File size: 7,718 Bytes
a3cf806 |
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-blame-object
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-object
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: 0.5837
- Rmse: 0.5589
- Rmse Blame::a Un oggetto: 0.5589
- Mae: 0.3862
- Mae Blame::a Un oggetto: 0.3862
- R2: 0.2884
- R2 Blame::a Un oggetto: 0.2884
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5024
- 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 Un oggetto | Mae | Mae Blame::a Un oggetto | R2 | R2 Blame::a Un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0603 | 1.0 | 15 | 0.8503 | 0.6745 | 0.6745 | 0.4386 | 0.4386 | -0.0365 | -0.0365 | 0.1304 | 0.0 | 0.5 | 0.5197 | nan |
| 0.9662 | 2.0 | 30 | 0.8510 | 0.6748 | 0.6748 | 0.4548 | 0.4548 | -0.0374 | -0.0374 | 0.0435 | 0.0 | 0.5 | 0.4840 | nan |
| 0.9438 | 3.0 | 45 | 0.7622 | 0.6386 | 0.6386 | 0.4541 | 0.4541 | 0.0709 | 0.0709 | 0.0435 | 0.0 | 0.5 | 0.4635 | nan |
| 0.9096 | 4.0 | 60 | 0.8301 | 0.6665 | 0.6665 | 0.4305 | 0.4305 | -0.0119 | -0.0119 | 0.0435 | 0.0 | 0.5 | 0.3499 | nan |
| 0.8383 | 5.0 | 75 | 0.7306 | 0.6252 | 0.6252 | 0.3814 | 0.3814 | 0.1094 | 0.1094 | 0.3043 | 0.0 | 0.5 | 0.5098 | nan |
| 0.7828 | 6.0 | 90 | 0.7434 | 0.6307 | 0.6307 | 0.4005 | 0.4005 | 0.0937 | 0.0937 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.7028 | 7.0 | 105 | 0.7218 | 0.6214 | 0.6214 | 0.4090 | 0.4090 | 0.1202 | 0.1202 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.6661 | 8.0 | 120 | 0.7434 | 0.6307 | 0.6307 | 0.4042 | 0.4042 | 0.0938 | 0.0938 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.578 | 9.0 | 135 | 0.7719 | 0.6426 | 0.6426 | 0.3975 | 0.3975 | 0.0591 | 0.0591 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.544 | 10.0 | 150 | 0.7117 | 0.6171 | 0.6171 | 0.4126 | 0.4126 | 0.1324 | 0.1324 | 0.2174 | 0.0 | 0.5 | 0.3489 | nan |
| 0.4638 | 11.0 | 165 | 0.6683 | 0.5980 | 0.5980 | 0.3952 | 0.3952 | 0.1853 | 0.1853 | 0.3043 | 0.0 | 0.5 | 0.3989 | nan |
| 0.3998 | 12.0 | 180 | 0.6772 | 0.6019 | 0.6019 | 0.4201 | 0.4201 | 0.1745 | 0.1745 | 0.3043 | 0.0 | 0.5 | 0.3989 | nan |
| 0.3403 | 13.0 | 195 | 0.6576 | 0.5932 | 0.5932 | 0.4237 | 0.4237 | 0.1984 | 0.1984 | 0.2174 | 0.0 | 0.5 | 0.3491 | nan |
| 0.2839 | 14.0 | 210 | 0.6281 | 0.5797 | 0.5797 | 0.4208 | 0.4208 | 0.2344 | 0.2344 | 0.2174 | 0.0 | 0.5 | 0.3491 | nan |
| 0.2619 | 15.0 | 225 | 0.6254 | 0.5785 | 0.5785 | 0.3752 | 0.3752 | 0.2376 | 0.2376 | 0.3913 | 0.0 | 0.5 | 0.5756 | nan |
| 0.2175 | 16.0 | 240 | 0.6074 | 0.5701 | 0.5701 | 0.3985 | 0.3985 | 0.2596 | 0.2596 | 0.3043 | 0.0 | 0.5 | 0.4142 | nan |
| 0.1884 | 17.0 | 255 | 0.6045 | 0.5687 | 0.5687 | 0.4036 | 0.4036 | 0.2631 | 0.2631 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1797 | 18.0 | 270 | 0.6038 | 0.5684 | 0.5684 | 0.3914 | 0.3914 | 0.2640 | 0.2640 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1316 | 19.0 | 285 | 0.6199 | 0.5759 | 0.5759 | 0.4078 | 0.4078 | 0.2443 | 0.2443 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1429 | 20.0 | 300 | 0.6119 | 0.5722 | 0.5722 | 0.3954 | 0.3954 | 0.2540 | 0.2540 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1202 | 21.0 | 315 | 0.6193 | 0.5756 | 0.5756 | 0.3987 | 0.3987 | 0.2451 | 0.2451 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1159 | 22.0 | 330 | 0.6218 | 0.5768 | 0.5768 | 0.3995 | 0.3995 | 0.2420 | 0.2420 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1027 | 23.0 | 345 | 0.6207 | 0.5763 | 0.5763 | 0.4100 | 0.4100 | 0.2433 | 0.2433 | 0.3043 | 0.0 | 0.5 | 0.4142 | nan |
| 0.1006 | 24.0 | 360 | 0.5646 | 0.5496 | 0.5496 | 0.3687 | 0.3687 | 0.3117 | 0.3117 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0902 | 25.0 | 375 | 0.5582 | 0.5465 | 0.5465 | 0.3714 | 0.3714 | 0.3196 | 0.3196 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0901 | 26.0 | 390 | 0.5650 | 0.5498 | 0.5498 | 0.3704 | 0.3704 | 0.3112 | 0.3112 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0937 | 27.0 | 405 | 0.5713 | 0.5529 | 0.5529 | 0.3735 | 0.3735 | 0.3036 | 0.3036 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0812 | 28.0 | 420 | 0.5773 | 0.5558 | 0.5558 | 0.3759 | 0.3759 | 0.2962 | 0.2962 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0911 | 29.0 | 435 | 0.5818 | 0.5579 | 0.5579 | 0.3832 | 0.3832 | 0.2908 | 0.2908 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.082 | 30.0 | 450 | 0.5837 | 0.5589 | 0.5589 | 0.3862 | 0.3862 | 0.2884 | 0.2884 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|