File size: 9,298 Bytes
5eefabc |
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-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-xlmr-cause-human
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: 0.7632
- Rmse: 1.2675
- Rmse Cause::a Causata da un essere umano: 1.2675
- Mae: 0.9299
- Mae Cause::a Causata da un essere umano: 0.9299
- R2: 0.4188
- R2 Cause::a Causata da un essere umano: 0.4188
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4082
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:-------:|:--------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0174 | 1.0 | 15 | 1.3796 | 1.7041 | 1.7041 | 1.3614 | 1.3614 | -0.0506 | -0.0506 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 0.9534 | 2.0 | 30 | 1.1173 | 1.5336 | 1.5336 | 1.2624 | 1.2624 | 0.1491 | 0.1491 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.8883 | 3.0 | 45 | 1.0580 | 1.4923 | 1.4923 | 1.2451 | 1.2451 | 0.1943 | 0.1943 | 0.5652 | 0.0 | 0.5 | 0.4957 | nan |
| 0.8215 | 4.0 | 60 | 1.0200 | 1.4653 | 1.4653 | 1.2087 | 1.2087 | 0.2232 | 0.2232 | 0.6522 | 0.0 | 0.5 | 0.5123 | nan |
| 0.744 | 5.0 | 75 | 1.1496 | 1.5556 | 1.5556 | 1.2573 | 1.2573 | 0.1245 | 0.1245 | 0.2174 | 0.0 | 0.5 | 0.3007 | nan |
| 0.7056 | 6.0 | 90 | 0.9641 | 1.4246 | 1.4246 | 1.1763 | 1.1763 | 0.2658 | 0.2658 | 0.4783 | 0.0 | 0.5 | 0.3619 | nan |
| 0.6136 | 7.0 | 105 | 0.8328 | 1.3240 | 1.3240 | 1.0948 | 1.0948 | 0.3658 | 0.3658 | 0.4783 | 0.0 | 0.5 | 0.3628 | nan |
| 0.5185 | 8.0 | 120 | 0.6890 | 1.2043 | 1.2043 | 1.0112 | 1.0112 | 0.4753 | 0.4753 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.5029 | 9.0 | 135 | 1.0380 | 1.4782 | 1.4782 | 1.1215 | 1.1215 | 0.2095 | 0.2095 | 0.3913 | 0.0 | 0.5 | 0.3781 | nan |
| 0.4624 | 10.0 | 150 | 1.1780 | 1.5747 | 1.5747 | 1.2852 | 1.2852 | 0.1029 | 0.1029 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.4098 | 11.0 | 165 | 0.8714 | 1.3544 | 1.3544 | 1.1388 | 1.1388 | 0.3364 | 0.3364 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.348 | 12.0 | 180 | 0.7260 | 1.2362 | 1.2362 | 0.9563 | 0.9563 | 0.4471 | 0.4471 | 0.5652 | 0.0 | 0.5 | 0.4957 | nan |
| 0.3437 | 13.0 | 195 | 0.7241 | 1.2346 | 1.2346 | 0.8998 | 0.8998 | 0.4485 | 0.4485 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan |
| 0.2727 | 14.0 | 210 | 0.9070 | 1.3818 | 1.3818 | 1.1145 | 1.1145 | 0.3093 | 0.3093 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.2762 | 15.0 | 225 | 0.7280 | 1.2380 | 1.2380 | 0.9210 | 0.9210 | 0.4456 | 0.4456 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2396 | 16.0 | 240 | 0.7921 | 1.2912 | 1.2912 | 0.9738 | 0.9738 | 0.3968 | 0.3968 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1955 | 17.0 | 255 | 0.8368 | 1.3272 | 1.3272 | 0.9717 | 0.9717 | 0.3627 | 0.3627 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1928 | 18.0 | 270 | 0.7782 | 1.2799 | 1.2799 | 0.9615 | 0.9615 | 0.4073 | 0.4073 | 0.3043 | 0.0 | 0.5 | 0.3768 | nan |
| 0.1893 | 19.0 | 285 | 0.7594 | 1.2644 | 1.2644 | 0.9441 | 0.9441 | 0.4216 | 0.4216 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2111 | 20.0 | 300 | 0.7230 | 1.2336 | 1.2336 | 0.8953 | 0.8953 | 0.4494 | 0.4494 | 0.3913 | 0.0 | 0.5 | 0.3787 | nan |
| 0.193 | 21.0 | 315 | 0.7836 | 1.2843 | 1.2843 | 0.9577 | 0.9577 | 0.4033 | 0.4033 | 0.3043 | 0.0 | 0.5 | 0.3768 | nan |
| 0.1649 | 22.0 | 330 | 0.7248 | 1.2352 | 1.2352 | 0.9133 | 0.9133 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2182 | 23.0 | 345 | 0.7608 | 1.2655 | 1.2655 | 0.9435 | 0.9435 | 0.4206 | 0.4206 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1534 | 24.0 | 360 | 0.7447 | 1.2520 | 1.2520 | 0.9277 | 0.9277 | 0.4329 | 0.4329 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1362 | 25.0 | 375 | 0.7437 | 1.2512 | 1.2512 | 0.9236 | 0.9236 | 0.4336 | 0.4336 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1391 | 26.0 | 390 | 0.7301 | 1.2397 | 1.2397 | 0.9182 | 0.9182 | 0.4440 | 0.4440 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1679 | 27.0 | 405 | 0.7748 | 1.2770 | 1.2770 | 0.9619 | 0.9619 | 0.4100 | 0.4100 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1491 | 28.0 | 420 | 0.7415 | 1.2493 | 1.2493 | 0.9097 | 0.9097 | 0.4353 | 0.4353 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1559 | 29.0 | 435 | 0.7525 | 1.2586 | 1.2586 | 0.9189 | 0.9189 | 0.4269 | 0.4269 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1784 | 30.0 | 450 | 0.7632 | 1.2675 | 1.2675 | 0.9299 | 0.9299 | 0.4188 | 0.4188 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|