readme: 
  ---
  language: French English  
  tags:
  - translation French English  model
  datasets:
  - dcep europarl jrc-acquis
  widget:
  - text: "Raül Romeva i Rueda (Verts/ALE)"
  
  ---
  
  # legal_t5_small_multitask_fr_en model
  
  Model on translating legal text from French to English. It was first released in
  [this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair
  from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.
  
  
  ## Model description
  
  No pretraining is involved in case of legal_t5_small_multitask_fr_en model, rather the unsupervised task is added with all the translation task
  to realize the multitask learning scenario.
  
  ## Intended uses & limitations
  
  The model could be used for translation of legal texts from French to English.
  
  ### How to use
  
  Here is how to use this model to translate legal text from French to English in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
  
  pipeline = TranslationPipeline(
  model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_fr_en"),
  tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_fr_en", do_lower_case=False, 
                                              skip_special_tokens=True),
      device=0
  )
  
  fr_text = "Raül Romeva i Rueda (Verts/ALE)"
  
  pipeline([fr_text], max_length=512)
  ```
  
  ## Training data
  
  The legal_t5_small_multitask_fr_en model (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 9 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.
  
  ### 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
  
  
  ## Evaluation results
  
  When the model is used for translation test dataset, achieves the following results:
  
  Test results :
  
  | Model | BLEU score |
  |:-----:|:-----:|
  |   legal_t5_small_multitask_fr_en | 39.123|
  
  
  ### 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/)
  