OneNMT v3b is a 1.1B parameter multilingual seq2seq translation model supporting 36 × 36 language pairs across 35 Indian languages and English. It is based on a deep transformer_18_18 architecture (18 encoder + 18 decoder layers) trained with fairseq and converted to the HuggingFace MBart format.

⚠️ Tokenization Warning: OneNMT v3b uses a custom fairseq dictionary (fairseq_dict.json) for token ID mapping. Using AutoTokenizer or any standard MBart tokenizer will produce wrong IDs and garbled output. Always use the provided hf_inference.py or the translate_onemt() wrapper.

Architecture

Property	Value
Architecture	`transformer_18_18` (MBartForConditionalGeneration wrapper)
Encoder / Decoder layers	18 + 18
Hidden size	1024
Attention heads	16
Parameters	~1.1B
Positional embeddings	Sinusoidal (not learned), max 256 positions
Normalization	Pre-norm (`normalize_before=True`)
Vocabulary	65,400 tokens (fairseq dictionary ordering)
Language tag format	`###src_flores-to-tgt_flores### {text}`
Decoder start token	EOS (id=2) — fairseq convention

Supported Languages

Short Code	Language	Script	FLORES-200 Code
`eng`	English	Latin	`eng_Latn`
`hin`	Hindi	Devanagari	`hin_Deva`
`tel`	Telugu	Telugu	`tel_Telu`
`tam`	Tamil	Tamil	`tam_Taml`
`mal`	Malayalam	Malayalam	`mal_Mlym`
`kan`	Kannada	Kannada	`kan_Knda`
`ben`	Bengali	Bengali	`ben_Beng`
`guj`	Gujarati	Gujarati	`guj_Gujr`
`mar`	Marathi	Devanagari	`mar_Deva`
`pan`	Punjabi	Gurmukhi	`pan_Guru`
`urd`	Urdu	Arabic	`urd_Arab`
`asm`	Assamese	Bengali	`asm_Beng`
`npi`	Nepali	Devanagari	`npi_Deva`
`ory`	Odia	Odia	`ory_Orya`
`san`	Sanskrit	Devanagari	`san_Deva`
`mai`	Maithili	Devanagari	`mai_Deva`
`brx`	Bodo	Devanagari	`brx_Deva`
`doi`	Dogri	Devanagari	`doi_Deva`
`gom`	Konkani	Devanagari	`gom_Deva`
`mni`	Meitei	Bengali	`mni_Beng`
`sat`	Santali	Ol Chiki	`sat_Olck`
`kas`	Kashmiri	Arabic	`kas_Arab`
`snd`	Sindhi	Arabic	`snd_Arab`

Installation

pip install -r requirements.txt

Quick Start

python

from hf_inference import translate_onemt, translate_batch

# Single sentence — English → Telugu
print(translate_onemt("Hello, how are you?", "eng", "tel"))
# హలో, మీరు ఎలా ఉన్నారు?

# Code-mixed Hindi → Telugu
print(translate_onemt("मुझे meeting attend करनी है।", "hin", "tel"))
# నాకు మీటింగ్‌కు హాజరు కావాలి.

# Telugu → English
print(translate_onemt("నమస్కారం, మీరు ఎలా ఉన్నారు?", "tel", "eng"))
# Hello, how are you?

# Tamil → Hindi
print(translate_onemt("இன்று வானிலை நன்றாக உள்ளது.", "tam", "hin"))
# आज मौसम अच्छा है।

# Batch translation
sentences = ["Good morning.", "Thank you.", "See you tomorrow."]
results   = translate_batch(sentences, sl="eng", tl="hin", batch_size=32)
for src, tgt in zip(sentences, results):
    print(f"{src}  →  {tgt}")

How Tokenization Works

Unlike standard MBart, OneNMT v3b uses a two-step tokenization pipeline:

source text
  → prepend language tag:   ###eng_Latn-to-tel_Telu### {text}
  → SentencePiece encoding  →  subword pieces
  → fairseq dict lookup     →  integer IDs   ← NOT SPM-native IDs
  → MBart encoder

The fairseq dictionary orders tokens by training frequency (descending), not SPM's internal ordering. This means the same subword piece maps to completely different IDs in the two systems — for example, T is ID 29 in the fairseq dict but ID 35376 via the HF tokenizer. Wrong IDs → wrong embeddings → garbled output.

The hf_inference.py script handles this correctly using onemtv3b_spm.model for segmentation and fairseq_dict.json for ID lookup.

Repository Files

File	Description
`model.safetensors`	Model weights
`config.json`	HF model architecture config
`generation_config.json`	Default generation parameters
`fairseq_dict.json`	Required — fairseq vocab ID mapping (piece → ID)
`onemtv3b_spm.model`	Required — SentencePiece model for subword segmentation
`hf_inference.py`	Ready-to-use inference script

tokenizer.json, tokenizer_config.json, special_tokens_map.json are present for HF Hub format compliance but must not be used for inference — they produce incorrect token IDs due to the SPM vs fairseq ordering mismatch described above.

Generation Parameters

Parameter	Value	Notes
`decoder_start_token_id`	2	EOS token — fairseq convention (not BOS)
`forced_bos_token_id`	None	No forced BOS
`num_beams`	5	Beam search
`max_new_tokens`	256	Matches model's trained max position
`no_repeat_ngram_size`	3	Reduces repetition
`repetition_penalty`	1.3	Additional repetition control

Citation

bibtex

@misc{mujadia2025bhashaversetranslationecosystem,
      title={BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages}, 
      author={Vandan Mujadia and Dipti Misra Sharma},
      year={2025},
      eprint={2412.04351},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.04351}, 
}

License

MIT

VIzzxy/Bhashaverse_base_updt_WMT2026

OneNMT v3b — Indic Multilingual Neural Machine Translation