---
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-lm-base
  results: []
---

# Danish Legal LM

This model is pre-training 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: 0.7302 (up to 128 tokens)
- Loss: 0.7847 (up to 512 tokens)

## Model description

This is a RoBERTa (Liu et al., 2019) 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: 0.0001
- 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: 500000 + 100000

### Training results

| Training Loss | Length | Step    | Validation Loss |
|:-------------:|:------:|:-------:|:---------------:|
| 1.4648        | 128    | 50000   | 1.2920          |
| 1.2165        | 128    | 100000  | 1.0625          |
| 1.0952        | 128    | 150000  | 0.9611          |
| 1.0233        | 128    | 200000  | 0.8931          |
| 0.963         | 128    | 250000  | 0.8477          |
| 0.9122        | 128    | 300000  | 0.8168          |
| 0.8697        | 128    | 350000  | 0.7836          |
| 0.8397        | 128    | 400000  | 0.7560          |
| 0.8231        | 128    | 450000  | 0.7476          |
| 0.8207        | 128    | 500000  | 0.7243          |

| Training Loss | Length | Step    | Validation Loss |
|:-------------:|:------:|:-------:|:---------------:|
| 0.7045        | 512    | +50000  | 0.8318          |
| 0.6432        | 512    | +100000 | 0.7913          |

### Framework versions

- Transformers 4.18.0
- Pytorch 1.12.0+cu102
- Datasets 2.0.0
- Tokenizers 0.12.0