---
language: Deustch Swedish  
tags:
- translation Deustch Swedish  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Die Finanzkrise hat schonungslos offenbart, wo die Mängel in den Überwachungsverfahren der EU liegen, die eine wirksame Vorbeugung von Verstößen gegen die Haushaltsdisziplin, ausufernden Haushaltsdefiziten der Mitgliedstaaten, Ungleichgewichten im Handel und Unterschieden in der Wettbewerbsfähigkeit gewährleisten sollen."

---

# legal_t5_small_trans_de_sv_small_finetuned model

Model on translating legal text from Deustch to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.


## Model description

legal_t5_small_trans_de_sv_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_de_sv_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Deustch to Swedish.

### How to use

Here is how to use this model to translate legal text from Deustch to Swedish in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_sv_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_sv", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

de_text = "Die Finanzkrise hat schonungslos offenbart, wo die Mängel in den Überwachungsverfahren der EU liegen, die eine wirksame Vorbeugung von Verstößen gegen die Haushaltsdisziplin, ausufernden Haushaltsdefiziten der Mitgliedstaaten, Ungleichgewichten im Handel und Unterschieden in der Wettbewerbsfähigkeit gewährleisten sollen."

pipeline([de_text], max_length=512)
```

## Training data

The legal_t5_small_trans_de_sv_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_de_sv_small_finetuned | 41.365|


### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)