---
language:
- zh
tags:
- Seq2SeqLM
- 古文
- 文言文
- 中国古代官职地名拆分
- ancient
- classical
license: cc-by-nc-sa-4.0
---

# <font color="IndianRed"> OTAS (Office Title Address Splitter)</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">OTAS (Office Title Address Splitter) </font> is a Named Entity Recognition Classical Chinese language model that is intended to  <font color="IndianRed">split the address portion in Classical Chinese office titles.</font>. This model is first inherited from raynardj/classical-chinese-punctuation-guwen-biaodian Classical Chinese punctuation model, and finetuned using over a 25,000 high-quality punctuation pairs collected CBDB group (China Biographical Database). 

### <font color="IndianRed"> Sample input txt file </font>
The sample input txt file can be downloaded here:
https://huggingface.co/cbdb/OfficeTitleAddressSplitter/blob/main/input.txt

### <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 AutoTokenizer, AutoModelForTokenClassification

PRETRAINED = "cbdb/OfficeTitleAddressSplitter"
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
model = AutoModelForTokenClassification.from_pretrained(PRETRAINED)
```

<font color="cornflowerblue"> 2. Load Data </font>
```python
# Load your data here
test_list = ['漢軍鑲黃旗副都統', '兵部右侍郎', '盛京戶部侍郎']
```


<font color="cornflowerblue"> 3. Make a prediction </font>
```python
def predict_class(test):
    tokens_test = tokenizer.encode_plus(
        test,
        add_special_tokens=True,
        return_attention_mask=True,
        padding=True,
        max_length=128,
        return_tensors='pt',
        truncation=True
    )

    test_seq = torch.tensor(tokens_test['input_ids'])
    test_mask = torch.tensor(tokens_test['attention_mask'])

    inputs = {
        "input_ids": test_seq,
        "attention_mask": test_mask
    }
    with torch.no_grad():
        # print(inputs.shape)
        outputs = model(**inputs)
        outputs = outputs.logits.detach().cpu().numpy()
        
    softmax_score = softmax(outputs)
    softmax_score = np.argmax(softmax_score, axis=2)[0]
    return test_seq, softmax_score

for test_sen0 in test_list:
    test_seq, pred_class_proba = predict_class(test_sen0)
    test_sen = tokenizer.decode(test_seq[0]).split()
    label = [idx2label[i] for i in pred_class_proba]

    element_to_find = '。'

    if element_to_find in label:
        index = label.index(element_to_find)
        test_sen_pred = [i for i in test_sen0]
        test_sen_pred.insert(index, element_to_find)
        test_sen_pred = ''.join(test_sen_pred)

    else:
        test_sen_pred = [i for i in test_sen0]
        test_sen_pred = ''.join(test_sen_pred)

    print(test_sen_pred)
```
漢軍鑲黃旗。副都統<br>
兵部右侍郎<br>
盛京。戶部侍郎<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.