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-blame-none
  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-blame-none

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: 0.8941
- Rmse: 1.1259
- Rmse Blame::a Nessuno: 1.1259
- Mae: 0.8559
- Mae Blame::a Nessuno: 0.8559
- R2: 0.2847
- R2 Blame::a Nessuno: 0.2847
- Cos: 0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3537
- 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 Blame::a Nessuno | Mae    | Mae Blame::a Nessuno | R2      | R2 Blame::a Nessuno | Cos     | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.042         | 1.0   | 15   | 1.2746          | 1.3443 | 1.3443                | 1.1788 | 1.1788               | -0.0197 | -0.0197             | 0.0435  | 0.0  | 0.5  | 0.2970    | nan |
| 0.9994        | 2.0   | 30   | 1.3264          | 1.3714 | 1.3714                | 1.1967 | 1.1967               | -0.0612 | -0.0612             | -0.0435 | 0.0  | 0.5  | 0.2961    | nan |
| 0.9123        | 3.0   | 45   | 1.2511          | 1.3319 | 1.3319                | 1.0932 | 1.0932               | -0.0009 | -0.0009             | 0.1304  | 0.0  | 0.5  | 0.2681    | nan |
| 0.741         | 4.0   | 60   | 1.0204          | 1.2028 | 1.2028                | 0.9818 | 0.9818               | 0.1836  | 0.1836              | 0.3043  | 0.0  | 0.5  | 0.3686    | nan |
| 0.6337        | 5.0   | 75   | 0.8607          | 1.1047 | 1.1047                | 0.8145 | 0.8145               | 0.3115  | 0.3115              | 0.3913  | 0.0  | 0.5  | 0.4044    | nan |
| 0.4974        | 6.0   | 90   | 0.8574          | 1.1026 | 1.1026                | 0.8095 | 0.8095               | 0.3140  | 0.3140              | 0.3913  | 0.0  | 0.5  | 0.4044    | nan |
| 0.4929        | 7.0   | 105  | 0.8548          | 1.1009 | 1.1009                | 0.8560 | 0.8560               | 0.3161  | 0.3161              | 0.3043  | 0.0  | 0.5  | 0.3686    | nan |
| 0.4378        | 8.0   | 120  | 0.6974          | 0.9944 | 0.9944                | 0.7503 | 0.7503               | 0.4421  | 0.4421              | 0.3043  | 0.0  | 0.5  | 0.3686    | nan |
| 0.3999        | 9.0   | 135  | 0.7955          | 1.0620 | 1.0620                | 0.7907 | 0.7907               | 0.3636  | 0.3636              | 0.3913  | 0.0  | 0.5  | 0.4044    | nan |
| 0.3715        | 10.0  | 150  | 0.8954          | 1.1267 | 1.1267                | 0.8036 | 0.8036               | 0.2837  | 0.2837              | 0.4783  | 0.0  | 0.5  | 0.4058    | nan |
| 0.3551        | 11.0  | 165  | 0.8449          | 1.0945 | 1.0945                | 0.8748 | 0.8748               | 0.3241  | 0.3241              | 0.3913  | 0.0  | 0.5  | 0.3931    | nan |
| 0.3428        | 12.0  | 180  | 0.7960          | 1.0624 | 1.0624                | 0.8000 | 0.8000               | 0.3632  | 0.3632              | 0.3913  | 0.0  | 0.5  | 0.4044    | nan |
| 0.2923        | 13.0  | 195  | 0.9027          | 1.1313 | 1.1313                | 0.8441 | 0.8441               | 0.2778  | 0.2778              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.2236        | 14.0  | 210  | 0.8914          | 1.1242 | 1.1242                | 0.8998 | 0.8998               | 0.2869  | 0.2869              | 0.2174  | 0.0  | 0.5  | 0.3324    | nan |
| 0.2553        | 15.0  | 225  | 0.9184          | 1.1411 | 1.1411                | 0.8633 | 0.8633               | 0.2652  | 0.2652              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.2064        | 16.0  | 240  | 0.9284          | 1.1473 | 1.1473                | 0.8919 | 0.8919               | 0.2573  | 0.2573              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1972        | 17.0  | 255  | 0.9495          | 1.1602 | 1.1602                | 0.8768 | 0.8768               | 0.2404  | 0.2404              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1622        | 18.0  | 270  | 0.9850          | 1.1818 | 1.1818                | 0.9303 | 0.9303               | 0.2120  | 0.2120              | 0.2174  | 0.0  | 0.5  | 0.3324    | nan |
| 0.1685        | 19.0  | 285  | 0.9603          | 1.1669 | 1.1669                | 0.8679 | 0.8679               | 0.2317  | 0.2317              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1773        | 20.0  | 300  | 0.9269          | 1.1464 | 1.1464                | 0.8391 | 0.8391               | 0.2585  | 0.2585              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1716        | 21.0  | 315  | 0.8936          | 1.1256 | 1.1256                | 0.8357 | 0.8357               | 0.2851  | 0.2851              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.161         | 22.0  | 330  | 0.8894          | 1.1230 | 1.1230                | 0.8593 | 0.8593               | 0.2884  | 0.2884              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1297        | 23.0  | 345  | 0.8997          | 1.1294 | 1.1294                | 0.8568 | 0.8568               | 0.2802  | 0.2802              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.15          | 24.0  | 360  | 0.8748          | 1.1137 | 1.1137                | 0.8541 | 0.8541               | 0.3002  | 0.3002              | 0.2174  | 0.0  | 0.5  | 0.3324    | nan |
| 0.1149        | 25.0  | 375  | 0.9264          | 1.1461 | 1.1461                | 0.8682 | 0.8682               | 0.2588  | 0.2588              | 0.3913  | 0.0  | 0.5  | 0.3901    | nan |
| 0.1354        | 26.0  | 390  | 0.8829          | 1.1188 | 1.1188                | 0.8608 | 0.8608               | 0.2937  | 0.2937              | 0.2174  | 0.0  | 0.5  | 0.3324    | nan |
| 0.1321        | 27.0  | 405  | 0.9137          | 1.1382 | 1.1382                | 0.8656 | 0.8656               | 0.2691  | 0.2691              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1154        | 28.0  | 420  | 0.8774          | 1.1154 | 1.1154                | 0.8488 | 0.8488               | 0.2980  | 0.2980              | 0.2174  | 0.0  | 0.5  | 0.3324    | nan |
| 0.1112        | 29.0  | 435  | 0.8985          | 1.1287 | 1.1287                | 0.8562 | 0.8562               | 0.2812  | 0.2812              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |
| 0.1525        | 30.0  | 450  | 0.8941          | 1.1259 | 1.1259                | 0.8559 | 0.8559               | 0.2847  | 0.2847              | 0.3043  | 0.0  | 0.5  | 0.3537    | nan |


### Framework versions

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