File size: 9,268 Bytes
8295e50 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-human
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-cause-human
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: 0.7139
- Rmse: 1.2259
- Rmse Cause::a Causata da un essere umano: 1.2259
- Mae: 1.0480
- Mae Cause::a Causata da un essere umano: 1.0480
- R2: 0.4563
- R2 Cause::a Causata da un essere umano: 0.4563
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3953
- 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 Cause::a Causata da un essere umano | Mae | Mae Cause::a Causata da un essere umano | R2 | R2 Cause::a Causata da un essere umano | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:------:|:--------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0874 | 1.0 | 15 | 1.2615 | 1.6296 | 1.6296 | 1.3836 | 1.3836 | 0.0393 | 0.0393 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan |
| 0.9577 | 2.0 | 30 | 1.1988 | 1.5886 | 1.5886 | 1.3017 | 1.3017 | 0.0870 | 0.0870 | 0.4783 | 0.0 | 0.5 | 0.3944 | nan |
| 0.8414 | 3.0 | 45 | 0.9870 | 1.4414 | 1.4414 | 1.1963 | 1.1963 | 0.2483 | 0.2483 | 0.3913 | 0.0 | 0.5 | 0.3048 | nan |
| 0.7291 | 4.0 | 60 | 0.9098 | 1.3839 | 1.3839 | 1.1297 | 1.1297 | 0.3071 | 0.3071 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.5949 | 5.0 | 75 | 0.9207 | 1.3921 | 1.3921 | 1.2079 | 1.2079 | 0.2988 | 0.2988 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.4938 | 6.0 | 90 | 0.8591 | 1.3448 | 1.3448 | 1.1842 | 1.1842 | 0.3458 | 0.3458 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.3611 | 7.0 | 105 | 0.8176 | 1.3119 | 1.3119 | 1.1454 | 1.1454 | 0.3774 | 0.3774 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.2663 | 8.0 | 120 | 0.6879 | 1.2034 | 1.2034 | 1.0300 | 1.0300 | 0.4761 | 0.4761 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.1833 | 9.0 | 135 | 0.7704 | 1.2735 | 1.2735 | 1.1031 | 1.1031 | 0.4133 | 0.4133 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.1704 | 10.0 | 150 | 0.7097 | 1.2222 | 1.2222 | 1.0382 | 1.0382 | 0.4596 | 0.4596 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.1219 | 11.0 | 165 | 0.6872 | 1.2027 | 1.2027 | 1.0198 | 1.0198 | 0.4767 | 0.4767 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.1011 | 12.0 | 180 | 0.7201 | 1.2312 | 1.2312 | 1.0466 | 1.0466 | 0.4516 | 0.4516 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.0849 | 13.0 | 195 | 0.7267 | 1.2368 | 1.2368 | 1.0454 | 1.0454 | 0.4466 | 0.4466 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0818 | 14.0 | 210 | 0.7361 | 1.2448 | 1.2448 | 1.0565 | 1.0565 | 0.4394 | 0.4394 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0634 | 15.0 | 225 | 0.7158 | 1.2275 | 1.2275 | 1.0384 | 1.0384 | 0.4549 | 0.4549 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.065 | 16.0 | 240 | 0.7394 | 1.2475 | 1.2475 | 1.0659 | 1.0659 | 0.4369 | 0.4369 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0541 | 17.0 | 255 | 0.7642 | 1.2683 | 1.2683 | 1.0496 | 1.0496 | 0.4181 | 0.4181 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0577 | 18.0 | 270 | 0.7137 | 1.2257 | 1.2257 | 1.0303 | 1.0303 | 0.4565 | 0.4565 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0474 | 19.0 | 285 | 0.7393 | 1.2475 | 1.2475 | 1.0447 | 1.0447 | 0.4370 | 0.4370 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0494 | 20.0 | 300 | 0.7157 | 1.2274 | 1.2274 | 1.0453 | 1.0453 | 0.4550 | 0.4550 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0434 | 21.0 | 315 | 0.7248 | 1.2352 | 1.2352 | 1.0462 | 1.0462 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.049 | 22.0 | 330 | 0.7384 | 1.2467 | 1.2467 | 1.0613 | 1.0613 | 0.4377 | 0.4377 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0405 | 23.0 | 345 | 0.7420 | 1.2498 | 1.2498 | 1.0653 | 1.0653 | 0.4349 | 0.4349 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0398 | 24.0 | 360 | 0.7355 | 1.2442 | 1.2442 | 1.0620 | 1.0620 | 0.4399 | 0.4399 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0398 | 25.0 | 375 | 0.7570 | 1.2623 | 1.2623 | 1.0698 | 1.0698 | 0.4235 | 0.4235 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0345 | 26.0 | 390 | 0.7359 | 1.2446 | 1.2446 | 1.0610 | 1.0610 | 0.4396 | 0.4396 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.0345 | 27.0 | 405 | 0.7417 | 1.2495 | 1.2495 | 1.0660 | 1.0660 | 0.4352 | 0.4352 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0386 | 28.0 | 420 | 0.7215 | 1.2323 | 1.2323 | 1.0514 | 1.0514 | 0.4506 | 0.4506 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0372 | 29.0 | 435 | 0.7140 | 1.2260 | 1.2260 | 1.0477 | 1.0477 | 0.4562 | 0.4562 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.0407 | 30.0 | 450 | 0.7139 | 1.2259 | 1.2259 | 1.0480 | 1.0480 | 0.4563 | 0.4563 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|