Our model TITO (Classical Chinese Office Title Translation) is a Sequence to Sequence Classical Chinese language model that is intended to translate a Classical Chinese office title into English. This model is first inherited from the MarianMTModel, and finetuned using a 6,208 high-quality translation pairs collected CBDB group (China Biographical Database).

How to use

Here is how to use this model to get the features of a given text in PyTorch:

1. Import model and packages

python

from transformers import MarianMTModel, MarianTokenizer

device = torch.device('cuda')
model_name = 'cbdb/ClassicalChineseOfficeTitleTranslation'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name).to(device)

2. Load Data

python

# Load your data here
tobe_translated = ['講筵官','判司簿尉','散騎常侍','殿中省尚輦奉御']

3. Make a prediction

python

inputs = tokenizer(tobe_translated, return_tensors="pt", padding=True).to(device)
translated = model.generate(**inputs, max_length=128)
tran = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
for c, t in zip(tobe_translated, tran):
    print(f'{c}: {t}')

講筵官: Lecturer
判司簿尉: Supervisor of the Commandant of Records
散騎常侍: Policy Advisor
殿中省尚輦奉御: Chief Steward of the Palace Administration

Authors

Queenie Luo (queenieluo[at]g.harvard.edu)
Hongsu Wang
Peter Bol
CBDB Group

License

Except where otherwise noted, content on this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.