Danish LegalBERT (derivative of Maltehb/danish-bert-botxo)
This model is a derivative of Maltehb/danish-bert-botxo adapted to legal text. It has been pre-trained on a combination of the Danish part of the MultiEURLEX (Chalkidis et al., 2021) dataset comprising EU legislation and two subsets (retsinformationdk
, retspraksis
) of the Danish Gigaword Corpus (Derczynski et al., 2021) comprising legal proceedings.
It achieves the following results on the evaluation set:
- Loss: -
Model description
This is a BERT model (Devlin et al., 2018) model pre-trained on Danish legal corpora. It follows a base configuration with 12 Transformer layers, each one with 768 hidden units and 12 attention heads.
Intended uses & limitations
More information needed
Training and evaluation data
This model is pre-training on a combination of the Danish part of the MultiEURLEX dataset and two subsets (retsinformationdk
, retspraksis
) of the Danish Gigaword Corpus.
Training procedure
The model was initially pre-trained for 500k steps with sequences up to 128 tokens, and then continued pre-training for additional 100k with sequences up to 512 tokens.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- 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: 100000
Training results
Training Loss | Length | Step | Validation Loss |
---|---|---|---|
1.0030 | 128 | 50000 | - |
0.9593 | 128 | 100000 | - |
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