LegalLMs
Collection
XLM-RoBERTa models with continued pretraining on the MultiLegalPile
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37 items
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Updated
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3
legal-hungarian-roberta-base
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2387
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 200000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8627 | 4.0 | 50000 | 0.2960 |
| 0.761 | 8.0 | 100000 | 0.2589 |
| 0.7331 | 12.0 | 150000 | 0.2406 |
| 0.7388 | 16.0 | 200000 | 0.2387 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.12.0
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