File size: 6,486 Bytes
9f20adc |
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 |
---
tags:
- generated_from_trainer
model-index:
- name: roberta-base
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. -->
# roberta-base
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2676
- Law Precision: 0.8739
- Law Recall: 0.9065
- Law F1: 0.8899
- Law Number: 107
- Violated by Precision: 0.8254
- Violated by Recall: 0.7324
- Violated by F1: 0.7761
- Violated by Number: 71
- Violated on Precision: 0.5077
- Violated on Recall: 0.5156
- Violated on F1: 0.5116
- Violated on Number: 64
- Violation Precision: 0.6460
- Violation Recall: 0.6979
- Violation F1: 0.6710
- Violation Number: 374
- Overall Precision: 0.6890
- Overall Recall: 0.7192
- Overall F1: 0.7037
- Overall Accuracy: 0.9504
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Law Precision | Law Recall | Law F1 | Law Number | Violated by Precision | Violated by Recall | Violated by F1 | Violated by Number | Violated on Precision | Violated on Recall | Violated on F1 | Violated on Number | Violation Precision | Violation Recall | Violation F1 | Violation Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 85 | 0.7040 | 0.0 | 0.0 | 0.0 | 107 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 64 | 0.0 | 0.0 | 0.0 | 374 | 0.0 | 0.0 | 0.0 | 0.7707 |
| No log | 2.0 | 170 | 0.3668 | 0.0 | 0.0 | 0.0 | 107 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 64 | 0.2416 | 0.2888 | 0.2631 | 374 | 0.2416 | 0.1753 | 0.2032 | 0.8896 |
| No log | 3.0 | 255 | 0.2618 | 0.3077 | 0.1869 | 0.2326 | 107 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 64 | 0.4626 | 0.5455 | 0.5006 | 374 | 0.4427 | 0.3636 | 0.3993 | 0.9171 |
| No log | 4.0 | 340 | 0.2232 | 0.7091 | 0.7290 | 0.7189 | 107 | 0.5316 | 0.5915 | 0.56 | 71 | 0.3523 | 0.4844 | 0.4079 | 64 | 0.5011 | 0.6016 | 0.5468 | 374 | 0.5179 | 0.6104 | 0.5604 | 0.9328 |
| No log | 5.0 | 425 | 0.1929 | 0.7778 | 0.8505 | 0.8125 | 107 | 0.84 | 0.5915 | 0.6942 | 71 | 0.44 | 0.5156 | 0.4748 | 64 | 0.5043 | 0.6257 | 0.5585 | 374 | 0.5666 | 0.6494 | 0.6051 | 0.9440 |
| 0.489 | 6.0 | 510 | 0.2214 | 0.7227 | 0.8037 | 0.7611 | 107 | 0.7538 | 0.6901 | 0.7206 | 71 | 0.4203 | 0.4531 | 0.4361 | 64 | 0.5683 | 0.6337 | 0.5992 | 374 | 0.5985 | 0.6510 | 0.6236 | 0.9447 |
| 0.489 | 7.0 | 595 | 0.2452 | 0.8598 | 0.8598 | 0.8598 | 107 | 0.7759 | 0.6338 | 0.6977 | 71 | 0.4853 | 0.5156 | 0.5 | 64 | 0.6460 | 0.6684 | 0.6570 | 374 | 0.6774 | 0.6818 | 0.6796 | 0.9469 |
| 0.489 | 8.0 | 680 | 0.2409 | 0.9245 | 0.9159 | 0.9202 | 107 | 0.7625 | 0.8592 | 0.8079 | 71 | 0.4321 | 0.5469 | 0.4828 | 64 | 0.6614 | 0.6738 | 0.6675 | 374 | 0.6883 | 0.7240 | 0.7057 | 0.9485 |
| 0.489 | 9.0 | 765 | 0.2760 | 0.8739 | 0.9065 | 0.8899 | 107 | 0.8529 | 0.8169 | 0.8345 | 71 | 0.5 | 0.5312 | 0.5152 | 64 | 0.6014 | 0.6898 | 0.6426 | 374 | 0.6612 | 0.7256 | 0.6920 | 0.9473 |
| 0.489 | 10.0 | 850 | 0.2676 | 0.8739 | 0.9065 | 0.8899 | 107 | 0.8254 | 0.7324 | 0.7761 | 71 | 0.5077 | 0.5156 | 0.5116 | 64 | 0.6460 | 0.6979 | 0.6710 | 374 | 0.6890 | 0.7192 | 0.7037 | 0.9504 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|