Update README.md

README.md

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----
-license: cc-by-nc-4.0
-tags:
-- legal-nlp
-- multilingual
-- constitutions
-- machine-translation
-- comparative-law
-- mcwc
-dataset-name: MCWC
----
-
-# Multilingual Corpus of World’s Constitutions (MCWC)
-
-The **MCWC** is a curated multilingual corpus of constitutional texts from **191 countries**, including both current and historical versions. The dataset provides aligned constitutional content in **English, Arabic, and Spanish**, enabling comparative legal analysis and multilingual NLP research.  
-
-This CSV version is a cleaned, structured, and sentence-aligned representation of the corpus, suitable for machine translation, information retrieval, and a wide range of legal NLP tasks.
-
-📄 **Paper (OSACT @ LREC-COLING 2024):**  
-https://aclanthology.org/2024.osact-1.7/
-
----
-
-## Contents of the dataset
-
-`MCWC.csv` contains:
-
-- **Constitutional text in three languages:** English, Arabic, Spanish  
-- **Pairwise sentence alignments** using a stable `AlignID`  
-- **Country and continent metadata**  
-- **Cleaned and normalised text** for direct use in NLP pipelines  
-- **Translations** for constitutions originally missing in Arabic or Spanish, generated using a fine-tuned NMT model  
-- **One row per sentence**, enabling easy filtering and cross-lingual matching
-
-Typical columns include:
-
-- `country`  
-- `continent`  
-- `align_id`  
-- `text_en`  
-- `text_ar`  
-- `text_es`  
-- `constitution_year`  
-- `language_source` (original vs. machine-translated)
-
----
-
-## Motivation
-
-Constitutions form the legal and philosophical foundation of nation-states. However, constitutional research is often limited by:
-
-- fragmented access to multilingual versions  
-- inconsistent formatting and metadata  
-- lack of high-quality sentence alignment  
-- scarcity of constitutional text in Arabic and Spanish  
-
-The MCWC addresses these gaps by offering a unified and multilingual corpus built for computational analysis, comparative constitutional research, and machine translation development.  
-
-The acronym **MCWC** is pronounced *“Makkuk”*, an Arabic word for *space shuttle*, chosen to symbolise the corpus’s role in enabling cross-lingual travel between legal systems.
-
----
-
-## Data sources and preparation
-
-The corpus integrates material from:
-
-- the Comparative Constitutions Project  
-- the Constitute Project  
-- Wikipedia and various governmental archives  
-
-Raw texts—primarily in XML—were normalised, cleaned, and sentence-segmented. For constitutions lacking Arabic or Spanish versions, we generated translations using a fine-tuned state-of-the-art MT model.
-
-A custom parser aligned sentences across languages using structural cues (article numbers, section labels, etc.), with each sentence assigned a persistent `AlignID` for alignment across all three languages.
-
-To support comparative analysis, we applied:
-
-- continent classification via gazetteer matching  
-- tokenisation and normalisation  
-- vocabulary overlap statistics  
-- TF-IDF cosine-similarity analysis (English)  
-
-The paper provides further detail, including heatmaps of by-continent overlap and cross-lingual similarity.
-
----
-
-## Dataset statistics
-
-- **223 constitutions**  
-- **191 countries**  
-- **95 constitutions available in all three languages**  
-- **52,177 aligned English–Arabic sentences**  
-- **48,892 aligned English–Spanish sentences**  
-- **27,352 aligned Arabic–Spanish sentences**  
-- **236,156 parallel machine-generated sentences** (SeamlessM4T-v2)
-
-Across continents:
-
-- Africa: 65 constitutions  
-- Asia: 54  
-- Europe: 49  
-- North America: 26  
-- South America: 15  
-- Oceania: 14  
-
-Average lengths and TTR values are reported in the paper.
-
----
-
-## Intended uses
-
-The MCWC is designed to support:
-
-- multilingual machine translation  
-- legal text analysis and retrieval  
-- constitutional comparison and modelling  
-- multilingual topic modelling  
-- cross-lingual semantic similarity  
-- legal corpus linguistics  
-- training/testing MT systems for legal text  
-
-Users may also employ the dataset to study variation in legal language across jurisdictions and historical periods.
-
----
-
-## Translation and evaluation
-
-For missing Arabic/Spanish texts, English constitutions were translated using **Facebook’s Seamless-m4t-v2-large**.  
-
-Evaluation included:
-
-- **BLEU = 0.68** on 500 manually inspected En–Ar and En–Es pairs  
-- **Human annotation** by two Arabic NLP experts  
-  - Cohen’s κ = 0.30 (due to label imbalance)  
-  - Krippendorff’s α ≈ 0.90 (robust reliability)  
-
-Fine-tuned Marian NMT models trained on the MCWC consistently improved translation quality across all six language pairs. These models are available on HuggingFace.
-
----
-### Train / Validation / Test Splits
-
-The original MCWC paper does not define an official train–validation–test split.  
-To support reproducibility and facilitate machine-learning experiments, we provide a recommended **80/10/10** split using a fixed random seed. This ensures consistent partitioning whilst keeping the dataset fully shuffled.
-
-The split is applied over the complete CSV file (`MCWC.csv`) and does not use stratification, as constitutional texts vary considerably across countries and languages.
-
-#### Splitting script
-
-```python
-import pandas as pd
-from sklearn.model_selection import train_test_split
-
-# Load the full corpus
-df = pd.read_csv("MCWC.csv")
-
-# First split: train vs temporary (validation + test)
-train_df, temp_df = train_test_split(
-    df,
-    test_size=0.2,     # 20% goes to val+test
-    random_state=42,
-    shuffle=True
-)
-
-# Second split: create separate validation and test sets
-val_df, test_df = train_test_split(
-    temp_df,
-    test_size=0.5,     # half of 20% → 10% each
-    random_state=42,
-    shuffle=True
-)
-
-# Save the splits
-train_df.to_csv("MCWC_train.csv", index=False)
-val_df.to_csv("MCWC_val.csv", index=False)
-test_df.to_csv("MCWC_test.csv", index=False)
-
-print("Train:", len(train_df))
-print("Validation:", len(val_df))
-print("Test:", len(test_df))
-```
-
-This produces three files:
-
-- `MCWC_train.csv`  
-- `MCWC_val.csv`  
-- `MCWC_test.csv`  
-
-These splits can be uploaded to HuggingFace or used directly for machine translation, classification, or any downstream constitutional NLP research.
-
----
-
-### Train–Validation–Test Split
-
-```python
-import pandas as pd
-from sklearn.model_selection import train_test_split
-
-# Load the full corpus
-df = pd.read_csv("MCWC.csv")
-
-# First split: train vs temporary (validation + test)
-train_df, temp_df = train_test_split(
-    df,
-    test_size=0.2,     # 20% goes to val+test
-    random_state=42,
-    shuffle=True
-)
-
-# Second split: create separate validation and test sets
-val_df, test_df = train_test_split(
-    temp_df,
-    test_size=0.5,     # half of 20% → 10% each
-    random_state=42,
-    shuffle=True
-)
-
-# Save the splits
-train_df.to_csv("MCWC_train.csv", index=False)
-val_df.to_csv("MCWC_val.csv", index=False)
-test_df.to_csv("MCWC_test.csv", index=False)
-
-print("Train:", len(train_df))
-print("Validation:", len(val_df))
-print("Test:", len(test_df))
-```
-
----
- 
-## Ethical considerations and limitations
-
-- Source texts originate from open datasets (CCP, Constitute Project) and governmental archives.  
-- This CSV is a *processed* and *aligned* derivative, not a redistribution of the original XML files.  
-- Translation quality varies depending on source material and MT system limitations.  
-- Some constitutions contain archaic, historical, or culturally-specific legal phrasing that may not translate literally.  
-- Differences in translation providers (HeinOnline, IDEA, OUP, etc.) may introduce stylistic or interpretative variance.
-
-Users requiring original XML files should retrieve them directly from the Constitute Project.
-
----
-
-## Citation
-
-If you use this dataset, please cite:
-
-**El-Haj, M. & Ezzini, S. (2024).**  
-*The Multilingual Corpus of World’s Constitutions (MCWC).*  
-OSACT Workshop @ LREC-COLING 2024.  
-https://aclanthology.org/2024.osact-1.7/
-
----
-
-## Contact
-
-For questions or collaboration:
-
-**Mo El-Haj**  
-UCREL NLP Group  
-Lancaster University & VinUniversity  
-Email: m.el-haj@lancaster.ac.uk / elhaj.m@vinuni.edu.vn
+---
+license: cc-by-nc-4.0
+tags:
+- legal-nlp
+- multilingual
+- constitutions
+- machine-translation
+- comparative-law
+- mcwc
+dataset-name: MCWC
+---
+
+# Multilingual Corpus of World’s Constitutions (MCWC)
+
+The **MCWC** is a curated multilingual corpus of constitutional texts from **191 countries**, including both current and historical versions. The dataset provides aligned constitutional content in **English, Arabic, and Spanish**, enabling comparative legal analysis and multilingual NLP research.  
+
+This CSV version is a cleaned, structured, and sentence-aligned representation of the corpus, suitable for machine translation, information retrieval, and a wide range of legal NLP tasks.
+
+📄 **Paper (OSACT @ LREC-COLING 2024):**  
+https://aclanthology.org/2024.osact-1.7/
+
+---
+
+## Contents of the dataset
+
+`MCWC.csv` contains:
+
+- **Constitutional text in three languages:** English, Arabic, Spanish  
+- **Pairwise sentence alignments** using a stable `AlignID`  
+- **Country and continent metadata**  
+- **Cleaned and normalised text** for direct use in NLP pipelines  
+- **Translations** for constitutions originally missing in Arabic or Spanish, generated using a fine-tuned NMT model  
+- **One row per sentence**, enabling easy filtering and cross-lingual matching
+
+Typical columns include:
+
+- `country`  
+- `continent`  
+- `align_id`  
+- `text_en`  
+- `text_ar`  
+- `text_es`  
+- `constitution_year`  
+- `language_source` (original vs. machine-translated)
+
+---
+
+## Motivation
+
+Constitutions form the legal and philosophical foundation of nation-states. However, constitutional research is often limited by:
+
+- fragmented access to multilingual versions  
+- inconsistent formatting and metadata  
+- lack of high-quality sentence alignment  
+- scarcity of constitutional text in Arabic and Spanish  
+
+The MCWC addresses these gaps by offering a unified and multilingual corpus built for computational analysis, comparative constitutional research, and machine translation development.  
+
+The acronym **MCWC** is pronounced *“Makkuk”*, an Arabic word for *space shuttle*, chosen to symbolise the corpus’s role in enabling cross-lingual travel between legal systems.
+
+---
+
+## Data sources and preparation
+
+The corpus integrates material from:
+
+- the Comparative Constitutions Project  
+- the Constitute Project  
+- Wikipedia and various governmental archives  
+
+Raw texts—primarily in XML—were normalised, cleaned, and sentence-segmented. For constitutions lacking Arabic or Spanish versions, we generated translations using a fine-tuned state-of-the-art MT model.
+
+A custom parser aligned sentences across languages using structural cues (article numbers, section labels, etc.), with each sentence assigned a persistent `AlignID` for alignment across all three languages.
+
+To support comparative analysis, we applied:
+
+- continent classification via gazetteer matching  
+- tokenisation and normalisation  
+- vocabulary overlap statistics  
+- TF-IDF cosine-similarity analysis (English)  
+
+The paper provides further detail, including heatmaps of by-continent overlap and cross-lingual similarity.
+
+---
+
+## Dataset statistics
+
+- **223 constitutions**  
+- **191 countries**  
+- **95 constitutions available in all three languages**  
+- **52,177 aligned English–Arabic sentences**  
+- **48,892 aligned English–Spanish sentences**  
+- **27,352 aligned Arabic–Spanish sentences**  
+- **236,156 parallel machine-generated sentences** (SeamlessM4T-v2)
+
+Across continents:
+
+- Africa: 65 constitutions  
+- Asia: 54  
+- Europe: 49  
+- North America: 26  
+- South America: 15  
+- Oceania: 14  
+
+Average lengths and TTR values are reported in the paper.
+
+---
+
+## Intended uses
+
+The MCWC is designed to support:
+
+- multilingual machine translation  
+- legal text analysis and retrieval  
+- constitutional comparison and modelling  
+- multilingual topic modelling  
+- cross-lingual semantic similarity  
+- legal corpus linguistics  
+- training/testing MT systems for legal text  
+
+Users may also employ the dataset to study variation in legal language across jurisdictions and historical periods.
+
+---
+
+## Translation and evaluation
+
+For missing Arabic/Spanish texts, English constitutions were translated using **Facebook’s Seamless-m4t-v2-large**.  
+
+Evaluation included:
+
+- **BLEU = 0.68** on 500 manually inspected En–Ar and En–Es pairs  
+- **Human annotation** by two Arabic NLP experts  
+  - Cohen’s κ = 0.30 (due to label imbalance)  
+  - Krippendorff’s α ≈ 0.90 (robust reliability)  
+
+Fine-tuned Marian NMT models trained on the MCWC consistently improved translation quality across all six language pairs. These models are available on HuggingFace.
+
+---
+### Train / Validation / Test Splits
+
+The original MCWC paper does not define an official train–validation–test split.  
+To support reproducibility and facilitate machine-learning experiments, we provide a recommended **80/10/10** split using a fixed random seed. This ensures consistent partitioning whilst keeping the dataset fully shuffled.
+
+The split is applied over the complete CSV file (`MCWC.csv`) and does not use stratification, as constitutional texts vary considerably across countries and languages.
+
+```python
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# Load the full corpus
+df = pd.read_csv("MCWC.csv")
+
+# First split: train vs temporary (validation + test)
+train_df, temp_df = train_test_split(
+    df,
+    test_size=0.2,     # 20% goes to val+test
+    random_state=42,
+    shuffle=True
+)
+
+# Second split: create separate validation and test sets
+val_df, test_df = train_test_split(
+    temp_df,
+    test_size=0.5,     # half of 20% → 10% each
+    random_state=42,
+    shuffle=True
+)
+
+# Save the splits
+train_df.to_csv("MCWC_train.csv", index=False)
+val_df.to_csv("MCWC_val.csv", index=False)
+test_df.to_csv("MCWC_test.csv", index=False)
+
+print("Train:", len(train_df))
+print("Validation:", len(val_df))
+print("Test:", len(test_df))
+```
+
+---
+ 
+## Ethical considerations and limitations
+
+- Source texts originate from open datasets (CCP, Constitute Project) and governmental archives.  
+- This CSV is a *processed* and *aligned* derivative, not a redistribution of the original XML files.  
+- Translation quality varies depending on source material and MT system limitations.  
+- Some constitutions contain archaic, historical, or culturally-specific legal phrasing that may not translate literally.  
+- Differences in translation providers (HeinOnline, IDEA, OUP, etc.) may introduce stylistic or interpretative variance.
+
+Users requiring original XML files should retrieve them directly from the Constitute Project.
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+**El-Haj, M. & Ezzini, S. (2024).**  
+*The Multilingual Corpus of World’s Constitutions (MCWC).*  
+OSACT Workshop @ LREC-COLING 2024.  
+https://aclanthology.org/2024.osact-1.7/
+
+---
+
+## Contact
+
+For questions or collaboration:
+
+**Mo El-Haj**  
+UCREL NLP Group  
+Lancaster University & VinUniversity  
+Email: m.el-haj@lancaster.ac.uk / elhaj.m@vinuni.edu.vn