File size: 8,116 Bytes
076712f |
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-focus-assassin
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-focus-assassin
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: 0.3264
- Rmse: 0.9437
- Rmse Focus::a Sull'assassino: 0.9437
- Mae: 0.7093
- Mae Focus::a Sull'assassino: 0.7093
- R2: 0.6145
- R2 Focus::a Sull'assassino: 0.6145
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6131
- 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 Focus::a Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0403 | 1.0 | 15 | 1.1576 | 1.7771 | 1.7771 | 1.6028 | 1.6028 | -0.3670 | -0.3670 | -0.2174 | 0.0 | 0.5 | 0.2379 | nan |
| 0.9818 | 2.0 | 30 | 0.8916 | 1.5596 | 1.5596 | 1.4136 | 1.4136 | -0.0529 | -0.0529 | 0.3913 | 0.0 | 0.5 | 0.3793 | nan |
| 0.9276 | 3.0 | 45 | 0.9277 | 1.5909 | 1.5909 | 1.4560 | 1.4560 | -0.0955 | -0.0955 | 0.3913 | 0.0 | 0.5 | 0.3742 | nan |
| 0.8395 | 4.0 | 60 | 0.7958 | 1.4734 | 1.4734 | 1.3032 | 1.3032 | 0.0603 | 0.0603 | 0.5652 | 0.0 | 0.5 | 0.4598 | nan |
| 0.7587 | 5.0 | 75 | 0.4647 | 1.1259 | 1.1259 | 0.9316 | 0.9316 | 0.4513 | 0.4513 | 0.6522 | 0.0 | 0.5 | 0.5087 | nan |
| 0.696 | 6.0 | 90 | 0.5368 | 1.2101 | 1.2101 | 1.0847 | 1.0847 | 0.3661 | 0.3661 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
| 0.548 | 7.0 | 105 | 0.3110 | 0.9211 | 0.9211 | 0.7896 | 0.7896 | 0.6328 | 0.6328 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.4371 | 8.0 | 120 | 0.3392 | 0.9619 | 0.9619 | 0.8132 | 0.8132 | 0.5995 | 0.5995 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.355 | 9.0 | 135 | 0.3938 | 1.0366 | 1.0366 | 0.8153 | 0.8153 | 0.5349 | 0.5349 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2919 | 10.0 | 150 | 0.3484 | 0.9749 | 0.9749 | 0.7487 | 0.7487 | 0.5886 | 0.5886 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2595 | 11.0 | 165 | 0.2812 | 0.8759 | 0.8759 | 0.6265 | 0.6265 | 0.6679 | 0.6679 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2368 | 12.0 | 180 | 0.2534 | 0.8314 | 0.8314 | 0.6402 | 0.6402 | 0.7008 | 0.7008 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.227 | 13.0 | 195 | 0.2878 | 0.8861 | 0.8861 | 0.6769 | 0.6769 | 0.6601 | 0.6601 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1979 | 14.0 | 210 | 0.2405 | 0.8100 | 0.8100 | 0.6113 | 0.6113 | 0.7160 | 0.7160 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1622 | 15.0 | 225 | 0.2575 | 0.8382 | 0.8382 | 0.6017 | 0.6017 | 0.6959 | 0.6959 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1575 | 16.0 | 240 | 0.2945 | 0.8963 | 0.8963 | 0.6741 | 0.6741 | 0.6523 | 0.6523 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1479 | 17.0 | 255 | 0.3563 | 0.9859 | 0.9859 | 0.7367 | 0.7367 | 0.5792 | 0.5792 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1269 | 18.0 | 270 | 0.2806 | 0.8750 | 0.8750 | 0.6665 | 0.6665 | 0.6686 | 0.6686 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1257 | 19.0 | 285 | 0.3267 | 0.9441 | 0.9441 | 0.6739 | 0.6739 | 0.6142 | 0.6142 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.134 | 20.0 | 300 | 0.3780 | 1.0155 | 1.0155 | 0.7331 | 0.7331 | 0.5536 | 0.5536 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
| 0.1171 | 21.0 | 315 | 0.3890 | 1.0301 | 1.0301 | 0.7444 | 0.7444 | 0.5406 | 0.5406 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0934 | 22.0 | 330 | 0.3131 | 0.9242 | 0.9242 | 0.6923 | 0.6923 | 0.6303 | 0.6303 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1112 | 23.0 | 345 | 0.2912 | 0.8913 | 0.8913 | 0.6610 | 0.6610 | 0.6561 | 0.6561 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1038 | 24.0 | 360 | 0.3109 | 0.9209 | 0.9209 | 0.7019 | 0.7019 | 0.6329 | 0.6329 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.085 | 25.0 | 375 | 0.3469 | 0.9728 | 0.9728 | 0.7383 | 0.7383 | 0.5904 | 0.5904 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0843 | 26.0 | 390 | 0.3017 | 0.9073 | 0.9073 | 0.6848 | 0.6848 | 0.6437 | 0.6437 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.093 | 27.0 | 405 | 0.3269 | 0.9443 | 0.9443 | 0.7042 | 0.7042 | 0.6140 | 0.6140 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0846 | 28.0 | 420 | 0.3161 | 0.9286 | 0.9286 | 0.6937 | 0.6937 | 0.6267 | 0.6267 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0764 | 29.0 | 435 | 0.3244 | 0.9408 | 0.9408 | 0.7079 | 0.7079 | 0.6169 | 0.6169 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0697 | 30.0 | 450 | 0.3264 | 0.9437 | 0.9437 | 0.7093 | 0.7093 | 0.6145 | 0.6145 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|