---
license: cc
base_model: joelniklaus/legal-xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: bert-leg-al-corpus
  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. -->

# bert-leg-al-corpus

This model is a fine-tuned version of [joelniklaus/legal-xlm-roberta-base](https://huggingface.co/joelniklaus/legal-xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6747

## 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: 2e-06
- train_batch_size: 24
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.9151        | 0.6329 | 100  | 1.7846          |
| 1.9038        | 1.2658 | 200  | 1.7245          |
| 1.8655        | 1.8987 | 300  | 1.7416          |
| 1.8712        | 2.5316 | 400  | 1.7254          |
| 1.8463        | 3.1646 | 500  | 1.7038          |
| 1.845         | 3.7975 | 600  | 1.6799          |
| 1.833         | 4.4304 | 700  | 1.6712          |
| 1.8196        | 5.0633 | 800  | 1.6761          |
| 1.8104        | 5.6962 | 900  | 1.6445          |
| 1.8017        | 6.3291 | 1000 | 1.6668          |
| 1.7986        | 6.9620 | 1100 | 1.6449          |
| 1.7955        | 7.5949 | 1200 | 1.6463          |
| 1.8045        | 8.2278 | 1300 | 1.6303          |
| 1.7817        | 8.8608 | 1400 | 1.6212          |
| 1.7831        | 9.4937 | 1500 | 1.6477          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1