Update README.md

README.md

@@ -7,4 +7,159 @@ task_categories:
 language:
 - en
 - si
----
+tags:
+- translation
+- transliteration
+- Sinhala
+- English
+- Singlish
+- NLP
+- dataset
+- low-resource
+pretty_name: Sinhala–English–Singlish Translation Dataset
+---
+
+# Sinhala–English–Singlish Translation Dataset
+
+> A parallel corpus of Sinhala sentences, their English translations, and romanized Sinhala (“Singlish”) transliterations.  
+
+---
+
+## 📋 Table of Contents
+
+1. [Dataset Overview](#dataset-overview)  
+2. [Installation](#installation)  
+3. [Quick Start](#quick-start)  
+4. [Dataset Structure](#dataset-structure)  
+5. [Usage Examples](#usage-examples)  
+6. [Citation](#citation)  
+7. [License](#license)  
+8. [Credits](#credits)  
+
+---
+
+## Dataset Overview
+
+- **Description**: 34,500 aligned triplets of  
+  - Sinhala (native script)  
+  - English (human translation)  
+  - Singlish (romanized Sinhala)  
+- **Source**:  
+  - 📊 Kaggle dataset: `programmerrdai/sinhala-english-singlish-translation-dataset`  
+  - 🛠️ Collection pipeline: GitHub [Sinenglish-LLM-Data-Collection](https://github.com/Programmer-RD-AI-Archive/Sinenglish-LLM-Data-Collection)  
+- **DOI**: 10.57967/hf/5605  
+- **Released**: 2025 (Revision `c6560ff`)  
+- **License**: MIT  
+
+---
+
+## Installation
+
+```bash
+pip install datasets
+````
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "Programmer-RD-AI/sinhala-english-singlish-translation", 
+    split="train"
+)
+print(ds[0])
+# {
+#   "sinhala": "මෙය මගේ ප්‍රධාන අයිතියයි",
+#   "english": "This is my headright.",
+#   "singlish": "meya mage pradhana ayithiyayi"
+# }
+```
+
+---
+
+## Dataset Structure
+
+| Column     | Type     | Description                            |
+| ---------- | -------- | -------------------------------------- |
+| `sinhala`  | `string` | Original sentence in Sinhala script    |
+| `english`  | `string` | Corresponding English translation      |
+| `singlish` | `string` | Romanized (“Singlish”) transliteration |
+
+* **Rows**: 34,500
+* **Format**: CSV (viewed as Parquet on HF)
+
+---
+
+## Usage Examples
+
+### Load into Pandas
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+df = load_dataset(
+    "Programmer-RD-AI/sinhala-english-singlish-translation", 
+    split="train"
+).to_pandas()
+
+print(df.head())
+```
+
+### Fine-tuning a Translation Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments
+
+# 1. Tokenizer & model
+tokenizer = AutoTokenizer.from_pretrained("t5-small")
+model     = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+# 2. Preprocess
+def preprocess(ex):
+    inputs  = "translate Sinhala to English: " + ex["sinhala"]
+    targets = ex["english"]
+    tokenized = tokenizer(inputs, text_target=targets, truncation=True)
+    return tokenized
+
+train_dataset = ds.map(preprocess, remove_columns=ds.column_names)
+  
+# 3. Training
+args = TrainingArguments(
+    output_dir="outputs",
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+)
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=train_dataset,
+    tokenizer=tokenizer
+)
+trainer.train()
+```
+
+---
+
+## Citation
+
+```bibtex
+@misc{programmer-rd-ai_2025,
+  author       = {Programmer-RD-AI},
+  title        = {sinhala-english-singlish-translation (Revision c6560ff)},
+  year         = {2025},
+  url          = {https://huggingface.co/datasets/Programmer-RD-AI/sinhala-english-singlish-translation},
+  doi          = {10.57967/hf/5605},
+  publisher    = {Hugging Face}
+}
+```
+
+---
+
+## License
+
+This dataset is released under the **CC License**. See the [LICENSE](LICENSE) file for details.
+