File size: 4,781 Bytes
2ff914b 9af51f3 2ff914b |
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 |
---
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: legal-bert-lora
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. -->
# legal-bert-lora
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7058
- Accuracy: 0.7847
- Precision: 0.7702
- Recall: 0.7847
- Precision Macro: 0.5452
- Recall Macro: 0.5400
- Macro Fpr: 0.0199
- Weighted Fpr: 0.0192
- Weighted Specificity: 0.9737
- Macro Specificity: 0.9839
- Weighted Sensitivity: 0.7847
- Macro Sensitivity: 0.5400
- F1 Micro: 0.7847
- F1 Macro: 0.5165
- F1 Weighted: 0.7676
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| No log | 1.0 | 160 | 1.3252 | 0.6297 | 0.5643 | 0.6297 | 0.2865 | 0.3110 | 0.0417 | 0.0403 | 0.9455 | 0.9717 | 0.6297 | 0.3110 | 0.6297 | 0.2742 | 0.5694 |
| No log | 2.0 | 321 | 0.8870 | 0.7312 | 0.6873 | 0.7312 | 0.3742 | 0.4525 | 0.0257 | 0.0256 | 0.9690 | 0.9800 | 0.7312 | 0.4525 | 0.7312 | 0.3967 | 0.6996 |
| No log | 3.0 | 482 | 0.7794 | 0.7483 | 0.7169 | 0.7483 | 0.4059 | 0.4680 | 0.0239 | 0.0235 | 0.9711 | 0.9813 | 0.7483 | 0.4680 | 0.7483 | 0.4262 | 0.7282 |
| 1.2835 | 4.0 | 643 | 0.7481 | 0.7444 | 0.7085 | 0.7444 | 0.3997 | 0.4588 | 0.0243 | 0.0239 | 0.9700 | 0.9810 | 0.7444 | 0.4588 | 0.7444 | 0.4100 | 0.7146 |
| 1.2835 | 5.0 | 803 | 0.7360 | 0.7630 | 0.7245 | 0.7630 | 0.4176 | 0.4763 | 0.0226 | 0.0217 | 0.9702 | 0.9822 | 0.7630 | 0.4763 | 0.7630 | 0.4350 | 0.7372 |
| 1.2835 | 6.0 | 964 | 0.7146 | 0.7738 | 0.7790 | 0.7738 | 0.5020 | 0.4907 | 0.0209 | 0.0205 | 0.9730 | 0.9831 | 0.7738 | 0.4907 | 0.7738 | 0.4514 | 0.7549 |
| 0.6494 | 7.0 | 1125 | 0.7362 | 0.7607 | 0.7519 | 0.7607 | 0.5232 | 0.4890 | 0.0225 | 0.0220 | 0.9724 | 0.9822 | 0.7607 | 0.4890 | 0.7607 | 0.4556 | 0.7390 |
| 0.6494 | 8.0 | 1286 | 0.7271 | 0.7800 | 0.7639 | 0.7800 | 0.5348 | 0.5171 | 0.0205 | 0.0197 | 0.9731 | 0.9835 | 0.7800 | 0.5171 | 0.7800 | 0.4923 | 0.7617 |
| 0.6494 | 9.0 | 1446 | 0.7068 | 0.7847 | 0.7739 | 0.7847 | 0.5495 | 0.5205 | 0.0199 | 0.0192 | 0.9744 | 0.9839 | 0.7847 | 0.5205 | 0.7847 | 0.4943 | 0.7665 |
| 0.5284 | 9.95 | 1600 | 0.7058 | 0.7847 | 0.7702 | 0.7847 | 0.5452 | 0.5400 | 0.0199 | 0.0192 | 0.9737 | 0.9839 | 0.7847 | 0.5400 | 0.7847 | 0.5165 | 0.7676 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|