J1mmymm/transformer-en-zh-translator

English-to-Chinese MarianMT fine-tune

This repository contains an English-to-Chinese MarianMT model fine-tuned from `Helsinki-NLP/opus-mt-en-zh`, plus the checkpoint of a smaller Transformer trained from scratch for comparison.

Intended use

• —Educational English-to-Chinese sentence translation
• —Reproducing the accompanying Transformer/MarianMT comparison
• —Local inference and further research fine-tuning

This is not a production translation service. It was trained on short conversational sentences and can mistranslate long, technical, safety-critical, or culturally sensitive text.

Evaluation

The fixed course test split contained 2,991 sentence pairs. Scores were computed with SacreBLEU (tokenize="zh"), chrF++ and TER.

These scores describe this fixed local split only and should not be interpreted as general-domain translation quality.

Training details

• —Base model: Helsinki-NLP/opus-mt-en-zh
• —Original course corpus: 26,918 training pairs, split into 25,573 train and 1,345 validation pairs
• —Epochs: 4; selected checkpoint: epoch 3 by validation loss
• —Optimizer: AdamW
• —Learning rate: 2e-5 with cosine decay
• —Micro-batch: 16; gradient accumulation: 2
• —Beam size: 4
• —Seed: 2026
• —Training device: NVIDIA GeForce RTX 4060 Laptop GPU

The sentence content appears to derive from the Tatoeba/ManyThings English–Mandarin collection. The course files did not retain per-sentence attribution metadata, so the corpus itself is not redistributed here. See the project repository for the expected data format and reproducibility notes.

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "J1mmymm/transformer-en-zh-translator"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

inputs = tokenizer("Machine learning is useful.", return_tensors="pt")
outputs = model.generate(**inputs, num_beams=4, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Additional scratch checkpoint

scratch/scratch_transformer.pt contains the state dictionary, English and Chinese vocabularies, exact architecture/training configuration, best validation loss, and best epoch for the custom nn.Transformer model. Load it with en_zh_translator.load_scratch_checkpoint from the GitHub project.

Limitations and bias

The model inherits limitations and biases from the base model and the short conversational fine-tuning corpus. References may have multiple valid translations, so exact-match is a particularly strict and incomplete metric. Human review is required before consequential use.

Licensing and attribution

The base model is Apache-2.0. Project code and this derived model release are published under Apache-2.0. Dataset text is not included; users preparing their own Tatoeba/ManyThings split must follow its attribution requirements.