---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ar-en
tags:
- machine-translation
- marian
- mcwc
- legal-nlp
- constitutional-texts
- generated_from_keras_callback
model-index:
- name: marian-finetuned-mcwc-ara-to-en
  results: []
---

# Marian MT fine-tuned on the Multilingual Corpus of World’s Constitutions (MCWC)
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en), adapted using high-quality sentence-aligned constitutional text from the **Multilingual Corpus of World’s Constitutions (MCWC)**  
📄 MCWC paper (OSACT 2024): https://aclanthology.org/2024.osact-1.7/

**This variant handles: _Arabic → English_ translation.**

---

## Overview
The MCWC provides a curated multilingual collection of constitutional texts from countries across the world. The corpus emphasises data cleanliness, high-quality sentence alignment, and detailed metadata (including country and continent mappings). It supports research in:

- legal and constitutional NLP  
- comparative constitutional studies  
- multilingual machine translation  
- cross-lingual semantic analysis  

This model was fine-tuned on the Arabic-English segment of the MCWC, enabling translation that is more attuned to legal and constitutional language than general-purpose MT systems.

---

## Intended use
This model is suitable for tasks such as:

- translating constitutional or legal documents  
- cross-lingual legal text comparison  
- multilingual information extraction  
- downstream legal NLP tasks requiring domain-specific MT  

It is **not** intended for casual or conversational translation, as it is optimised for formal and legal text.

---

## Training data
The model was trained on the MCWC’s Arabic-English aligned sentence pairs.  
The MCWC dataset includes:

- cleaned constitutional text  
- high-quality sentence segmentation  
- pairwise alignments  
- country and regional metadata  

More details may be found in the accompanying paper:
> *El-Haj, M. & Ezzini, S. (2024). “The Multilingual Corpus of World’s Constitutions (MCWC).”*  
> OSACT @ LREC-COLING 2024.  
> https://aclanthology.org/2024.osact-1.7/

---

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.3918     | 1.1473          | 0     |
| 1.0745     | 1.1021          | 1     |
| 0.9486     | 1.0908          | 2     |


### Framework versions

- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3

---

## Citation

If you use this model, please cite the MCWC paper:

**El-Haj, M. & Ezzini, S. (2024).**  
*The Multilingual Corpus of World’s Constitutions (MCWC).*  
Proceedings of OSACT @ LREC-COLING 2024.  
https://aclanthology.org/2024.osact-1.7/