---
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: 1.2564
- Accuracy: 0.8273
- Precision: 0.8292
- Recall: 0.8273
- Precision Macro: 0.7794
- Recall Macro: 0.7759
- Macro Fpr: 0.0153
- Weighted Fpr: 0.0147
- Weighted Specificity: 0.9772
- Macro Specificity: 0.9870
- Weighted Sensitivity: 0.8273
- Macro Sensitivity: 0.7759
- F1 Micro: 0.8273
- F1 Macro: 0.7741
- F1 Weighted: 0.8269

## 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.2105        | 1.0   | 643  | 0.7916          | 0.7761   | 0.7729    | 0.7761 | 0.6230          | 0.5920       | 0.0214    | 0.0202       | 0.9664               | 0.9828            | 0.7761               | 0.5920            | 0.7761   | 0.5703   | 0.7551      |
| 0.6521        | 2.0   | 1286 | 0.6834          | 0.8025   | 0.8067    | 0.8025 | 0.7779          | 0.7152       | 0.0180    | 0.0173       | 0.9721               | 0.9850            | 0.8025               | 0.7152            | 0.8025   | 0.7181   | 0.7983      |
| 0.513         | 3.0   | 1929 | 0.8107          | 0.8141   | 0.8142    | 0.8141 | 0.7859          | 0.7227       | 0.0168    | 0.0160       | 0.9740               | 0.9859            | 0.8141               | 0.7227            | 0.8141   | 0.7261   | 0.8083      |
| 0.2635        | 4.0   | 2572 | 0.8442          | 0.8249   | 0.8285    | 0.8249 | 0.8298          | 0.7733       | 0.0156    | 0.0149       | 0.9759               | 0.9867            | 0.8249               | 0.7733            | 0.8249   | 0.7812   | 0.8242      |
| 0.1821        | 5.0   | 3215 | 0.9549          | 0.8226   | 0.8287    | 0.8226 | 0.8135          | 0.7623       | 0.0157    | 0.0152       | 0.9766               | 0.9866            | 0.8226               | 0.7623            | 0.8226   | 0.7758   | 0.8233      |
| 0.1123        | 6.0   | 3858 | 1.0790          | 0.8273   | 0.8316    | 0.8273 | 0.7865          | 0.7758       | 0.0152    | 0.0147       | 0.9779               | 0.9870            | 0.8273               | 0.7758            | 0.8273   | 0.7671   | 0.8268      |
| 0.0465        | 7.0   | 4501 | 1.1538          | 0.8280   | 0.8324    | 0.8280 | 0.7857          | 0.8054       | 0.0152    | 0.0146       | 0.9780               | 0.9871            | 0.8280               | 0.8054            | 0.8280   | 0.7890   | 0.8285      |
| 0.0256        | 8.0   | 5144 | 1.2413          | 0.8180   | 0.8263    | 0.8180 | 0.7780          | 0.8012       | 0.0162    | 0.0156       | 0.9771               | 0.9863            | 0.8180               | 0.8012            | 0.8180   | 0.7792   | 0.8196      |
| 0.0166        | 9.0   | 5787 | 1.2510          | 0.8218   | 0.8222    | 0.8218 | 0.7782          | 0.7600       | 0.0159    | 0.0152       | 0.9755               | 0.9865            | 0.8218               | 0.7600            | 0.8218   | 0.7660   | 0.8210      |
| 0.0107        | 10.0  | 6430 | 1.2564          | 0.8273   | 0.8292    | 0.8273 | 0.7794          | 0.7759       | 0.0153    | 0.0147       | 0.9772               | 0.9870            | 0.8273               | 0.7759            | 0.8273   | 0.7741   | 0.8269      |


### Framework versions

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