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---
license: mit
task_categories:
- text-generation
- text-classification
language:
- my
tags:
- MyanmarSentences
- Burmese
- BurmeseSentences
---
# 🧠 1_pattern_10Kplus_myanmar_sentences
A structured dataset of **11,452 Myanmar sentences** generated from a single, powerful grammar pattern:
### 📌 Pattern:
**`Verb လည်း Verb တယ်။`**
> A natural way to express repetition, emphasis, or causal connection in Myanmar.
---
## 💡 About the Dataset
This dataset demonstrates how applying just **one syntactic pattern** to a curated verb list — combined with syllable-aware rules — can produce a high-quality corpus of over **10,000 valid Myanmar sentences**.
Each sentence is:
- Grammatically valid
- Syllable-tokenized
- Pattern-consistent
- Cleaned and filtered
---
## 🔁 Pattern in Use
Examples:
- ချစ်လည်း ချစ်တယ်။
- ကစားလည်း ကစားတယ်။
- ကံကြီးလည်း ထိုက်တယ်။
- ခေါင်းချင်းဆိုင်လည်း တိုက်တယ်။
---
## 📏 Rules in Use
| Syllable Count | Rule Name | Sentence Format | # of Sentences Generated |
|----------------|-------------------|---------------------------------------------|---------------------------|
| 1 | Rule_1_Syllable | `Aလည်း Aတယ်။` | 1 |
| 2 | Rule_2_Syllable | `Aလည်း Bတယ်။` and `Aလည်း ABတယ်။` | 2 (dual sentences) |
| 3 | Rule_3_Syllable | `ABလည်း Cတယ်။` | 1 |
| 4 | Rule_4_Syllable | `ABCလည်း Dတယ်။` | 1 |
| 5+ | Rule_{N}_Syllable | `ABCD...လည်း Zတယ်။` | 1 per item |
---
## 📁 Dataset Format
Each row in the CSV contains:
| Column | Description |
|------------------|--------------------------------------------------|
| `my_sentence` | The full generated Myanmar sentence |
| `my_word` | The original verb the sentence is based on |
| `my_subword` | List of syllable-level tokens (as a string list) |
| `subword_number` | Number of syllables in `my_word` |
**Example:**
```text
my_sentence: ကလည်း ကတယ်။
my_word: က
my_subword: ["က"]
subword_number: 1
```
## 🤯 Why It’s Special
```
• ✅ Only one pattern → yet over 11,000 real Myanmar sentences
• ✅ Rule logic scales across syllable complexity
• ✅ Cleaned, structured, and easy to extend
• ✅ Represents real grammar, not artificial templates
```
---
## 😅 Problems We Faced
We started with a large list of Myanmar verbs and applied a syllable-level tokenizer to break each verb into structured chunks. Based on syllable count, we applied one of six rules to generate sentences.
Challenges included:
```
• Unicode inconsistencies (e.g., ဥ် vs ဉ်, န့် vs န့်)
• Visually similar characters causing mis-splitting
• Manual review needed for edge cases
• Some grammatically valid outputs lacked semantic sense
```
## 🔮 Future Plans
This is just Pattern 1.
Coming soon:
• မ V နဲ့။
We aim to build a full-scale, pattern-rich Myanmar corpus — one rule at a time.
⸻
## 🎯 Use Cases
• Fine-tune sentence generation models
• Train grammar correction systems
• Build linguistic datasets for Myanmar NLP
• Teach Myanmar grammar through concrete patterns
• Benchmark syllable tokenizers
⸻
## 🧪 Quality & Manual Review
Even though all sentences were generated using grammatical rules, not all combinations may sound natural or meaningful in everyday Myanmar.
📝 This dataset should be manually reviewed by native speakers to ensure each sentence:
```
• Sounds natural
• Makes semantic sense
• Feels appropriate for real-world use
```
That said — even if you:
```
• Remove awkward or illogical samples
• Filter or adjust by context
• Expand with more rules and patterns
```
➡️ You’ll still retain 10K+ high-quality, structured Myanmar sentences.
### ⚠️ Please don’t use this dataset blindly for production training without native review.
⸻
## 📜 License
MIT — free to use, adapt, remix, or improve.
But give credit where it’s due 💛
⸻
## 🔗 Citation
```
@dataset{myanmar_verb_pattern_2025,
title={1 Pattern 10K+ Myanmar Sentences},
author={freococo},
year={2025},
url={https://huggingface.co/datasets/freococo/1_pattern_10Kplus_myanmar_sentences}
}
```
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