---
license: cc-by-nc-4.0
language:
- en
- vi
tags:
- translation
- machine-translation
- english-to-vietnamese
- onnx
- seq2seq
- bart
pipeline_tag: translation
widget:
- text: "Machine translation is useful for multilingual applications."
  example_title: "EN → VI"
---

# AnViMt

**AnViMt** — **An**=English **Vi**=Vietnamese **Mt**=Machine Translation — dịch **EN → VI** cho văn bản thường + kỹ thuật/hệ thống/lập trình/hội thoại.

Export sang **ONNX** để chạy chỉ với `onnxruntime + sentencepiece + numpy` (không cần `torch/transformers`).

## Architecture

**Base:** `transformers.BartForConditionalGeneration` train từ đầu, không phải pretrained BART.

- `vocab_size=32000` (SentencePiece BPE, `byte_fallback=True`)
- `d_model=512` | `encoder_layers=6` | `decoder_layers=6`
- `encoder_ffn_dim=2048` | `decoder_ffn_dim=2048`
- `encoder_attention_heads=8` | `decoder_attention_heads=8`
- `max_position_embeddings=256` | `dropout 0.1`
- `tie_word_embeddings=True` | `is_encoder_decoder=True`
- `pad_token_id=0` (`<pad>`) | `unk_token_id=1` (`<unk>`) | `bos_token_id=2` (`<s>`) | `eos_token_id=3` (`</s>`) | `decoder_start_token_id=2`
- **Params:** ~60.79M

```
EN text
  ↓ spm.model [2] + BPE ids + [3] (pad 0, bos 2, eos 3, max 256)
  ↓
encoder_model.onnx (input_ids, attention_mask → encoder_hidden_states [batch, seq, 512])
  ↓
decoder_model.onnx / decoder_with_past_model.onnx
  (decoder_input_ids [2] + encoder_hidden_states + encoder_attention_mask + past_key_values → logits)
  ↓ autoregressive greedy (argmax) đến eos 3
  ↓ sp.decode → VI text
```

## Files

Export bằng `optimum` `task=seq2seq-lm` `opset=14`:

```
AnViMt/
├── encoder_model.onnx          # 136M
├── decoder_model.onnx          # 223M
├── decoder_with_past_model.onnx # KV cache (tùy chọn, tăng tốc)
├── spm.model                   # 752K, vocab 32000
└── spm.vocab
```

Runtime tối thiểu chỉ cần `encoder_model.onnx` + `decoder_model.onnx` + `spm.model`. `decoder_with_past_model.onnx` dùng khi muốn KV-cache.

Không cần: `config.json`, `pytorch_model.bin`, `safetensors`, `torch`, `transformers`.

## Installation

```bash
pip install onnxruntime sentencepiece numpy
# Termux: apt install python3-onnxruntime python3-sentencepiece python3-numpy
```

## Quick Start

```
project/
├── main.py
├── encoder_model.onnx
├── decoder_model.onnx
└── spm.model   # (decoder_with_past_model.onnx nếu có)
```

`main.py` thực tế (đã test, xử lý đúng BOS/EOS/pad):

```python
import numpy as np, onnxruntime as ort, sentencepiece as spm
sp = spm.SentencePieceProcessor(); sp.load("spm.model")
enc = ort.InferenceSession("encoder_model.onnx", providers=["CPUExecutionProvider"])
dec = ort.InferenceSession("decoder_model.onnx", providers=["CPUExecutionProvider"])
BOS, EOS, PAD = 2, 3, 0
def translate(text, max_len=256):
    ids = [BOS] + sp.encode(text, out_type=int)[:254] + [EOS]
    input_ids = np.array([ids], dtype=np.int64)
    attn = np.ones_like(input_ids)
    enc_hs = enc.run(None, {"input_ids": input_ids, "attention_mask": attn})[0]
    gen = [BOS]
    for _ in range(max_len):
        dec_ids = np.array([gen], dtype=np.int64)
        logits = dec.run(None, {"input_ids": dec_ids, "encoder_hidden_states": enc_hs, "encoder_attention_mask": attn})[0]
        nxt = int(np.argmax(logits[0, -1]))
        gen.append(nxt)
        if nxt == EOS: break
    out = [x for x in gen[1:] if x not in (BOS, EOS, PAD)]
    if EOS in out: out = out[:out.index(EOS)]
    return sp.decode(out)
```

## Training Data

Pretrain 1.13M EN-VI (PhoMT + TED + TECH) → finetune 200k TECH → finetune 27k v3 (hội thoại + kỹ thuật). Tokenizer BPE 32k train trên 400k pairs reservoir sampled, `nmt_nfkc`, `split_digits`, `byte_fallback`.

## Limitations

EN → VI only. Câu >254 BPE tokens bị cắt. Thuật ngữ mới có thể giữ nguyên EN.

## License

CC BY-NC 4.0 — non-commercial.
Author: Phitran21