--- language: French Italian tags: - translation French Italian model datasets: - dcep europarl jrc-acquis --- # legal_t5_small_trans_fr_it model Model on translating legal text from French to Italian. 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_it 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 Italian. ### How to use Here is how to use this model to translate legal text from French to Italian in PyTorch: ```python from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline pipeline = TranslationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_it"), tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_fr_it", do_lower_case=False, skip_special_tokens=True), device=0 ) fr_text = "Occupazione e affari sociali " pipeline([fr_text], max_length=512) ``` ## Training data The legal_t5_small_trans_fr_it 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_it | 46.45| ### BibTeX entry and citation info