LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-assassin
  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-assassin

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: 0.3264
- Rmse: 0.9437
- Rmse Focus::a Sull'assassino: 0.9437
- Mae: 0.7093
- Mae Focus::a Sull'assassino: 0.7093
- R2: 0.6145
- R2 Focus::a Sull'assassino: 0.6145
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6131
- 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 Sull'assassino | Mae    | Mae Focus::a Sull'assassino | R2      | R2 Focus::a Sull'assassino | Cos     | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0403        | 1.0   | 15   | 1.1576          | 1.7771 | 1.7771                       | 1.6028 | 1.6028                      | -0.3670 | -0.3670                    | -0.2174 | 0.0  | 0.5  | 0.2379    | nan |
| 0.9818        | 2.0   | 30   | 0.8916          | 1.5596 | 1.5596                       | 1.4136 | 1.4136                      | -0.0529 | -0.0529                    | 0.3913  | 0.0  | 0.5  | 0.3793    | nan |
| 0.9276        | 3.0   | 45   | 0.9277          | 1.5909 | 1.5909                       | 1.4560 | 1.4560                      | -0.0955 | -0.0955                    | 0.3913  | 0.0  | 0.5  | 0.3742    | nan |
| 0.8395        | 4.0   | 60   | 0.7958          | 1.4734 | 1.4734                       | 1.3032 | 1.3032                      | 0.0603  | 0.0603                     | 0.5652  | 0.0  | 0.5  | 0.4598    | nan |
| 0.7587        | 5.0   | 75   | 0.4647          | 1.1259 | 1.1259                       | 0.9316 | 0.9316                      | 0.4513  | 0.4513                     | 0.6522  | 0.0  | 0.5  | 0.5087    | nan |
| 0.696         | 6.0   | 90   | 0.5368          | 1.2101 | 1.2101                       | 1.0847 | 1.0847                      | 0.3661  | 0.3661                     | 0.7391  | 0.0  | 0.5  | 0.5302    | nan |
| 0.548         | 7.0   | 105  | 0.3110          | 0.9211 | 0.9211                       | 0.7896 | 0.7896                      | 0.6328  | 0.6328                     | 0.6522  | 0.0  | 0.5  | 0.5261    | nan |
| 0.4371        | 8.0   | 120  | 0.3392          | 0.9619 | 0.9619                       | 0.8132 | 0.8132                      | 0.5995  | 0.5995                     | 0.6522  | 0.0  | 0.5  | 0.5261    | nan |
| 0.355         | 9.0   | 135  | 0.3938          | 1.0366 | 1.0366                       | 0.8153 | 0.8153                      | 0.5349  | 0.5349                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.2919        | 10.0  | 150  | 0.3484          | 0.9749 | 0.9749                       | 0.7487 | 0.7487                      | 0.5886  | 0.5886                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.2595        | 11.0  | 165  | 0.2812          | 0.8759 | 0.8759                       | 0.6265 | 0.6265                      | 0.6679  | 0.6679                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.2368        | 12.0  | 180  | 0.2534          | 0.8314 | 0.8314                       | 0.6402 | 0.6402                      | 0.7008  | 0.7008                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.227         | 13.0  | 195  | 0.2878          | 0.8861 | 0.8861                       | 0.6769 | 0.6769                      | 0.6601  | 0.6601                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.1979        | 14.0  | 210  | 0.2405          | 0.8100 | 0.8100                       | 0.6113 | 0.6113                      | 0.7160  | 0.7160                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.1622        | 15.0  | 225  | 0.2575          | 0.8382 | 0.8382                       | 0.6017 | 0.6017                      | 0.6959  | 0.6959                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1575        | 16.0  | 240  | 0.2945          | 0.8963 | 0.8963                       | 0.6741 | 0.6741                      | 0.6523  | 0.6523                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1479        | 17.0  | 255  | 0.3563          | 0.9859 | 0.9859                       | 0.7367 | 0.7367                      | 0.5792  | 0.5792                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1269        | 18.0  | 270  | 0.2806          | 0.8750 | 0.8750                       | 0.6665 | 0.6665                      | 0.6686  | 0.6686                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1257        | 19.0  | 285  | 0.3267          | 0.9441 | 0.9441                       | 0.6739 | 0.6739                      | 0.6142  | 0.6142                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.134         | 20.0  | 300  | 0.3780          | 1.0155 | 1.0155                       | 0.7331 | 0.7331                      | 0.5536  | 0.5536                     | 0.7391  | 0.0  | 0.5  | 0.5302    | nan |
| 0.1171        | 21.0  | 315  | 0.3890          | 1.0301 | 1.0301                       | 0.7444 | 0.7444                      | 0.5406  | 0.5406                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.0934        | 22.0  | 330  | 0.3131          | 0.9242 | 0.9242                       | 0.6923 | 0.6923                      | 0.6303  | 0.6303                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1112        | 23.0  | 345  | 0.2912          | 0.8913 | 0.8913                       | 0.6610 | 0.6610                      | 0.6561  | 0.6561                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.1038        | 24.0  | 360  | 0.3109          | 0.9209 | 0.9209                       | 0.7019 | 0.7019                      | 0.6329  | 0.6329                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.085         | 25.0  | 375  | 0.3469          | 0.9728 | 0.9728                       | 0.7383 | 0.7383                      | 0.5904  | 0.5904                     | 0.8261  | 0.0  | 0.5  | 0.6622    | nan |
| 0.0843        | 26.0  | 390  | 0.3017          | 0.9073 | 0.9073                       | 0.6848 | 0.6848                      | 0.6437  | 0.6437                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.093         | 27.0  | 405  | 0.3269          | 0.9443 | 0.9443                       | 0.7042 | 0.7042                      | 0.6140  | 0.6140                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.0846        | 28.0  | 420  | 0.3161          | 0.9286 | 0.9286                       | 0.6937 | 0.6937                      | 0.6267  | 0.6267                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.0764        | 29.0  | 435  | 0.3244          | 0.9408 | 0.9408                       | 0.7079 | 0.7079                      | 0.6169  | 0.6169                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |
| 0.0697        | 30.0  | 450  | 0.3264          | 0.9437 | 0.9437                       | 0.7093 | 0.7093                      | 0.6145  | 0.6145                     | 0.7391  | 0.0  | 0.5  | 0.6131    | nan |


### Framework versions

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