A sequence-to-sequence translation model that translates text from Chinese 🇨🇳 to English 🇬🇧. This repository hosts an ONNX-converted version of Helsinki-NLP/opus-mt_tiny_zho-eng, packaged for use with the Teradata mldb.ONNXSeq2Seq BYOM function.

This repository does not redistribute the original model weights. It contains only:

onnx/model-fp32.onnx — full-precision ONNX graph
tokenizer.json — repacked Marian tokenizer suitable for BYOM
config.json — model architecture metadata, copied unchanged from the upstream repo
generation_config.json — generation defaults, copied unchanged from the upstream repo

A weight-only int8-quantized variant is also published as onnx/model-int8.onnx. Use model-fp32.onnx unless deployment size is a constraint.

For the original PyTorch weights and training details, see the upstream model: Helsinki-NLP/opus-mt_tiny_zho-eng.

Specifications


Source language	Chinese 🇨🇳 (`zh`)
Target language	English 🇬🇧 (`en`)
Architecture	MarianMT (encoder-decoder)
Max input tokens	256
Max output tokens	512
ONNX file size	177 MB (fp32) / 95 MB (int8)
ONNX opset	14
ONNX IR version	8 (BYOM 7.0+ compatible)
License	Apache-2.0 (from upstream)
Reference	https://huggingface.co/Helsinki-NLP/opus-mt_tiny_zho-eng

Generation parameters are configurable at SQL time via the mldb.ONNXSeq2Seq USING clause through Const_* keys: Const_min_length, Const_max_length, Const_num_beams, Const_length_penalty, Const_repetition_penalty. They are not fixed in the ONNX graph. (num_return_sequences is the exception — it's baked into the graph as 1.)

Quickstart: Deploying this Model in Teradata

Requires Teradata 17.20+ with BYOM 7.0.0.4 or newer (the conversion targets ONNX IR version 8, which BYOM 7.0.x requires).

Note on schema name: the SQL example below uses mldb.ONNXSeq2Seq. On modern Teradata deployments BYOM is installed in the td_mldb database — adjust the schema prefix in the SQL accordingly.

python

import getpass
import teradataml as tdml
from huggingface_hub import hf_hub_download

repo_id  = "Teradata/opus-mt_tiny_zho-eng"
model_id = "opus-mt_tiny_zho-eng"          # used as BYOM model_id

# 1. Download artifacts from this repo
hf_hub_download(repo_id=repo_id, filename="onnx/model-fp32.onnx", local_dir="./")
hf_hub_download(repo_id=repo_id, filename="tokenizer.json",       local_dir="./")

# 2. Connect to Teradata
tdml.create_context(
    host=input("host: "),
    username=input("user: "),
    password=getpass.getpass("password: "),
)

# 3. Load model + tokenizer into BYOM tables
tdml.save_byom(model_id=model_id, model_file="onnx/model-fp32.onnx",
               table_name="translation_models")
tdml.save_byom(model_id=model_id, model_file="tokenizer.json",
               table_name="translation_tokenizers")

# 4. Translate
query = f"""
SELECT id, sequences
FROM mldb.ONNXSeq2Seq(
    ON (SELECT id, txt FROM your_input_table) AS InputTable
    ON (SELECT model_id, model FROM translation_models
         WHERE model_id = '{model_id}') AS ModelTable DIMENSION
    ON (SELECT model AS tokenizer FROM translation_tokenizers
         WHERE model_id = '{model_id}') AS TokenizerTable DIMENSION
    USING
        Accumulate('id')
        ModelOutputTensor('sequences')
        SkipSpecialTokens('true')
        OutputLength(512)
        OverwriteCachedModel('true')
        Const_min_length(1)
        Const_max_length(64)
        Const_num_beams(4)
        Const_length_penalty(1.0)
        Const_repetition_penalty(1.0)
) AS t
"""
print(tdml.DataFrame.from_query(query))

How this model was converted

This model was produced with the open-source teradata-opus-translate package, which exports the encoder/decoder, stitches in the BeamSearch op, applies weight-only int8 quantization, and verifies parity against PyTorch on a small sample set.

Note: the same package can convert any Helsinki-NLP MarianMT model (including ones not in this collection) to a BYOM-ready ONNX bundle. If you have a translation pair that's not published here, install the package and run:

python
1from teradata_opus_translate import convert_model, convert_tokenizer 2 3convert_model( 4 "Helsinki-NLP/<your-model>", 5 output_path="model-fp32.onnx", 6) 7convert_tokenizer( 8 "Helsinki-NLP/<your-model>", 9 output_path="tokenizer.json", 10)

The resulting model-fp32.onnx and tokenizer.json are ready to deploy with the Quickstart flow above.

Disclaimer

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