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README.md

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+---
+language:
+- zh
+tags:
+- Seq2SeqLM
+- 古文
+- 文言文
+- 中国古代官职翻译
+- ancient
+- classical
+license: cc-by-nc-sa-4.0
+metrics:
+- sacrebleu
+---
+
+# <font color="IndianRed"> TITO (Classical Chinese Office Title Translation)</font>
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UoG3QebyBlK6diiYckiQv-5dRB9dA4iv?usp=sharing/)
+
+Our model <font color="cornflowerblue">TITO (Classical Chinese Office Title Translation) </font> is a Sequence to Sequence Classical Chinese language model that is intended to  <font color="IndianRed">translate a Classical Chinese office title into English</font>. This model is first inherited from the MarianMTModel, and finetuned using a 6,208 high-quality translation pairs collected CBDB group (China Biographical Database). 
+
+### <font color="IndianRed"> How to use </font>
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+<font color="cornflowerblue"> 1. Import model and packages </font>
+```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)
+```
+
+<font color="cornflowerblue"> 2. Load Data </font>
+```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']
+```
+
+<font color="cornflowerblue"> 3. Make a prediction </font>
+```python
+trans_list = []
+for chinese_sentence in tobe_translated:
+    inputs = tokenizer([chinese_sentence], 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][0]
+    print(f'{chinese_sentence}: {tran}')
+    trans_list.append(tran[0])
+```
+講筵官: Lecturer<br>
+判司簿尉: Supervisor of the Commandant of Records<br>
+散騎常侍: Policy Advisor<br>
+殿中省尚輦奉御: Chief Steward of the Palace Administration<br>
+
+### <font color="IndianRed">Authors </font>
+Queenie Luo (queenieluo[at]g.harvard.edu)
+<br>
+Hongsu Wang
+<br>
+Peter Bol
+<br>
+CBDB Group
+
+### <font color="IndianRed">License </font>
+Copyright (c) 2023 CBDB
+
+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.