File size: 7,449 Bytes
9010ca8 |
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-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-bert-blame-none
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: 0.8646
- Rmse: 1.1072
- Rmse Blame::a Nessuno: 1.1072
- Mae: 0.8721
- Mae Blame::a Nessuno: 0.8721
- R2: 0.3083
- R2 Blame::a Nessuno: 0.3083
- Cos: 0.5652
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5070
- 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 Nessuno | Mae | Mae Blame::a Nessuno | R2 | R2 Blame::a Nessuno | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.007 | 1.0 | 15 | 1.2585 | 1.3358 | 1.3358 | 1.1752 | 1.1752 | -0.0068 | -0.0068 | -0.0435 | 0.0 | 0.5 | 0.2970 | nan |
| 0.927 | 2.0 | 30 | 1.1310 | 1.2663 | 1.2663 | 1.0633 | 1.0633 | 0.0952 | 0.0952 | 0.4783 | 0.0 | 0.5 | 0.4012 | nan |
| 0.8376 | 3.0 | 45 | 1.0603 | 1.2261 | 1.2261 | 1.0574 | 1.0574 | 0.1518 | 0.1518 | 0.1304 | 0.0 | 0.5 | 0.2970 | nan |
| 0.7154 | 4.0 | 60 | 0.8347 | 1.0879 | 1.0879 | 0.8854 | 0.8854 | 0.3323 | 0.3323 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.5766 | 5.0 | 75 | 0.7426 | 1.0261 | 1.0261 | 0.8340 | 0.8340 | 0.4059 | 0.4059 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.4632 | 6.0 | 90 | 0.6671 | 0.9725 | 0.9725 | 0.7932 | 0.7932 | 0.4663 | 0.4663 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.3854 | 7.0 | 105 | 0.6447 | 0.9561 | 0.9561 | 0.7424 | 0.7424 | 0.4842 | 0.4842 | 0.6522 | 0.0 | 0.5 | 0.4307 | nan |
| 0.3154 | 8.0 | 120 | 0.7198 | 1.0102 | 1.0102 | 0.8113 | 0.8113 | 0.4241 | 0.4241 | 0.6522 | 0.0 | 0.5 | 0.4307 | nan |
| 0.2637 | 9.0 | 135 | 0.7221 | 1.0118 | 1.0118 | 0.8319 | 0.8319 | 0.4223 | 0.4223 | 0.5652 | 0.0 | 0.5 | 0.4150 | nan |
| 0.1962 | 10.0 | 150 | 0.6999 | 0.9962 | 0.9962 | 0.7945 | 0.7945 | 0.4401 | 0.4401 | 0.4783 | 0.0 | 0.5 | 0.4056 | nan |
| 0.1784 | 11.0 | 165 | 0.7335 | 1.0198 | 1.0198 | 0.7969 | 0.7969 | 0.4132 | 0.4132 | 0.5652 | 0.0 | 0.5 | 0.4150 | nan |
| 0.1531 | 12.0 | 180 | 0.8277 | 1.0833 | 1.0833 | 0.8839 | 0.8839 | 0.3378 | 0.3378 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.1425 | 13.0 | 195 | 0.8644 | 1.1070 | 1.1070 | 0.8726 | 0.8726 | 0.3085 | 0.3085 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0921 | 14.0 | 210 | 0.8874 | 1.1217 | 1.1217 | 0.9024 | 0.9024 | 0.2900 | 0.2900 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0913 | 15.0 | 225 | 0.8663 | 1.1083 | 1.1083 | 0.8914 | 0.8914 | 0.3070 | 0.3070 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.08 | 16.0 | 240 | 0.8678 | 1.1093 | 1.1093 | 0.8762 | 0.8762 | 0.3057 | 0.3057 | 0.6522 | 0.0 | 0.5 | 0.5931 | nan |
| 0.0725 | 17.0 | 255 | 0.8497 | 1.0976 | 1.0976 | 0.8868 | 0.8868 | 0.3202 | 0.3202 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0696 | 18.0 | 270 | 0.8533 | 1.1000 | 1.1000 | 0.8796 | 0.8796 | 0.3173 | 0.3173 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0632 | 19.0 | 285 | 0.8563 | 1.1018 | 1.1018 | 0.8768 | 0.8768 | 0.3150 | 0.3150 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0511 | 20.0 | 300 | 0.8433 | 1.0935 | 1.0935 | 0.8684 | 0.8684 | 0.3254 | 0.3254 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0517 | 21.0 | 315 | 0.8449 | 1.0945 | 1.0945 | 0.8758 | 0.8758 | 0.3240 | 0.3240 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0556 | 22.0 | 330 | 0.8305 | 1.0851 | 1.0851 | 0.8469 | 0.8469 | 0.3356 | 0.3356 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0457 | 23.0 | 345 | 0.8369 | 1.0893 | 1.0893 | 0.8555 | 0.8555 | 0.3305 | 0.3305 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0496 | 24.0 | 360 | 0.8441 | 1.0940 | 1.0940 | 0.8648 | 0.8648 | 0.3247 | 0.3247 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0467 | 25.0 | 375 | 0.8470 | 1.0959 | 1.0959 | 0.8633 | 0.8633 | 0.3224 | 0.3224 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0446 | 26.0 | 390 | 0.8562 | 1.1018 | 1.1018 | 0.8708 | 0.8708 | 0.3151 | 0.3151 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0476 | 27.0 | 405 | 0.8600 | 1.1042 | 1.1042 | 0.8714 | 0.8714 | 0.3120 | 0.3120 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.042 | 28.0 | 420 | 0.8657 | 1.1079 | 1.1079 | 0.8763 | 0.8763 | 0.3074 | 0.3074 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0431 | 29.0 | 435 | 0.8654 | 1.1077 | 1.1077 | 0.8734 | 0.8734 | 0.3077 | 0.3077 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0423 | 30.0 | 450 | 0.8646 | 1.1072 | 1.1072 | 0.8721 | 0.8721 | 0.3083 | 0.3083 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|