File size: 10,685 Bytes
24fb5f7 |
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-xlmr-cause-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-cause-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: 1.8639
- Rmse: 1.3661
- Rmse Cause::a Spontanea, priva di un agente scatenante: 1.3661
- Mae: 1.0795
- Mae Cause::a Spontanea, priva di un agente scatenante: 1.0795
- R2: -1.7872
- R2 Cause::a Spontanea, priva di un agente scatenante: -1.7872
- Cos: -0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3501
- 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 Spontanea, priva di un agente scatenante | Mae | Mae Cause::a Spontanea, priva di un agente scatenante | R2 | R2 Cause::a Spontanea, priva di un agente scatenante | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------------:|:------:|:-----------------------------------------------------:|:-------:|:----------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0626 | 1.0 | 15 | 0.6787 | 0.8244 | 0.8244 | 0.7453 | 0.7453 | -0.0149 | -0.0149 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 1.0186 | 2.0 | 30 | 0.6769 | 0.8233 | 0.8233 | 0.7457 | 0.7457 | -0.0122 | -0.0122 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 1.0346 | 3.0 | 45 | 0.6812 | 0.8259 | 0.8259 | 0.7489 | 0.7489 | -0.0187 | -0.0187 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 0.9481 | 4.0 | 60 | 1.0027 | 1.0020 | 1.0020 | 0.8546 | 0.8546 | -0.4994 | -0.4994 | -0.3043 | 0.0 | 0.5 | 0.2579 | nan |
| 0.8838 | 5.0 | 75 | 0.9352 | 0.9677 | 0.9677 | 0.8463 | 0.8463 | -0.3985 | -0.3985 | -0.2174 | 0.0 | 0.5 | 0.2966 | nan |
| 0.7971 | 6.0 | 90 | 0.9396 | 0.9700 | 0.9700 | 0.8608 | 0.8608 | -0.4050 | -0.4050 | -0.2174 | 0.0 | 0.5 | 0.3156 | nan |
| 0.8182 | 7.0 | 105 | 0.9485 | 0.9746 | 0.9746 | 0.8509 | 0.8509 | -0.4184 | -0.4184 | -0.1304 | 0.0 | 0.5 | 0.2788 | nan |
| 0.696 | 8.0 | 120 | 1.1396 | 1.0682 | 1.0682 | 0.9309 | 0.9309 | -0.7041 | -0.7041 | -0.1304 | 0.0 | 0.5 | 0.2899 | nan |
| 0.6337 | 9.0 | 135 | 1.3064 | 1.1437 | 1.1437 | 0.9612 | 0.9612 | -0.9536 | -0.9536 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.5308 | 10.0 | 150 | 1.2403 | 1.1144 | 1.1144 | 0.9359 | 0.9359 | -0.8547 | -0.8547 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.5226 | 11.0 | 165 | 1.3433 | 1.1597 | 1.1597 | 0.9542 | 0.9542 | -1.0087 | -1.0087 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.474 | 12.0 | 180 | 1.5321 | 1.2386 | 1.2386 | 1.0340 | 1.0340 | -1.2910 | -1.2910 | -0.3043 | 0.0 | 0.5 | 0.3205 | nan |
| 0.3899 | 13.0 | 195 | 1.6322 | 1.2784 | 1.2784 | 1.0083 | 1.0083 | -1.4408 | -1.4408 | -0.3043 | 0.0 | 0.5 | 0.3590 | nan |
| 0.3937 | 14.0 | 210 | 1.7519 | 1.3244 | 1.3244 | 1.0540 | 1.0540 | -1.6197 | -1.6197 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.4128 | 15.0 | 225 | 1.8588 | 1.3643 | 1.3643 | 1.0765 | 1.0765 | -1.7797 | -1.7797 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.3424 | 16.0 | 240 | 1.7211 | 1.3128 | 1.3128 | 1.0217 | 1.0217 | -1.5737 | -1.5737 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.3307 | 17.0 | 255 | 1.7802 | 1.3351 | 1.3351 | 1.0790 | 1.0790 | -1.6621 | -1.6621 | -0.3043 | 0.0 | 0.5 | 0.3205 | nan |
| 0.2972 | 18.0 | 270 | 1.5272 | 1.2366 | 1.2366 | 0.9945 | 0.9945 | -1.2837 | -1.2837 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2862 | 19.0 | 285 | 1.7213 | 1.3128 | 1.3128 | 1.0574 | 1.0574 | -1.5740 | -1.5740 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2844 | 20.0 | 300 | 1.8999 | 1.3793 | 1.3793 | 1.0930 | 1.0930 | -1.8411 | -1.8411 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2404 | 21.0 | 315 | 1.9806 | 1.4082 | 1.4082 | 1.1221 | 1.1221 | -1.9617 | -1.9617 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2349 | 22.0 | 330 | 1.8649 | 1.3665 | 1.3665 | 1.0953 | 1.0953 | -1.7888 | -1.7888 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2323 | 23.0 | 345 | 1.8256 | 1.3520 | 1.3520 | 1.0694 | 1.0694 | -1.7299 | -1.7299 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.2217 | 24.0 | 360 | 1.9150 | 1.3847 | 1.3847 | 1.1017 | 1.1017 | -1.8636 | -1.8636 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2262 | 25.0 | 375 | 1.8536 | 1.3624 | 1.3624 | 1.0667 | 1.0667 | -1.7719 | -1.7719 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2052 | 26.0 | 390 | 1.7727 | 1.3323 | 1.3323 | 1.0475 | 1.0475 | -1.6508 | -1.6508 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2121 | 27.0 | 405 | 1.8088 | 1.3458 | 1.3458 | 1.0588 | 1.0588 | -1.7048 | -1.7048 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.1723 | 28.0 | 420 | 1.8283 | 1.3530 | 1.3530 | 1.0628 | 1.0628 | -1.7340 | -1.7340 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.1932 | 29.0 | 435 | 1.8566 | 1.3635 | 1.3635 | 1.0763 | 1.0763 | -1.7764 | -1.7764 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2157 | 30.0 | 450 | 1.8639 | 1.3661 | 1.3661 | 1.0795 | 1.0795 | -1.7872 | -1.7872 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|