nyayalm-tokenizer-64k_v2

NyayaLM Tokenizer (64k v2)

SentencePiece-BPE tokenizer (exact vocabulary size 64,000) designed for NyayaLM-200M, a bilingual (Nepali + English) legal-domain language model.

It is optimized for:

• Native Nepali (Devanagari)
• Romanized Nepali (Latin script)
• English
• Mixed code-switched legal / everyday text
• ChatML-style conversation formatting (Qwen3)

---

Key Features

The tokenizer was trained without any hardcoded seed symbols — the entire alphabet (Devanagari + Latin + digits + punctuation) is learned purely from the bilingual legal + general corpus.

---

Training Data Summary

A carefully balanced bilingual corpus was constructed from the following sources (streaming + deduplication via BLAKE2b):

Nepali (Devanagari)

• Wikipedia Nepali dump
• Himalaya-AI Nepali tokenizer corpus
• Nepali Supreme Court judgement text corpus
• Sangraha Nepali
• Nepal Legal Acts corpus (Nepali)

≈ 885k documents · ~1.33 billion characters

English + Romanized Nepali

• GPT-2 style English pre-train corpus
• High-quality English sentences
• Nepal Legal Acts corpus (English)
• WikiText-103 (raw)
• Roman-Nepali Alpaca
• Roman-Nepali e-commerce QA (ShareGPT format)

≈ 2.40 million documents · ~1.22 billion characters

Final training corpus after balancing & interleaving:

• 3.28 million lines
• ~3.9 GB of clean text
• Strong coverage of both Devanagari and Latin scripts (verified by character statistics)

---

Intended Use

• Pre-training / continued pre-training of small–medium LLMs focused on Nepali legal text
• Instruction tuning and chat with ChatML format
• Any application that needs efficient tokenization of Nepali + English + Romanized Nepali mixtures

---

Evaluation Metrics

We evaluated the tokenizer against strong baselines on four language categories:

• Pure English
• Pure Nepali (Devanagari)
• Romanized Nepali
• Mixed / code-switched

Primary metrics (lower fertility & higher STRR are better):

Overall Mean Metrics (all languages combined)

Per-Language Fertility (lower = better)

Per-Language STRR (higher = better)

Key takeaways:

• Best overall fertility (1.483) and highest STRR (0.709) among all compared tokenizers.
• Dramatically lower fertility on pure Nepali (1.252 vs 1.77–7.28 for others).
• Highest single-token retention on Nepali (81.6%) — most words stay intact.
• Competitive or best on English and Mixed text.
• Strong advantage on legal-domain and code-switched Nepali–English sentences.

---

Usage

from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("chhatramani/nyayalm-tokenizer-64k_v2")

# Simple encode / decode
text = "नेपालको संविधान २०७२ ले नागरिकलाई मौलिक हक प्रदान गरेको छ।"
ids = tok.encode(text, add_special_tokens=False)
print(len(ids), tok.decode(ids))

# ChatML conversation
messages = [
    {"role": "system", "content": "You are a helpful Nepali legal assistant."},
    {"role": "user", "content": "मेरो मौलिक हक के के हुन्?"},
]
prompt = tok.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
print(prompt)

Special token IDs (example)

Pythonprint(tok.convert_tokens_to_ids("<|endoftext|>"))  # 63982
print(tok.convert_tokens_to_ids("<|im_start|>"))   # 63983
print(tok.convert_tokens_to_ids("<|im_end|>"))     # 63984
print(len(tok))

Citation

If you use this tokenizer in your research or product, please cite:

bibtex@misc{nyayalm-tokenizer-64k-v2,
  title  = {NyayaLM 64k SentencePiece-BPE Tokenizer},
  author = {Chhatramani},
  year   = {2026},
  url    = {https://huggingface.co/chhatramani/nyayalm-tokenizer-64k_v2}
}

License

Apache License 2.0