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---
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-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
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