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---
language:
- zh
tags:
- Seq2SeqLM
- 古文
- 文言文
- ancient
- classical
- 作者书名拆分
license: cc-by-nc-sa-4.0
---
# <font color="IndianRed"> Person And Book Title Splitter </font>
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eZyJgeQOFfpG3QOlq0haDz8pE8jhsGOt#scrollTo=XChPisgxiiji)
Our model <font color="cornflowerblue"> Person And Book Title Splitter </font> is a Named Entity Recognition Classical Chinese language model that is intended to <font color="IndianRed">split author names and book titles, such as 徐元文漢魏風致集.</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/PersonAndBookTitleSplitter"
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
model = AutoModelForTokenClassification.from_pretrained(PRETRAINED)
```
<font color="cornflowerblue"> 2. Load Data </font>
```python
# Load your data here
test_list = ['徐元文漢魏風致集', '熊方補後漢書年表', '羅振玉本朝學術源流槪略 一卷', '陶諧陶莊敏集']
```
<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)
```
徐元文。漢魏風致集<br>
熊方。補後漢書年表<br>
羅振玉。本朝學術源流槪略 一卷<br>
陶諧。陶莊敏集<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. |