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README.md CHANGED
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  ---
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- license: cc-by-nc-4.0
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: apache-2.0
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+ base_model: Helsinki-NLP/opus-mt-ar-en
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+ tags:
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+ - machine-translation
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+ - marian
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+ - mcwc
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+ - legal-nlp
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+ - constitutional-texts
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+ - generated_from_keras_callback
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+ model-index:
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+ - name: marian-finetuned-mcwc-ara-to-en
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+ results: []
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  ---
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+
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+ # Marian MT fine-tuned on the Multilingual Corpus of World’s Constitutions (MCWC)
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+ This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en), adapted using high-quality sentence-aligned constitutional text from the **Multilingual Corpus of World’s Constitutions (MCWC)**
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+ 📄 MCWC paper (OSACT 2024): https://aclanthology.org/2024.osact-1.7/
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+
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+ **This variant handles: _Arabic → English_ translation.**
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+
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+ ---
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+
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+ ## Overview
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+ 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:
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+
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+ - legal and constitutional NLP
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+ - comparative constitutional studies
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+ - multilingual machine translation
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+ - cross-lingual semantic analysis
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+
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+ 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.
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+
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+ ---
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+
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+ ## Intended use
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+ This model is suitable for tasks such as:
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+
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+ - translating constitutional or legal documents
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+ - cross-lingual legal text comparison
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+ - multilingual information extraction
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+ - downstream legal NLP tasks requiring domain-specific MT
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+
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+ It is **not** intended for casual or conversational translation, as it is optimised for formal and legal text.
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+
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+ ---
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+
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+ ## Training data
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+ The model was trained on the MCWC’s Arabic-English aligned sentence pairs.
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+ The MCWC dataset includes:
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+
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+ - cleaned constitutional text
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+ - high-quality sentence segmentation
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+ - pairwise alignments
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+ - country and regional metadata
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+
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+ More details may be found in the accompanying paper:
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+ > *El-Haj, M. & Ezzini, S. (2024). “The Multilingual Corpus of World’s Constitutions (MCWC).”*
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+ > OSACT @ LREC-COLING 2024.
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+ > https://aclanthology.org/2024.osact-1.7/
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+
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+ ---
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - 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}
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+ - training_precision: mixed_float16
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+
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+ ### Training results
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+
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+ | Train Loss | Validation Loss | Epoch |
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+ |:----------:|:---------------:|:-----:|
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+ | 1.3918 | 1.1473 | 0 |
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+ | 1.0745 | 1.1021 | 1 |
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+ | 0.9486 | 1.0908 | 2 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - TensorFlow 2.13.0
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use this model, please cite the MCWC paper:
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+
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+ **El-Haj, M. & Ezzini, S. (2024).**
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+ *The Multilingual Corpus of World’s Constitutions (MCWC).*
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+ Proceedings of OSACT @ LREC-COLING 2024.
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+ https://aclanthology.org/2024.osact-1.7/
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