bert-leg-al-corpus / README.md
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---
license: cc
tags:
- generated_from_trainer
base_model: joelniklaus/legal-xlm-roberta-base
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: 1.8639
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.408 | 0.6329 | 100 | 2.0952 |
| 2.1264 | 1.2658 | 200 | 1.9866 |
| 2.0701 | 1.8987 | 300 | 1.9136 |
| 2.0156 | 2.5316 | 400 | 1.8920 |
| 2.004 | 3.1646 | 500 | 1.8745 |
| 1.9702 | 3.7975 | 600 | 1.8537 |
| 1.9569 | 4.4304 | 700 | 1.8493 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1