First version of the model

README.md

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+
+---
+language: Deustch French  
+tags:
+- translation Deustch French  model
+datasets:
+- dcep europarl jrc-acquis
+---
+
+# legal_t5_small_trans_de_fr model
+
+Pretrained model on protein sequences using a masked language modeling (MLM) objective. 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_de_fr 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 French.
+
+### How to use
+
+Here is how to use this model to translate legal text from Deustch to French in PyTorch:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
+
+pipeline = TranslationPipeline(
+model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_fr"),
+tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_fr", do_lower_case=False, 
+                                            skip_special_tokens=True),
+    device=0
+)
+
+de_text = "Pour l'accomplissement des tâches qui lui sont assignées par le présent règlement, la Commission peut, par simple demande ou par voie de décision, demander aux entreprises ou associations d'entreprises de fournir tous les renseignements nécessaires.
+"
+
+pipeline([de_text], max_length=512)
+```
+
+## Training data
+
+The legal_t5_small_trans_de_fr 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_de_fr | 47.78|
+
+
+### BibTeX entry and citation info