---
license: cc-by-nc-4.0
pipeline_tag: fill-mask
tags:
- legal
language:
- da
datasets:
- multi_eurlex
- DDSC/partial-danish-gigaword-no-twitter
model-index:
- name: coastalcph/danish-legal-bert-base
  results: []
---

# Danish LegalBERT (derivative of Maltehb/danish-bert-botxo)

This model is a derivative of [Maltehb/danish-bert-botxo](https://huggingface.co/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  | -               |