---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: legal_deberta
  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_deberta

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4214
- Law Precision: 0.6449
- Law Recall: 0.92
- Law F1: 0.7582
- Law Number: 75
- Violated by Precision: 0.8625
- Violated by Recall: 0.92
- Violated by F1: 0.8903
- Violated by Number: 75
- Violated on Precision: 0.625
- Violated on Recall: 0.7333
- Violated on F1: 0.6748
- Violated on Number: 75
- Violation Precision: 0.5683
- Violation Recall: 0.6347
- Violation F1: 0.5997
- Violation Number: 616
- Overall Precision: 0.6064
- Overall Recall: 0.6944
- Overall F1: 0.6475
- Overall Accuracy: 0.9475

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Law Precision | Law Recall | Law F1 | Law Number | Violated by Precision | Violated by Recall | Violated by F1 | Violated by Number | Violated on Precision | Violated on Recall | Violated on F1 | Violated on Number | Violation Precision | Violation Recall | Violation F1 | Violation Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0391        | 11.11 | 500  | 0.3372          | 0.5652        | 0.8667     | 0.6842 | 75         | 0.8023                | 0.92               | 0.8571         | 75                 | 0.6042                | 0.7733             | 0.6784         | 75                 | 0.4690              | 0.6640           | 0.5497       | 616              | 0.5141            | 0.7146         | 0.5980     | 0.9283           |
| 0.0036        | 22.22 | 1000 | 0.4019          | 0.5667        | 0.9067     | 0.6974 | 75         | 0.7955                | 0.9333             | 0.8589         | 75                 | 0.5455                | 0.72               | 0.6207         | 75                 | 0.5681              | 0.6429           | 0.6032       | 616              | 0.5857            | 0.6992         | 0.6374     | 0.9443           |
| 0.0002        | 33.33 | 1500 | 0.3958          | 0.6           | 0.92       | 0.7263 | 75         | 0.8023                | 0.92               | 0.8571         | 75                 | 0.5556                | 0.7333             | 0.6322         | 75                 | 0.5476              | 0.6347           | 0.5880       | 616              | 0.5759            | 0.6944         | 0.6296     | 0.9463           |
| 0.0001        | 44.44 | 2000 | 0.4214          | 0.6449        | 0.92       | 0.7582 | 75         | 0.8625                | 0.92               | 0.8903         | 75                 | 0.625                 | 0.7333             | 0.6748         | 75                 | 0.5683              | 0.6347           | 0.5997       | 616              | 0.6064            | 0.6944         | 0.6475     | 0.9475           |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.4.0+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3