LABEL_0
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-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-bert-blame-assassin

This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5128
- Rmse: 1.0287
- Rmse Blame::a L'assassino: 1.0287
- Mae: 0.8883
- Mae Blame::a L'assassino: 0.8883
- R2: 0.5883
- R2 Blame::a L'assassino: 0.5883
- Cos: 0.6522
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5795
- 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 L'assassino | Mae    | Mae Blame::a L'assassino | R2     | R2 Blame::a L'assassino | Cos    | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0184        | 1.0   | 15   | 1.2219          | 1.5879 | 1.5879                    | 1.4308 | 1.4308                   | 0.0191 | 0.0191                  | 0.3913 | 0.0  | 0.5  | 0.3781    | nan |
| 0.9214        | 2.0   | 30   | 1.0927          | 1.5017 | 1.5017                    | 1.3634 | 1.3634                   | 0.1227 | 0.1227                  | 0.5652 | 0.0  | 0.5  | 0.4512    | nan |
| 0.7809        | 3.0   | 45   | 0.8206          | 1.3013 | 1.3013                    | 1.1808 | 1.1808                   | 0.3412 | 0.3412                  | 0.4783 | 0.0  | 0.5  | 0.3819    | nan |
| 0.6593        | 4.0   | 60   | 0.5894          | 1.1029 | 1.1029                    | 1.0145 | 1.0145                   | 0.5268 | 0.5268                  | 0.7391 | 0.0  | 0.5  | 0.6408    | nan |
| 0.4672        | 5.0   | 75   | 0.4759          | 0.9910 | 0.9910                    | 0.8868 | 0.8868                   | 0.6180 | 0.6180                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.3356        | 6.0   | 90   | 0.4220          | 0.9332 | 0.9332                    | 0.8083 | 0.8083                   | 0.6612 | 0.6612                  | 0.6522 | 0.0  | 0.5  | 0.4249    | nan |
| 0.2782        | 7.0   | 105  | 0.4477          | 0.9612 | 0.9612                    | 0.8046 | 0.8046                   | 0.6406 | 0.6406                  | 0.6522 | 0.0  | 0.5  | 0.6101    | nan |
| 0.2075        | 8.0   | 120  | 0.4389          | 0.9518 | 0.9518                    | 0.8050 | 0.8050                   | 0.6476 | 0.6476                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.1725        | 9.0   | 135  | 0.4832          | 0.9985 | 0.9985                    | 0.8356 | 0.8356                   | 0.6121 | 0.6121                  | 0.7391 | 0.0  | 0.5  | 0.6616    | nan |
| 0.1642        | 10.0  | 150  | 0.4368          | 0.9494 | 0.9494                    | 0.8060 | 0.8060                   | 0.6493 | 0.6493                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.1172        | 11.0  | 165  | 0.4538          | 0.9677 | 0.9677                    | 0.8174 | 0.8174                   | 0.6357 | 0.6357                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.104         | 12.0  | 180  | 0.4672          | 0.9819 | 0.9819                    | 0.8384 | 0.8384                   | 0.6249 | 0.6249                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0822        | 13.0  | 195  | 0.4401          | 0.9530 | 0.9530                    | 0.8107 | 0.8107                   | 0.6467 | 0.6467                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0755        | 14.0  | 210  | 0.4464          | 0.9598 | 0.9598                    | 0.8251 | 0.8251                   | 0.6416 | 0.6416                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0801        | 15.0  | 225  | 0.4834          | 0.9988 | 0.9988                    | 0.8604 | 0.8604                   | 0.6119 | 0.6119                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.053         | 16.0  | 240  | 0.4846          | 1.0001 | 1.0001                    | 0.8651 | 0.8651                   | 0.6109 | 0.6109                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0573        | 17.0  | 255  | 0.4970          | 1.0128 | 1.0128                    | 0.8743 | 0.8743                   | 0.6010 | 0.6010                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0571        | 18.0  | 270  | 0.4803          | 0.9956 | 0.9956                    | 0.8503 | 0.8503                   | 0.6144 | 0.6144                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0483        | 19.0  | 285  | 0.4936          | 1.0093 | 1.0093                    | 0.8740 | 0.8740                   | 0.6037 | 0.6037                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0414        | 20.0  | 300  | 0.5138          | 1.0297 | 1.0297                    | 0.8943 | 0.8943                   | 0.5875 | 0.5875                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0513        | 21.0  | 315  | 0.5240          | 1.0399 | 1.0399                    | 0.9050 | 0.9050                   | 0.5793 | 0.5793                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0499        | 22.0  | 330  | 0.5275          | 1.0434 | 1.0434                    | 0.9048 | 0.9048                   | 0.5765 | 0.5765                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0423        | 23.0  | 345  | 0.5350          | 1.0508 | 1.0508                    | 0.8872 | 0.8872                   | 0.5705 | 0.5705                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0447        | 24.0  | 360  | 0.4963          | 1.0120 | 1.0120                    | 0.8754 | 0.8754                   | 0.6016 | 0.6016                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0364        | 25.0  | 375  | 0.5009          | 1.0167 | 1.0167                    | 0.8809 | 0.8809                   | 0.5979 | 0.5979                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0412        | 26.0  | 390  | 0.5060          | 1.0219 | 1.0219                    | 0.8781 | 0.8781                   | 0.5938 | 0.5938                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0297        | 27.0  | 405  | 0.5027          | 1.0185 | 1.0185                    | 0.8838 | 0.8838                   | 0.5964 | 0.5964                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0416        | 28.0  | 420  | 0.5071          | 1.0230 | 1.0230                    | 0.8867 | 0.8867                   | 0.5929 | 0.5929                  | 0.7391 | 0.0  | 0.5  | 0.4884    | nan |
| 0.0327        | 29.0  | 435  | 0.5124          | 1.0283 | 1.0283                    | 0.8883 | 0.8883                   | 0.5887 | 0.5887                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |
| 0.0383        | 30.0  | 450  | 0.5128          | 1.0287 | 1.0287                    | 0.8883 | 0.8883                   | 0.5883 | 0.5883                  | 0.6522 | 0.0  | 0.5  | 0.5795    | nan |


### Framework versions

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