docs: rewrite README — focus on Myanmar Coder LLM purpose, document 4M bilingual data

README.md

@@ -1,83 +1,190 @@
 ---
 license: apache-2.0
 language:
+- my
 - en
-multilinguality: monolingual
-size_categories: n_1M_to_n_10M
-datasets:
-- amkyawdev/mm-llm-coder-dataset
+task_categories:
+- text-generation
+- question-answering
 tags:
-- code-generation
-- debugging
-- python
-- javascript
+- code
 - coding
+- myanmar
+- burmese
+- llm
+- instruction-tuning
+- conversational
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
-# Coder Dataset - code-skill.md
+# 🇲🇲 Myanmar LLM Coder Dataset (mm-llm-coder-dataset)
 
 ![License](https://img.shields.io/badge/License-Apache--2.0-yellow)
-![Rows](https://img.shields.io/badge/Rows-2M-blue)
-![Format](https://img.shields.io/badge/Format-JSON-blue)
+![Rows](https://img.shields.io/badge/Rows-4M-blue)
+![Languages](https://img.shields.io/badge/Languages-my%20%7C%20en-green)
+![Format](https://img.shields.io/badge/Format-Parquet-orange)
 
-Code generation and debugging dataset for training coding LLMs. Contains programming Q&A and error fixing examples.
+> **မြန်မာဘာသာ Coding LLM များ training အတွက် ရည်ရွယ်ထားသော dataset**
+>
+> A bilingual (Myanmar + English) coding instruction dataset designed primarily for training **Myanmar language Coder LLMs**.
 
-## Skill Type: Code/Skill
+---
 
-This dataset is part of the combined Myanmar LLM dataset collection:
-- **chat-skill.md** - [amkyawdev/myanmar-llm-data](https://huggingface.co/datasets/amkyawdev/myanmar-llm-data)
-- **agent-skill.md** - [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset)
-- **code-skill.md** - Myanmar conversational data, translations, Q&A
+## 🎯 ရည်ရွယ်ချက် / Purpose
 
-## Overview
+ဤ dataset သည် **မြန်မာဘာသာ programming/coding LLM** များ training လုပ်ရန်အတွက် အဓိက ရည်ရွယ်ထားပါသည်။ မြန်မာ developer များ၏ မိခင်ဘာသာစကားဖြင့် coding အကူအညီပေးနိုင်သော AI assistant များကို ဖန်တီးနိုင်စေရန် Myanmar (my) နှင့် English (en) ဘာသာစကား နှစ်မျိုးဖြင့် pair training data ထည့်သွင်းထားပါသည်။
 
-This dataset contains code generation and debugging tasks:
-- 💻 **Code Generation**: Python, JavaScript, TypeScript, React
-- 🐛 **Debugging**: Error fixing patterns
-- 📝 **Q&A Format**: Programming questions and answers
-- 🔍 **Code Review**: Best practices and optimization
+This dataset is primarily intended for training **Myanmar (Burmese) language Coder LLMs** — enabling AI coding assistants that natively understand and respond in မြန်မာဘာသာ. Both English and Myanmar examples share the same schema for parallel/cross-lingual training.
 
-## Dataset Statistics
+## 📊 Dataset Statistics
 
 | Metric | Value |
-|--------|-------|
-| Total Samples | ~2,000,000 |
+| --- | --- |
+| Total Samples | **4,000,000** |
+| Myanmar (my) | 2,000,000 |
+| English (en) | 2,000,000 |
+| Format | Parquet (Snappy compressed) |
+| Files | `data/train-00000-of-00004.parquet` … `data/train-00003-of-00004.parquet` |
+| Splits | `train` (single split) |
+
+## 🗂️ Schema
+
+```python
+{
+    "instruction": str,          # Topic/title of the Q&A
+    "messages": [                # Multi-turn conversation
+        {"role": "user", "content": str},
+        {"role": "assistant", "content": str},
+        ...                      # 2 / 4 / 6 messages per sample
+    ],
+    "category": str,             # See categories below
+    "language": str,             # "my" or "en"
+    "difficulty": str,           # "beginner" | "intermediate" | "advanced"
+    "response": str,             # JSON-serialized messages (string form)
+    "task_type": str,            # "qa_conversation"
+}
+```
+
+### Categories (၉ မျိုး)
+
+`code_debugging`, `mongodb_project`, `telegram_bot`, `ui_ux_design`,
+`cloudflare_worker`, `deployment_help`, `api_integration`,
+`firebase_project`, `website_builder`
+
+### Difficulty Levels
 
-## Data Format
+`beginner` · `intermediate` · `advanced`
 
+## 📝 Sample Examples
+
+### Myanmar (my)
+```json
+{
+  "instruction": "React useEffect ကို StrictMode မှာ နှစ်ကြိမ် run တာ ဘယ်လိုဖြေရှင်းမလဲ?",
+  "messages": [
+    {"role": "user", "content": "ကျွန်တော့် useEffect က development မှာ နှစ်ကြိမ် run နေပါတယ်။ ဘာကြောင့်လဲ?"},
+    {"role": "assistant", "content": "React 18 StrictMode က side effects တွေကို detect လုပ်ဖို့အတွက် effects တွေကို နှစ်ကြိမ်ခေါ်ပါတယ်။\n\n**ဖြေရှင်းနည်း**: cleanup function ထည့်ပါ..."}
+  ],
+  "category": "code_debugging",
+  "language": "my",
+  "difficulty": "beginner",
+  "task_type": "qa_conversation"
+}
+```
+
+### English (en)
 ```json
 {
+  "instruction": "React useEffect runs twice in StrictMode - how to fix?",
   "messages": [
-    {"role": "user", "content": "Debug CORS error in production"},
-    {"role": "assistant", "content": "Common causes: 1. Different domain..."}
+    {"role": "user", "content": "My useEffect is running twice in development. Why?"},
+    {"role": "assistant", "content": "React 18 StrictMode intentionally double-invokes effects to detect side effects..."}
   ],
   "category": "code_debugging",
   "language": "en",
-  "difficulty": "intermediate"
+  "difficulty": "beginner",
+  "task_type": "qa_conversation"
 }
 ```
 
-## Usage
+## 🚀 Usage
 
+### Load full dataset
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("amkyawdev/mm-llm-coder-dataset")
-print(dataset)
+ds = load_dataset("amkyawdev/mm-llm-coder-dataset")
+print(ds)
+# DatasetDict({ train: Dataset(num_rows=4000000, ...) })
+```
 
-# Access sample
-sample = dataset["train"][0]
-print("Messages:", sample["messages"])
-print("Category:", sample["category"])
+### Filter by language
+
+```python
+# Myanmar only — for Myanmar-focused fine-tuning
+my_data = ds["train"].filter(lambda x: x["language"] == "my")
+
+# English only — for cross-lingual / parallel training
+en_data = ds["train"].filter(lambda x: x["language"] == "en")
+```
+
+### Filter by category & difficulty
+```python
+debugging_advanced = ds["train"].filter(
+    lambda x: x["category"] == "code_debugging" and x["difficulty"] == "advanced"
+)
 ```
 
-## Use Cases
+### Streaming (recommended for large-scale training)
+```python
+ds = load_dataset("amkyawdev/mm-llm-coder-dataset", streaming=True)
+for sample in ds["train"]:
+    print(sample["language"], sample["instruction"])
+    break
+```
+
+## 🎓 Use Cases
+
+1. **🇲🇲 Myanmar Coder LLM training** — fine-tune base models (Llama, Qwen, Mistral, etc.) into Myanmar-language coding assistants
+2. **Cross-lingual code Q&A** — train models that handle both Myanmar and English coding queries
+3. **Instruction tuning** — multi-turn conversation format suitable for chat models
+4. **Code debugging assistants** — error fixing patterns across React, Node.js, MongoDB, WebSocket, etc.
+5. **Topic-specific fine-tuning** — filter by category (e.g., MongoDB-only, Firebase-only)
 
-1. **Code Generation**: Train models for programming tasks
-2. **Debugging**: Learn error fixing patterns
-3. **Code Review**: Best practices training
-4. **Q&A Systems**: Programming help chatbots
+## 🔗 Related Datasets
 
-## License
+This dataset is part of the combined Myanmar LLM dataset collection by [@amkyawdev](https://huggingface.co/amkyawdev):
 
-Apache 2.0 License
+- **chat-skill** → [amkyawdev/myanmar-llm-data](https://huggingface.co/datasets/amkyawdev/myanmar-llm-data) — conversational data, translations, general Q&A
+- **agent-skill** → [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset) — agentic coding tasks
+- **code-skill** → **this dataset** — code generation, debugging, and Q&A
+
+## ⚠️ Notes / Caveats
+
+- The dataset is **template-based**: the 4M samples are produced by combining a curated set of coding instructions with category × difficulty × conversation-length variations. This makes the dataset large and structurally consistent, but with limited semantic diversity per topic.
+- For higher-quality, more diverse Myanmar samples, you may consider augmenting with LLM-generated translations of curated English programming Q&A.
+- Both `messages` (list) and `response` (JSON string) fields contain the same conversation — use whichever your training pipeline prefers.
+
+## 📄 License
+
+Apache 2.0
+
+## 🙏 Citation
+
+If you use this dataset in your work, please cite:
+
+```bibtex
+@dataset{amkyawdev_mm_llm_coder_2025,
+  author    = {amkyawdev},
+  title     = {Myanmar LLM Coder Dataset (mm-llm-coder-dataset)},
+  year      = {2025},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/amkyawdev/mm-llm-coder-dataset}
+}
+```