readme: 
  ---
  language: Cszech Deustch  
  tags:
  - translation Cszech Deustch  model
  datasets:
  - dcep europarl jrc-acquis
  widget:
  - text: "Konečná zpráva bude Parlamentu předložena na konci nového funkčního období."
  
  ---
  
  # legal_t5_small_trans_cs_de model
  
  Model on translating legal text from Cszech 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_cs_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 Cszech to Deustch.
  
  ### How to use
  
  Here is how to use this model to translate legal text from Cszech to Deustch in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
  
  pipeline = TranslationPipeline(
  model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_de"),
  tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_de", do_lower_case=False, 
                                              skip_special_tokens=True),
      device=0
  )
  
  cs_text = "Konečná zpráva bude Parlamentu předložena na konci nového funkčního období."
  
  pipeline([cs_text], max_length=512)
  ```
  
  ## Training data
  
  The legal_t5_small_trans_cs_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
  
  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
  
  
  
  ## Evaluation results
  
  When the model is used for translation test dataset, achieves the following results:
  
  Test results :
  
  | Model | BLEU score |
  |:-----:|:-----:|
  |   legal_t5_small_trans_cs_de | 44.69|
  
  
  ### 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/)
  