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myX-Tokenizer-BPE

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- kalixlouiis/raw-data
+language:
+- my
+new_version: DatarrX/myX-Tokenizer-Unigram
+pipeline_tag: feature-extraction
 ---
+# DatarrX / myX-Tokenizer-BPE ⚙️
+**myX-Tokenizer-BPE** is a Byte Pair Encoding (BPE) based tokenizer specifically trained for the Burmese language. Developed by [**Khant Sint Heinn (Kalix Louis)**](https://huggingface.co/kalixlouiis) under [**DatarrX (Myanmar Open Source NGO)**](https://huggingface.co/DatarrX), this model serves as a baseline for Burmese NLP tasks using the BPE algorithm.
+## 🎯 Objectives & Characteristics
+* **BPE Baseline:** Designed to provide a standard BPE-based segmentation for Burmese text.
+* **Burmese Focus:** This model was trained exclusively on Burmese text, making it highly specialized for native scripts.
+* **Memory Efficiency:** Trained using a RAM-efficient approach with a large-scale corpus.
+## 🛠️ Technical Specifications
+* **Algorithm:** Byte Pair Encoding (BPE).
+* **Vocabulary Size:** 64,000.
+* **Normalization:** NFKC.
+* **Features:** Byte-fallback, Split Digits, and Dummy Prefix.
 ### Training Data
+Trained on [kalixlouiis/raw-data](https://huggingface.co/datasets/kalixlouiis/raw-data) using **1.5 million** Burmese-only sentences.
+## ⚠️ Important Considerations (Limitations)
+* **English Language Weakness:** Since this model was trained purely on Burmese data, it is notably weak in processing English text, often leading to excessive character-level fragmentation for Latin scripts.
+* **BPE Nature:** Compared to our Unigram models, this BPE version may offer different segmentation logic which might affect certain downstream NLP tasks.
+---
+# DatarrX - myX-Tokenizer-BPE (မြန်မာဘာသာ) ⚙️
+**myX-Tokenizer-BPE** သည် Byte Pair Encoding (BPE) algorithm ကို အသုံးပြု၍ မြန်မာဘာသာစကားအတွက် အထူးရည်ရွယ် တည်ဆောက်ထားသော Tokenizer ဖြစ်ပါသည်။ ဤ Model ကို **DatarrX (Myanmar Open Source NGO)**](https://huggingface.co/DatarrX) မှ ထုတ်ဝေခြင်းဖြစ်ပြီ [**Khant Sint Heinn (Kalix Louis)**](https://huggingface.co/kalixlouiis) မှ အဓိက ဖန်တီးထားခြင်း ဖြစ်ပါသည်။
+## 🎯 ရည်ရွယ်ချက်နှင့် ထူးခြားချက်များ
+* **BPE အခြေခံ:** မြန်မာစာသားများကို BPE နည်းပညာဖြင့် ဖြတ်တောက်ရာတွင် စံနှုန်းတစ်ခုအဖြစ် အသုံးပြုနိုင်ရန်။
+* **မြန်မာစာ သီးသန့်:** ဤ Model ကို မြန်မာစာသား သီးသန့်ဖြင့်သာ လေ့ကျင့်ထားသဖြင့် ဗမာ(မြန်မာ)စာအရေးအသားများအတွက် အထူးပြုထားပါသည်။
+* **အရည်အသွေးမြင့် Training:** စာကြောင်းပေါင်း ၁.၅ သန်းကို အသုံးပြု၍ RAM-efficient ဖြစ်သော နည်းလမ်းဖြင့် တည်ဆောက်ထားပါသည်။
+## 🛠️ နည်းပညာဆိုင်ရာ အချက်အလက်များ
+* **Algorithm:** Byte Pair Encoding (BPE)။
+* **Vocab Size:** 64,000။
+* **Normalization:** NFKC။
+* **Features:** Byte-fallback, Split Digits နှင့် Dummy Prefix အင်္ဂါရပ်များ ပါဝင်ပါသည်။
+### အသုံးပြုထားသော Dataset
+[kalixlouiis/raw-data](https://huggingface.co/datasets/kalixlouiis/raw-data) ထဲမှ သန့်စင်ပြီးသား မြန်မာစာကြောင်းပေါင်း **၁.၅ သန်း (1.5 Million)** ကို အသုံးပြုထားပါသည်။
+## ⚠️ သိထားရန် ကန့်သတ်ချက်များ
+* **အင်္ဂလိပ်စာ အားနည်းမှု:** ဤ Model ကို မြန်မာစာ သီးသန့်ဖြင့်သာ Train ထားခြင်းကြောင့် အင်္ဂလိပ်စာလုံးများကို ဖြတ်တောက်ရာတွင် အလွန်အားနည်းပြီး စာလုံးတစ်လုံးချင်းစီ ကွဲထွက်သွားတတ်ပါသည်။
+* **BPE ၏ သဘာဝ:** ကျွန်တော်တို့၏ Unigram model များနှင့် ယှဉ်ပါက ဖြတ်တောက်ပုံခြင်း ကွဲပြားနိုင်သဖြင့် မိမိအသုံးပြုမည့် task အပေါ် မူတည်၍ ရွေးချယ်ရန် လိုအပ်ပါသည်။
+---
+## 💻 How to Use (အသုံးပြုနည်း)
+```python
+import sentencepiece as spm
+from huggingface_hub import hf_hub_download
+model_path = hf_hub_download(repo_id="DatarrX/myX-Tokenizer-BPE", filename="myX-Tokenizer.model")
+sp = spm.SentencePieceProcessor(model_file=model_path)
+text = "မြန်မာစာကို BPE algorithm နဲ့ ဖြတ်တောက်ကြည့်ခြင်း။"
+print(sp.encode_as_pieces(text))
+```
+# ✍️ Project Authors
+- Developer: [**Khant Sint Heinn (Kalix Louis)**](https://huggingface.co/kalixlouiis)
+- Organization: [**DatarrX (Myanmar Open Source NGO)**](https://huggingface.co/DatarrX)