---
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