:|:--------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0174        | 1.0   | 15   | 1.3796          | 1.7041 | 1.7041                                   | 1.3614 | 1.3614                                  | -0.0506 | -0.0506                                | -0.1304 | 0.0  | 0.5  | 0.2971    | nan |
| 0.9534        | 2.0   | 30   | 1.1173          | 1.5336 | 1.5336                                   | 1.2624 | 1.2624                                  | 0.1491  | 0.1491                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.8883        | 3.0   | 45   | 1.0580          | 1.4923 | 1.4923                                   | 1.2451 | 1.2451                                  | 0.1943  | 0.1943                                 | 0.5652  | 0.0  | 0.5  | 0.4957    | nan |
| 0.8215        | 4.0   | 60   | 1.0200          | 1.4653 | 1.4653                                   | 1.2087 | 1.2087                                  | 0.2232  | 0.2232                                 | 0.6522  | 0.0  | 0.5  | 0.5123    | nan |
| 0.744         | 5.0   | 75   | 1.1496          | 1.5556 | 1.5556                                   | 1.2573 | 1.2573                                  | 0.1245  | 0.1245                                 | 0.2174  | 0.0  | 0.5  | 0.3007    | nan |
| 0.7056        | 6.0   | 90   | 0.9641          | 1.4246 | 1.4246                                   | 1.1763 | 1.1763                                  | 0.2658  | 0.2658                                 | 0.4783  | 0.0  | 0.5  | 0.3619    | nan |
| 0.6136        | 7.0   | 105  | 0.8328          | 1.3240 | 1.3240                                   | 1.0948 | 1.0948                                  | 0.3658  | 0.3658                                 | 0.4783  | 0.0  | 0.5  | 0.3628    | nan |
| 0.5185        | 8.0   | 120  | 0.6890          | 1.2043 | 1.2043                                   | 1.0112 | 1.0112                                  | 0.4753  | 0.4753                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.5029        | 9.0   | 135  | 1.0380          | 1.4782 | 1.4782                                   | 1.1215 | 1.1215                                  | 0.2095  | 0.2095                                 | 0.3913  | 0.0  | 0.5  | 0.3781    | nan |
| 0.4624        | 10.0  | 150  | 1.1780          | 1.5747 | 1.5747                                   | 1.2852 | 1.2852                                  | 0.1029  | 0.1029                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.4098        | 11.0  | 165  | 0.8714          | 1.3544 | 1.3544                                   | 1.1388 | 1.1388                                  | 0.3364  | 0.3364                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.348         | 12.0  | 180  | 0.7260          | 1.2362 | 1.2362                                   | 0.9563 | 0.9563                                  | 0.4471  | 0.4471                                 | 0.5652  | 0.0  | 0.5  | 0.4957    | nan |
| 0.3437        | 13.0  | 195  | 0.7241          | 1.2346 | 1.2346                                   | 0.8998 | 0.8998                                  | 0.4485  | 0.4485                                 | 0.6522  | 0.0  | 0.5  | 0.4727    | nan |
| 0.2727        | 14.0  | 210  | 0.9070          | 1.3818 | 1.3818                                   | 1.1145 | 1.1145                                  | 0.3093  | 0.3093                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.2762        | 15.0  | 225  | 0.7280          | 1.2380 | 1.2380                                   | 0.9210 | 0.9210                                  | 0.4456  | 0.4456                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.2396        | 16.0  | 240  | 0.7921          | 1.2912 | 1.2912                                   | 0.9738 | 0.9738                                  | 0.3968  | 0.3968                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1955        | 17.0  | 255  | 0.8368          | 1.3272 | 1.3272                                   | 0.9717 | 0.9717                                  | 0.3627  | 0.3627                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1928        | 18.0  | 270  | 0.7782          | 1.2799 | 1.2799                                   | 0.9615 | 0.9615                                  | 0.4073  | 0.4073                                 | 0.3043  | 0.0  | 0.5  | 0.3768    | nan |
| 0.1893        | 19.0  | 285  | 0.7594          | 1.2644 | 1.2644                                   | 0.9441 | 0.9441                                  | 0.4216  | 0.4216                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.2111        | 20.0  | 300  | 0.7230          | 1.2336 | 1.2336                                   | 0.8953 | 0.8953                                  | 0.4494  | 0.4494                                 | 0.3913  | 0.0  | 0.5  | 0.3787    | nan |
| 0.193         | 21.0  | 315  | 0.7836          | 1.2843 | 1.2843                                   | 0.9577 | 0.9577                                  | 0.4033  | 0.4033                                 | 0.3043  | 0.0  | 0.5  | 0.3768    | nan |
| 0.1649        | 22.0  | 330  | 0.7248          | 1.2352 | 1.2352                                   | 0.9133 | 0.9133                                  | 0.4480  | 0.4480                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.2182        | 23.0  | 345  | 0.7608          | 1.2655 | 1.2655                                   | 0.9435 | 0.9435                                  | 0.4206  | 0.4206                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.1534        | 24.0  | 360  | 0.7447          | 1.2520 | 1.2520                                   | 0.9277 | 0.9277                                  | 0.4329  | 0.4329                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.1362        | 25.0  | 375  | 0.7437          | 1.2512 | 1.2512                                   | 0.9236 | 0.9236                                  | 0.4336  | 0.4336                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1391        | 26.0  | 390  | 0.7301          | 1.2397 | 1.2397                                   | 0.9182 | 0.9182                                  | 0.4440  | 0.4440                                 | 0.4783  | 0.0  | 0.5  | 0.4446    | nan |
| 0.1679        | 27.0  | 405  | 0.7748          | 1.2770 | 1.2770                                   | 0.9619 | 0.9619                                  | 0.4100  | 0.4100                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1491        | 28.0  | 420  | 0.7415          | 1.2493 | 1.2493                                   | 0.9097 | 0.9097                                  | 0.4353  | 0.4353                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1559        | 29.0  | 435  | 0.7525          | 1.2586 | 1.2586                                   | 0.9189 | 0.9189                                  | 0.4269  | 0.4269                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |
| 0.1784        | 30.0  | 450  | 0.7632          | 1.2675 | 1.2675                                   | 0.9299 | 0.9299                                  | 0.4188  | 0.4188                                 | 0.3913  | 0.0  | 0.5  | 0.4082    | nan |


### Framework versions

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