|
--- |
|
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 |
|
|
