Legal T5 Small Trans De Es

🚀 legal_t5_small_trans_de_es model

A model for translating legal text from German to Spanish. It offers an efficient solution for legal document translation between these two languages.

🚀 Quick Start

The legal_t5_small_trans_de_es model is designed for translating legal text from German to Spanish. It was first released in this repository and trained on three parallel corpora from JRC-ACQUIS, Europarl, and DCEP.

✨ Features

• Based on t5-small: The model is built upon the t5-small architecture, which is scaled down with dmodel = 512, dff = 2,048, 8 - headed attention, and only 6 layers each in the encoder and decoder. It has about 60 million parameters.
• Trained on Large Parallel Corpus: It was trained on a large corpus of parallel text, enabling accurate legal text translation.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Here is how to use this model to translate legal text from German to Spanish in PyTorch:

from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_es"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_es", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

de_text = "7. betont, dass die Kommission und die Mitgliedstaaten die Rolle der Frauen in der Sozialwirtschaft aufgrund der hohen Frauenerwerbstätigkeit in dem Sektor und der Bedeutung der Dienstleistungen, die er für die Förderung der Vereinbarkeit von Beruf und Privatleben bietet, aufwerten, unterstützen und verstärken müssen;"

pipeline([de_text], max_length=512)

📚 Documentation

Model description

The legal_t5_small_trans_de_es model is based on the t5-small model and trained on a large parallel text corpus. It's a smaller model with specific architectural settings, resulting in about 60 million parameters.

Intended uses & limitations

The model is intended for the translation of legal texts from German to Spanish.

Training data

The model was trained on JRC - ACQUIS, EUROPARL, and DCEP datasets, which consist of 5 million parallel texts.

Training procedure

• Overall Training: The model was trained on a single TPU Pod V3 - 8 for 250K steps in total, using a sequence length of 512 (batch size 4096). It has approximately 220M parameters and was trained using the encoder - decoder architecture.
• Optimizer: The optimizer used is AdaFactor with an inverse square root learning rate schedule for pre - training.
• Preprocessing: An unigram model was 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.

Evaluation results

When the model is used for the translation test dataset, it achieves the following results:

BibTeX entry and citation info

Created by Ahmed Elnaggar/@Elnaggar_AI | LinkedIn