Update README.md

README.md

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----
-license: "cc-by-4.0"
-language:
-  - ar
-task_categories:
-  - text-classification
-tags:
-  - arabic
-  - dialect-identification
-  - multilingual
-  - sociolinguistics
-  - code-switching
-  - bivalency
-configs:
-  - config_name: full_text
-    description: "Raw dialectal text files for EGY, GLF, LAV, NOR and MSA."
-  - config_name: freq_lists
-    description: "Dialect-specific vocabulary lists, bivalency-removed lists and MSA shared lists."
----
-
-# Arabic Dialects Dataset (Bivalency & Code-Switching)
-
-The **Arabic Dialects Dataset** is a specialised corpus designed for automatic dialect identification, with a focus on the linguistic phenomena of **bivalency** and **written code-switching** between major Arabic dialects and Modern Standard Arabic (MSA).  
-It covers five varieties:
-
-- **EGY** – Egyptian Arabic  
-- **GLF** – Gulf Arabic  
-- **LAV** – Levantine Arabic  
-- **NOR** – North African / Tunisian Arabic  
-- **MSA** – Modern Standard Arabic  
-
-The dataset was created for research on fine-grained linguistic variation and has been used to evaluate new methods such as **Subtractive Bivalency Profiling (SBP)**, achieving over **76% accuracy** in supervised dialect identification.
-
-This HuggingFace release makes the dataset machine-readable and ready for text classification, feature engineering, or corpus linguistic exploration.
-
----
-
-## 📘 Citation
-
-If you use this dataset, please cite:
-
-**El-Haj M., Rayson P., Aboelezz M. (2018)**  
-*Arabic Dialect Identification in the Context of Bivalency and Code-Switching.*  
-In **Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)**, Miyazaki, Japan, pp. 3622–3627.  
-European Language Resources Association (ELRA).  
-PDF: https://elhaj.uk/docs/237_Paper.pdf
-
----
-
-## 📂 Dataset Structure
-
-The dataset is distributed across two main sections:
-
-### **1. Dialects Full Text**
-Five files, each containing all instances belonging to one dialect:
-
-```
-Dialects Full Text/
-│── allEGY.txt
-│── allGLF.txt
-│── allLAV.txt
-│── allMSA.txt
-└── allNOR.txt
-```
-
----
-
-
-Each file contains raw text samples, one per line, suitable for direct use in dialect classification experiments.
-
----
-
-### **2. Dialectal Frequency Lists**
-
-These resources support linguistic analysis and the SBP approach introduced in the paper.
-
-```
-Dialects Frequency Lists/
-│
-├── Bivalency Removed (dialect - MSA)/
-│ allEGY_minusMSA.txt
-│ allGLF_minusMSA.txt
-│ allLAV_minusMSA.txt
-│ allNOR_minusMSA.txt
-│
-├── Dialects’ MSA/
-│ allEGY_dialectal_MSA.txt
-│ allGLF_dialectal_MSA.txt
-│ allLAV_dialectal_MSA.txt
-│ allNOR_dialectal_MSA.txt
-│
-├── Dialects Tokens WITH Frequency Count/
-│ all-EGY_FreqList.txt
-│ all-GLF_FreqList.txt
-│ all-LAV_FreqList.txt
-│ all-MSA_FreqList.txt
-│ all-NOR_FreqList.txt
-│
-└── Dialects Tokens NO Frequency Count/
-all-EGY_FreqList2.txt
-all-GLF_FreqList2.txt
-all-LAV_FreqList2.txt
-all-MSA_FreqList2.txt
-all-NOR_FreqList2.txt
-```
-
-**These include:**
-
-- **Bivalency-removed lists:** dialect-specific vocabularies after removing shared (bivalent) words  
-- **Dialectal-MSA lists:** vocabulary shared with MSA via written code-switching  
-- **Frequency lists:** token frequency counts  
-- **Non-frequency lists:** raw token lists without counts  
-
----
-
-## 🧪 Intended Use
-
-This dataset supports several research tasks:
-
-### **Dialect Identification**
-- Train machine-learning models to classify EGY/GLF/LAV/NOR/MSA  
-- Evaluate performance on highly bivalent and lexically overlapping dialects  
-- Benchmark features beyond n-grams  
-
-### **Bivalency & Code-Switching Analysis**
-- Study how words shift between dialects and MSA  
-- Explore written code-switching in online text and commentary discourse  
-
-### **Linguistic Feature Engineering**
-- Use SBP lists as interpretable features  
-- Combine stylistic, grammatical, and frequency-based signals  
-
----
-
-## 📊 Data Statistics
-
-(From the original publication)
-
-| Dialect | Sentences | Words |
-|--------|-----------|-------|
-| EGY | 4,061 | 118,152 |
-| GLF | 2,546 | 65,752 |
-| LAV | 2,463 | 67,976 |
-| MSA | 3,731 | 49,985 |
-| NOR | 3,693 | 53,204 |
-| **Total** | **16,494** | **355,069** |
-
----
-
-## 🔍 Example Usage
-
-### Load the full text for dialect classification
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("YOUR_REPO_NAME", "full_text")
-print(ds["train"][0])
-ds = load_dataset("YOUR_REPO_NAME", "freq_lists")
-freq_list = ds["EGY_freq"]
-```
-
----
-## ⚠ Licence
-
-This dataset is released for research purposes only.
-Texts originate from publicly available online sources or earlier datasets where redistribution for research is permitted.
-
----
-
-## 🙏 Acknowledgements
-
+---
+license: "cc-by-4.0"
+language:
+  - ar
+task_categories:
+  - text-classification
+tags:
+  - arabic
+  - dialect-identification
+  - multilingual
+  - sociolinguistics
+  - code-switching
+  - bivalency
+configs:
+  - config_name: full_text
+    description: "Raw dialectal text files for EGY, GLF, LAV, NOR and MSA."
+  - config_name: freq_lists
+    description: "Dialect-specific vocabulary lists, bivalency-removed lists and MSA shared lists."
+---
+
+# Arabic Dialects Dataset (Bivalency & Code-Switching)
+
+The **Arabic Dialects Dataset** is a specialised corpus designed for automatic dialect identification, with a focus on the linguistic phenomena of **bivalency** and **written code-switching** between major Arabic dialects and Modern Standard Arabic (MSA).  
+It covers five varieties:
+
+- **EGY** – Egyptian Arabic  
+- **GLF** – Gulf Arabic  
+- **LAV** – Levantine Arabic  
+- **NOR** – North African / Tunisian Arabic  
+- **MSA** – Modern Standard Arabic  
+
+The dataset was created for research on fine-grained linguistic variation and has been used to evaluate new methods such as **Subtractive Bivalency Profiling (SBP)**, achieving over **76% accuracy** in supervised dialect identification.
+
+This HuggingFace release makes the dataset machine-readable and ready for text classification, feature engineering, or corpus linguistic exploration.
+
+---
+
+## 📘 Citation
+
+If you use this dataset, please cite:
+
+**El-Haj M., Rayson P., Aboelezz M. (2018)**  
+*Arabic Dialect Identification in the Context of Bivalency and Code-Switching.*  
+In **Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)**, Miyazaki, Japan, pp. 3622–3627.  
+European Language Resources Association (ELRA).  
+PDF: https://elhaj.uk/docs/237_Paper.pdf
+
+---
+
+## 📂 Dataset Structure
+
+The dataset is distributed across two main sections:
+
+### **1. Dialects Full Text**
+Five files, each containing all instances belonging to one dialect:
+
+```
+Dialects Full Text/
+│── allEGY.txt
+│── allGLF.txt
+│── allLAV.txt
+│── allMSA.txt
+└── allNOR.txt
+```
+
+---
+
+
+Each file contains raw text samples, one per line, suitable for direct use in dialect classification experiments.
+
+---
+
+### **2. Dialectal Frequency Lists**
+
+These resources support linguistic analysis and the SBP approach introduced in the paper.
+
+```
+Dialects Frequency Lists/
+│
+├── Bivalency Removed (dialect - MSA)/
+│ allEGY_minusMSA.txt
+│ allGLF_minusMSA.txt
+│ allLAV_minusMSA.txt
+│ allNOR_minusMSA.txt
+│
+├── Dialects’ MSA/
+│ allEGY_dialectal_MSA.txt
+│ allGLF_dialectal_MSA.txt
+│ allLAV_dialectal_MSA.txt
+│ allNOR_dialectal_MSA.txt
+│
+├── Dialects Tokens WITH Frequency Count/
+│ all-EGY_FreqList.txt
+│ all-GLF_FreqList.txt
+│ all-LAV_FreqList.txt
+│ all-MSA_FreqList.txt
+│ all-NOR_FreqList.txt
+│
+└── Dialects Tokens NO Frequency Count/
+all-EGY_FreqList2.txt
+all-GLF_FreqList2.txt
+all-LAV_FreqList2.txt
+all-MSA_FreqList2.txt
+all-NOR_FreqList2.txt
+```
+
+**These include:**
+
+- **Bivalency-removed lists:** dialect-specific vocabularies after removing shared (bivalent) words  
+- **Dialectal-MSA lists:** vocabulary shared with MSA via written code-switching  
+- **Frequency lists:** token frequency counts  
+- **Non-frequency lists:** raw token lists without counts  
+
+---
+
+## 📊 **CSV Conversion + Train/Dev/Test Splits**
+The original text files were converted into a unified sentence-level CSV: arabic_dialects_full.csv
+
+
+with the schema:
+
+| sentence | dialect |
+|----------|---------|
+| … | EGY / GLF / LAV / MSA / NOR |
+
+Each sentence corresponds to one line from the original files.
+
+This CSV was then **stratified and split** into:
+arabic_dialects_train.csv
+arabic_dialects_dev.csv
+arabic_dialects_test.csv
+
+
+
+Splits preserve dialect balance and follow an 80/10/10 ratio.
+
+These files power the `csv_splits` configuration for immediate model training.
+
+---
+
+## 🧪 Intended Use
+
+This dataset supports several research tasks:
+
+### **Dialect Identification**
+- Train machine-learning models to classify EGY/GLF/LAV/NOR/MSA  
+- Evaluate performance on highly bivalent and lexically overlapping dialects  
+- Benchmark features beyond n-grams  
+
+### **Bivalency & Code-Switching Analysis**
+- Study how words shift between dialects and MSA  
+- Explore written code-switching in online text and commentary discourse  
+
+### **Linguistic Feature Engineering**
+- Use SBP lists as interpretable features  
+- Combine stylistic, grammatical, and frequency-based signals  
+
+---
+
+## 📊 Data Statistics
+
+(From the original publication)
+
+| Dialect | Sentences | Words |
+|--------|-----------|-------|
+| EGY | 4,061 | 118,152 |
+| GLF | 2,546 | 65,752 |
+| LAV | 2,463 | 67,976 |
+| MSA | 3,731 | 49,985 |
+| NOR | 3,693 | 53,204 |
+| **Total** | **16,494** | **355,069** |
+
+---
+
+## 🔍 Example Usage
+
+### Load the full text for dialect classification
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("YOUR_REPO_NAME", "full_text")
+print(ds["train"][0])
+ds = load_dataset("YOUR_REPO_NAME", "freq_lists")
+freq_list = ds["EGY_freq"]
+```
+
+---
+## ⚠ Licence
+
+This dataset is released for research purposes only.
+Texts originate from publicly available online sources or earlier datasets where redistribution for research is permitted.
+
+---
+
+## 🙏 Acknowledgements
+
 This dataset was developed at UCREL, Lancaster University, as part of research into Arabic dialect variation, bivalency, and automatic dialect identification.