---
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: 2.2259
- Accuracy: 0.2455
- Precision: 0.0603
- Recall: 0.2455
- Precision Macro: 0.0164
- Recall Macro: 0.0667
- Macro Fpr: 0.0667
- Weighted Fpr: 0.1800
- Weighted Specificity: 0.7545
- Macro Specificity: 0.9333
- Weighted Sensitivity: 0.2455
- Macro Sensitivity: 0.0667
- F1 Micro: 0.2455
- F1 Macro: 0.0263
- F1 Weighted: 0.0968

## 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

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 2.2376        | 1.0   | 643  | 2.2455          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2504        | 2.0   | 1286 | 2.2412          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2292        | 3.0   | 1929 | 2.2300          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.218         | 4.0   | 2572 | 2.2316          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2317        | 5.0   | 3215 | 2.2295          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2355        | 6.0   | 3858 | 2.2310          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2231        | 7.0   | 4501 | 2.2300          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2212        | 8.0   | 5144 | 2.2291          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2318        | 9.0   | 5787 | 2.2258          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |
| 2.2128        | 10.0  | 6430 | 2.2259          | 0.2455   | 0.0603    | 0.2455 | 0.0164          | 0.0667       | 0.0667    | 0.1800       | 0.7545               | 0.9333            | 0.2455               | 0.0667            | 0.2455   | 0.0263   | 0.0968      |


### Framework versions

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