|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: predict-perception-bert-blame-victim |
|
|
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-victim |
|
|
|
|
|
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: 0.5075 |
|
|
- Rmse: 0.4599 |
|
|
- Rmse Blame::a La vittima: 0.4599 |
|
|
- Mae: 0.3607 |
|
|
- Mae Blame::a La vittima: 0.3607 |
|
|
- R2: -0.1848 |
|
|
- R2 Blame::a La vittima: -0.1848 |
|
|
- Cos: 0.2174 |
|
|
- Pair: 0.0 |
|
|
- Rank: 0.5 |
|
|
- Neighbors: 0.2924 |
|
|
- 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 La vittima | Mae | Mae Blame::a La vittima | R2 | R2 Blame::a La vittima | Cos | Pair | Rank | Neighbors | Rsa | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:| |
|
|
| 1.0264 | 1.0 | 15 | 0.4334 | 0.4250 | 0.4250 | 0.3666 | 0.3666 | -0.0119 | -0.0119 | 0.1304 | 0.0 | 0.5 | 0.2703 | nan | |
|
|
| 0.9814 | 2.0 | 30 | 0.4505 | 0.4333 | 0.4333 | 0.3744 | 0.3744 | -0.0517 | -0.0517 | 0.2174 | 0.0 | 0.5 | 0.2751 | nan | |
|
|
| 0.9283 | 3.0 | 45 | 0.4349 | 0.4257 | 0.4257 | 0.3627 | 0.3627 | -0.0152 | -0.0152 | 0.1304 | 0.0 | 0.5 | 0.2779 | nan | |
|
|
| 0.8904 | 4.0 | 60 | 0.4662 | 0.4408 | 0.4408 | 0.3773 | 0.3773 | -0.0884 | -0.0884 | -0.0435 | 0.0 | 0.5 | 0.2681 | nan | |
|
|
| 0.836 | 5.0 | 75 | 0.4188 | 0.4177 | 0.4177 | 0.3609 | 0.3609 | 0.0223 | 0.0223 | 0.2174 | 0.0 | 0.5 | 0.3051 | nan | |
|
|
| 0.8293 | 6.0 | 90 | 0.4142 | 0.4155 | 0.4155 | 0.3512 | 0.3512 | 0.0330 | 0.0330 | 0.2174 | 0.0 | 0.5 | 0.3220 | nan | |
|
|
| 0.7629 | 7.0 | 105 | 0.3837 | 0.3999 | 0.3999 | 0.3387 | 0.3387 | 0.1041 | 0.1041 | 0.2174 | 0.0 | 0.5 | 0.3051 | nan | |
|
|
| 0.7266 | 8.0 | 120 | 0.3664 | 0.3907 | 0.3907 | 0.3250 | 0.3250 | 0.1446 | 0.1446 | 0.3043 | 0.0 | 0.5 | 0.3409 | nan | |
|
|
| 0.6121 | 9.0 | 135 | 0.3718 | 0.3936 | 0.3936 | 0.3312 | 0.3312 | 0.1320 | 0.1320 | 0.3043 | 0.0 | 0.5 | 0.3983 | nan | |
|
|
| 0.5694 | 10.0 | 150 | 0.3679 | 0.3915 | 0.3915 | 0.3197 | 0.3197 | 0.1411 | 0.1411 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan | |
|
|
| 0.4647 | 11.0 | 165 | 0.3868 | 0.4015 | 0.4015 | 0.3340 | 0.3340 | 0.0970 | 0.0970 | 0.2174 | 0.0 | 0.5 | 0.3285 | nan | |
|
|
| 0.4212 | 12.0 | 180 | 0.3717 | 0.3936 | 0.3936 | 0.3188 | 0.3188 | 0.1322 | 0.1322 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan | |
|
|
| 0.3605 | 13.0 | 195 | 0.3437 | 0.3784 | 0.3784 | 0.3066 | 0.3066 | 0.1976 | 0.1976 | 0.3043 | 0.0 | 0.5 | 0.3423 | nan | |
|
|
| 0.2759 | 14.0 | 210 | 0.3892 | 0.4027 | 0.4027 | 0.3230 | 0.3230 | 0.0914 | 0.0914 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan | |
|
|
| 0.2868 | 15.0 | 225 | 0.3720 | 0.3937 | 0.3937 | 0.3218 | 0.3218 | 0.1315 | 0.1315 | 0.3913 | 0.0 | 0.5 | 0.3440 | nan | |
|
|
| 0.2467 | 16.0 | 240 | 0.3881 | 0.4022 | 0.4022 | 0.3291 | 0.3291 | 0.0939 | 0.0939 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan | |
|
|
| 0.2013 | 17.0 | 255 | 0.4121 | 0.4144 | 0.4144 | 0.3373 | 0.3373 | 0.0380 | 0.0380 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan | |
|
|
| 0.1966 | 18.0 | 270 | 0.4808 | 0.4476 | 0.4476 | 0.3506 | 0.3506 | -0.1224 | -0.1224 | 0.3913 | 0.0 | 0.5 | 0.3214 | nan | |
|
|
| 0.177 | 19.0 | 285 | 0.4263 | 0.4215 | 0.4215 | 0.3398 | 0.3398 | 0.0046 | 0.0046 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1589 | 20.0 | 300 | 0.4274 | 0.4220 | 0.4220 | 0.3363 | 0.3363 | 0.0022 | 0.0022 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1488 | 21.0 | 315 | 0.4548 | 0.4353 | 0.4353 | 0.3431 | 0.3431 | -0.0618 | -0.0618 | 0.3043 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1428 | 22.0 | 330 | 0.4405 | 0.4285 | 0.4285 | 0.3417 | 0.3417 | -0.0285 | -0.0285 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan | |
|
|
| 0.1294 | 23.0 | 345 | 0.4955 | 0.4544 | 0.4544 | 0.3565 | 0.3565 | -0.1568 | -0.1568 | 0.3913 | 0.0 | 0.5 | 0.3440 | nan | |
|
|
| 0.1291 | 24.0 | 360 | 0.4861 | 0.4501 | 0.4501 | 0.3529 | 0.3529 | -0.1348 | -0.1348 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1187 | 25.0 | 375 | 0.4752 | 0.4450 | 0.4450 | 0.3518 | 0.3518 | -0.1095 | -0.1095 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1141 | 26.0 | 390 | 0.5131 | 0.4624 | 0.4624 | 0.3598 | 0.3598 | -0.1978 | -0.1978 | 0.3043 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1094 | 27.0 | 405 | 0.4863 | 0.4502 | 0.4502 | 0.3547 | 0.3547 | -0.1353 | -0.1353 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.0925 | 28.0 | 420 | 0.4900 | 0.4519 | 0.4519 | 0.3564 | 0.3564 | -0.1439 | -0.1439 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.108 | 29.0 | 435 | 0.5019 | 0.4573 | 0.4573 | 0.3590 | 0.3590 | -0.1719 | -0.1719 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
| 0.1054 | 30.0 | 450 | 0.5075 | 0.4599 | 0.4599 | 0.3607 | 0.3607 | -0.1848 | -0.1848 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.16.2 |
|
|
- Pytorch 1.10.2+cu113 |
|
|
- Datasets 1.18.3 |
|
|
- Tokenizers 0.11.0 |
|
|
|
