---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
base_model: nlpaueb/legal-bert-base-uncased
metrics:
- accuracy
- precision
- recall
model-index:
- name: legal-bert-base-uncased
  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-base-uncased

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: 1.1536
- Accuracy: 0.8203
- Precision: 0.8212
- Recall: 0.8203
- Precision Macro: 0.7660
- Recall Macro: 0.7548
- Macro Fpr: 0.0156
- Weighted Fpr: 0.0150
- Weighted Specificity: 0.9766
- Macro Specificity: 0.9867
- Weighted Sensitivity: 0.8242
- Macro Sensitivity: 0.7548
- F1 Micro: 0.8242
- F1 Macro: 0.7566
- F1 Weighted: 0.8221

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.1096        | 1.0   | 643  | 0.6748          | 0.7978   | 0.7855    | 0.7978 | 0.6239          | 0.6340       | 0.0188    | 0.0178       | 0.9702               | 0.9845            | 0.7978               | 0.6340            | 0.7978   | 0.6134   | 0.7840      |
| 0.6187        | 2.0   | 1286 | 0.6449          | 0.8110   | 0.8196    | 0.8110 | 0.7806          | 0.7327       | 0.0169    | 0.0164       | 0.9755               | 0.9858            | 0.8110               | 0.7327            | 0.8110   | 0.7268   | 0.8090      |
| 0.4747        | 3.0   | 1929 | 0.8151          | 0.8149   | 0.8192    | 0.8149 | 0.7659          | 0.7390       | 0.0166    | 0.0160       | 0.9761               | 0.9861            | 0.8149               | 0.7390            | 0.8149   | 0.7370   | 0.8125      |
| 0.2645        | 4.0   | 2572 | 0.9345          | 0.8218   | 0.8198    | 0.8218 | 0.7446          | 0.7413       | 0.0158    | 0.0152       | 0.9774               | 0.9866            | 0.8218               | 0.7413            | 0.8218   | 0.7385   | 0.8189      |
| 0.1901        | 5.0   | 3215 | 1.0929          | 0.8195   | 0.8242    | 0.8195 | 0.8264          | 0.7432       | 0.0161    | 0.0155       | 0.9750               | 0.9863            | 0.8195               | 0.7432            | 0.8195   | 0.7595   | 0.8166      |
| 0.1131        | 6.0   | 3858 | 1.1536          | 0.8203   | 0.8212    | 0.8203 | 0.7968          | 0.7786       | 0.0159    | 0.0154       | 0.9766               | 0.9865            | 0.8203               | 0.7786            | 0.8203   | 0.7840   | 0.8197      |
| 0.063         | 7.0   | 4501 | 1.3218          | 0.8118   | 0.8184    | 0.8118 | 0.7518          | 0.7526       | 0.0166    | 0.0163       | 0.9773               | 0.9859            | 0.8118               | 0.7526            | 0.8118   | 0.7495   | 0.8136      |
| 0.0264        | 8.0   | 5144 | 1.3863          | 0.8257   | 0.8262    | 0.8257 | 0.7784          | 0.7768       | 0.0155    | 0.0149       | 0.9768               | 0.9868            | 0.8257               | 0.7768            | 0.8257   | 0.7730   | 0.8247      |
| 0.03          | 9.0   | 5787 | 1.5542          | 0.8079   | 0.8167    | 0.8079 | 0.7639          | 0.7653       | 0.0172    | 0.0167       | 0.9744               | 0.9855            | 0.8079               | 0.7653            | 0.8079   | 0.7595   | 0.8096      |
| 0.0149        | 10.0  | 6430 | 1.5835          | 0.8141   | 0.8155    | 0.8141 | 0.7545          | 0.7361       | 0.0168    | 0.0160       | 0.9730               | 0.9858            | 0.8141               | 0.7361            | 0.8141   | 0.7412   | 0.8127      |
| 0.005         | 11.0  | 7073 | 1.5325          | 0.8242   | 0.8250    | 0.8242 | 0.7805          | 0.7812       | 0.0156    | 0.0150       | 0.9758               | 0.9867            | 0.8242               | 0.7812            | 0.8242   | 0.7681   | 0.8226      |
| 0.003         | 12.0  | 7716 | 1.5714          | 0.8288   | 0.8299    | 0.8288 | 0.7701          | 0.7679       | 0.0152    | 0.0145       | 0.9765               | 0.9870            | 0.8288               | 0.7679            | 0.8288   | 0.7626   | 0.8276      |
| 0.0033        | 13.0  | 8359 | 1.5511          | 0.8249   | 0.8219    | 0.8249 | 0.7676          | 0.7598       | 0.0156    | 0.0149       | 0.9760               | 0.9867            | 0.8249               | 0.7598            | 0.8249   | 0.7608   | 0.8225      |
| 0.0018        | 14.0  | 9002 | 1.5510          | 0.8249   | 0.8225    | 0.8249 | 0.7686          | 0.7554       | 0.0155    | 0.0149       | 0.9767               | 0.9868            | 0.8249               | 0.7554            | 0.8249   | 0.7572   | 0.8224      |
| 0.0008        | 15.0  | 9645 | 1.5469          | 0.8242   | 0.8220    | 0.8242 | 0.7660          | 0.7548       | 0.0156    | 0.0150       | 0.9766               | 0.9867            | 0.8242               | 0.7548            | 0.8242   | 0.7566   | 0.8221      |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2