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---
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
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