language: French Italian
tags:
- translation French Italian model
datasets:
- dcep europarl jrc-acquis
widget:
- text: >-
invite les États membres, lorsqu'ils mettent en œuvre des politiques
d'émancipation, à suivre et/ou à maintenir une double approche, comportant
des politiques qui intègrent une perspective de genre dans tous les
domaines des politiques habituelles ainsi que des politiques et des
actions spécifiques visant à assurer l'autonomisation des femmes et à
assurer l'égalité entre hommes et femmes;
legal_t5_small_trans_fr_it model
Model on translating legal text from French to Italian. It was first released in this repository. 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:
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 = "invite les États membres, lorsqu'ils mettent en œuvre des politiques d'émancipation, à suivre et/ou à maintenir une double approche, comportant des politiques qui intègrent une perspective de genre dans tous les domaines des politiques habituelles ainsi que des politiques et des actions spécifiques visant à assurer l'autonomisation des femmes et à assurer l'égalité entre hommes et femmes;"
pipeline([fr_text], max_length=512)
Training data
The legal_t5_small_trans_fr_it model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.
Training procedure
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.
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
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_fr_it | 46.45 |
BibTeX entry and citation info
Created by Ahmed Elnaggar/@Elnaggar_AI | LinkedIn