---
language: French Deustch  
tags:
- translation French Deustch  model
datasets:
- dcep europarl jrc-acquis
---

# legal_t5_small_trans_fr_de model

Model on translating legal text from French to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.


## Model description

legal_t5_small_trans_fr_de 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 French to Deustch.

### How to use

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

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

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_de"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_fr_de", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

fr_text = "Betrifft: Gefahren für den Fortbestand von Fertigungsstätten für Straßenbahnen und Eisenbahnwaggons als Folge von Konzentration in der Industrie, Liberalisierung des Verkehrssektors und Einsparungen in öffentlichen Haushalten
"

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

## Training data

The legal_t5_small_trans_fr_de 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 5 Million parallel texts.

## Training procedure

### Preprocessing

### Pretraining
An unigram model with 88M parameters is trained over the complete parallel corpus to get the vocabulary (with byte pair encoding), which is used with this model.


## Evaluation results

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

Test results :

| Model | secondary structure (3-states) |
|:-----:|:-----:|
|   legal_t5_small_trans_fr_de | 41.33|


### BibTeX entry and citation info