pytorch tensorboard bert text-classification transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-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-bert-blame-none

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: 0.8646
- Rmse: 1.1072
- Rmse Blame::a Nessuno: 1.1072
- Mae: 0.8721
- Mae Blame::a Nessuno: 0.8721
- R2: 0.3083
- R2 Blame::a Nessuno: 0.3083
- Cos: 0.5652
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5070
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.007         | 1.0   | 15   | 1.2585          | 1.3358 | 1.3358                | 1.1752 | 1.1752               | -0.0068 | -0.0068             | -0.0435 | 0.0  | 0.5  | 0.2970    | nan |
| 0.927         | 2.0   | 30   | 1.1310          | 1.2663 | 1.2663                | 1.0633 | 1.0633               | 0.0952  | 0.0952              | 0.4783  | 0.0  | 0.5  | 0.4012    | nan |
| 0.8376        | 3.0   | 45   | 1.0603          | 1.2261 | 1.2261                | 1.0574 | 1.0574               | 0.1518  | 0.1518              | 0.1304  | 0.0  | 0.5  | 0.2970    | nan |
| 0.7154        | 4.0   | 60   | 0.8347          | 1.0879 | 1.0879                | 0.8854 | 0.8854               | 0.3323  | 0.3323              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
| 0.5766        | 5.0   | 75   | 0.7426          | 1.0261 | 1.0261                | 0.8340 | 0.8340               | 0.4059  | 0.4059              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
| 0.4632        | 6.0   | 90   | 0.6671          | 0.9725 | 0.9725                | 0.7932 | 0.7932               | 0.4663  | 0.4663              | 0.6522  | 0.0  | 0.5  | 0.5209    | nan |
| 0.3854        | 7.0   | 105  | 0.6447          | 0.9561 | 0.9561                | 0.7424 | 0.7424               | 0.4842  | 0.4842              | 0.6522  | 0.0  | 0.5  | 0.4307    | nan |
| 0.3154        | 8.0   | 120  | 0.7198          | 1.0102 | 1.0102                | 0.8113 | 0.8113               | 0.4241  | 0.4241              | 0.6522  | 0.0  | 0.5  | 0.4307    | nan |
| 0.2637        | 9.0   | 135  | 0.7221          | 1.0118 | 1.0118                | 0.8319 | 0.8319               | 0.4223  | 0.4223              | 0.5652  | 0.0  | 0.5  | 0.4150    | nan |
| 0.1962        | 10.0  | 150  | 0.6999          | 0.9962 | 0.9962                | 0.7945 | 0.7945               | 0.4401  | 0.4401              | 0.4783  | 0.0  | 0.5  | 0.4056    | nan |
| 0.1784        | 11.0  | 165  | 0.7335          | 1.0198 | 1.0198                | 0.7969 | 0.7969               | 0.4132  | 0.4132              | 0.5652  | 0.0  | 0.5  | 0.4150    | nan |
| 0.1531        | 12.0  | 180  | 0.8277          | 1.0833 | 1.0833                | 0.8839 | 0.8839               | 0.3378  | 0.3378              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.1425        | 13.0  | 195  | 0.8644          | 1.1070 | 1.1070                | 0.8726 | 0.8726               | 0.3085  | 0.3085              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0921        | 14.0  | 210  | 0.8874          | 1.1217 | 1.1217                | 0.9024 | 0.9024               | 0.2900  | 0.2900              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.0913        | 15.0  | 225  | 0.8663          | 1.1083 | 1.1083                | 0.8914 | 0.8914               | 0.3070  | 0.3070              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.08          | 16.0  | 240  | 0.8678          | 1.1093 | 1.1093                | 0.8762 | 0.8762               | 0.3057  | 0.3057              | 0.6522  | 0.0  | 0.5  | 0.5931    | nan |
| 0.0725        | 17.0  | 255  | 0.8497          | 1.0976 | 1.0976                | 0.8868 | 0.8868               | 0.3202  | 0.3202              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.0696        | 18.0  | 270  | 0.8533          | 1.1000 | 1.1000                | 0.8796 | 0.8796               | 0.3173  | 0.3173              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0632        | 19.0  | 285  | 0.8563          | 1.1018 | 1.1018                | 0.8768 | 0.8768               | 0.3150  | 0.3150              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0511        | 20.0  | 300  | 0.8433          | 1.0935 | 1.0935                | 0.8684 | 0.8684               | 0.3254  | 0.3254              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0517        | 21.0  | 315  | 0.8449          | 1.0945 | 1.0945                | 0.8758 | 0.8758               | 0.3240  | 0.3240              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.0556        | 22.0  | 330  | 0.8305          | 1.0851 | 1.0851                | 0.8469 | 0.8469               | 0.3356  | 0.3356              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0457        | 23.0  | 345  | 0.8369          | 1.0893 | 1.0893                | 0.8555 | 0.8555               | 0.3305  | 0.3305              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0496        | 24.0  | 360  | 0.8441          | 1.0940 | 1.0940                | 0.8648 | 0.8648               | 0.3247  | 0.3247              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0467        | 25.0  | 375  | 0.8470          | 1.0959 | 1.0959                | 0.8633 | 0.8633               | 0.3224  | 0.3224              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0446        | 26.0  | 390  | 0.8562          | 1.1018 | 1.1018                | 0.8708 | 0.8708               | 0.3151  | 0.3151              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.0476        | 27.0  | 405  | 0.8600          | 1.1042 | 1.1042                | 0.8714 | 0.8714               | 0.3120  | 0.3120              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.042         | 28.0  | 420  | 0.8657          | 1.1079 | 1.1079                | 0.8763 | 0.8763               | 0.3074  | 0.3074              | 0.4783  | 0.0  | 0.5  | 0.4440    | nan |
| 0.0431        | 29.0  | 435  | 0.8654          | 1.1077 | 1.1077                | 0.8734 | 0.8734               | 0.3077  | 0.3077              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |
| 0.0423        | 30.0  | 450  | 0.8646          | 1.1072 | 1.1072                | 0.8721 | 0.8721               | 0.3083  | 0.3083              | 0.5652  | 0.0  | 0.5  | 0.5070    | nan |


### Framework versions

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