This repository contains the translation model for English-Traditional Chinese trained with HPLT data only. The model is available in both Marian and Hugging Face formats.
Model Info
Source language: English
Target language: Traditional Chinese
Data: HPLT data only
Model architecture: Transformer-base
Tokenizer: SentencePiece (Unigram)
Cleaning: We used OpusCleaner with a set of basic rules. Details can be found in the filter files here.
The model has been trained with MarianNMT and the weights are in the Marian format. We have also converted the model into the Hugging Face format so it is compatible with transformers.
Using Marian
To run inference with MarianNMT, refer to the Inference/Decoding/Translation section of our GitHub repository. You will need the model file model.npz.best-chrf.npz and the vocabulary file model.en-zh_hant.spm from this repository.
Using transformers
We have also converted this model to the Hugging Face format and you can get started with the script below. Note that due a known issue in weight conversion, the checkpoint cannot work with transformer versions <4.26 or >4.30. We tested and suggest pip install transformers==4.28.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/translate-en-zh_hant-v1.0-hplt")
model = AutoModelForSeq2SeqLM.from_pretrained("HPLT/translate-en-zh_hant-v1.0-hplt")
inputs = ["Input goes here.", "Make sure the language is right."]
batch_tokenized = tokenizer(inputs, return_tensors="pt", padding=True)
model_output = model.generate(
**batch_tokenized, num_beams=6, max_new_tokens=512
)
batch_detokenized = tokenizer.batch_decode(
model_output,
skip_special_tokens=True,
)
print(batch_detokenized)
Benchmarks
When decoded using Marian, the model has the following test scores.
Test set
BLEU
chrF++
COMET22
FLORES200
25.4
18.9
0.8017
NTREX
21.3
21.6
0.7492
Acknowledgements
This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number 10052546]
Brought to you by researchers from the University of Edinburgh and Charles University in Prague with support from the whole HPLT consortium.
