bert-ancient-chinese
Introduction
With the current wave of Artificial Intelligence and Digital Humanities sweeping the world, the automatic analysis of modern Chinese has achieved great results. However, the automatic analysis and research of ancient Chinese is relatively weak, and it is difficult to meet the actual needs of Sinology, history, philology, Chinese history and the education of Sinology and traditional culture. There are many controversies about characters, words and parts of speech in ancient Chinese, and there are many difficulties in resource construction. Digital Humanities research requires large-scale corpora and high-performance ancient natural language processing tools. In view of the fact that pre-trained language models have greatly improved the accuracy of text mining in English and modern Chinese texts, there is an urgent need for pre-trained models for the automatic processing of ancient texts.
In 2022, we took part in EvaHan 2022, the first NLP tool evaluation competition in the field of ancient Chinese. bert-ancient-chinese
is trained to further optimize the model effect in open environment.
If you want to refer to our work, you can refer to this paper:
@inproceedings{wang2022uncertainty,
title={The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS},
author={Wang, Pengyu and Ren, Zhichen},
booktitle={Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages},
pages={164--168},
year={2022}
}
You can view the introduction of the Chinese version through this link.
Further Pre-training
Compared with the previous pre-trained models, bert-ancient-chinese
mainly has the following characteristics:
Ancient Chinese texts mostly appear in traditional Chinese characters and contain a large number of uncommon Chinese characters, which makes the
vocab table
(vocabulary) of the pre-trained model without some uncommon Chinese characters.bert-ancient-chinese
further expands thevocab
(dictionary) of the pre-trained model by learning in a large-scale corpus. The finalvocab table
size is 38208, compared tobert-base-chinese
vocabulary size of 21128,siku-bert
vocabulary size of 29791,bert-ancient-chinese
has a larger vocabulary, and also includes more uncommon vocabulary word, which is more conducive to improving the performance of the model in downstream tasks. Thevocab table
is the vocabulary table, which is included in thevocab.txt
in the pre-trained model.bert-ancient-chinese
uses a larger training set. Compared withsiku-bert
only using"Siku Quanshu"
as training dataset, we use a larger-scale dataset (about six times that of"Siku Quanshu"
), covering from the Ministry of Cong, the Ministry of Taoism, the Ministry of Buddhism, the Ministry of Confucianism, the Ministry of Poetry, the Ministry of History, the Ministry of Medicine, the Ministry of Art, the Ministry of Yi, and the Ministry of Zi, are richer in content and wider in scope than the"Siku Quanshu"
.Based on the idea of
Domain-Adaptive Pretraining
,bert-ancient-chinese
was trained on the basis ofbert-base-chinese
and was combined with ancient Chinese corpus to obtain a pre-trained model for the field of automatic processing of ancient Chinese.
How to use
Huggingface Transformers
The from_pretrained
method based on Huggingface Transformers can directly obtain bert-ancient-chinese
model online.
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Jihuai/bert-ancient-chinese")
model = AutoModel.from_pretrained("Jihuai/bert-ancient-chinese")
Download PTM
The model we provide is the PyTorch
version.
From Huggingface
Download directly through Huggingface's official website, and the model on the official website has been updated to the latest version simultaneously:
- bert-ancient-chinese:Jihuai/bert-ancient-chinese · Hugging Face
From Cloud Disk
Download address:
Model | Link |
---|---|
bert-ancient-chinese | Link Extraction code: qs7x |
Evaluation & Results
We tested and compared different pre-trained models on the training and test sets provided by the competition EvaHan 2022. We compare the performance of the models by fine-tuning them on the downstream tasks of Chinese Word Segmentation(CWS)
and part-of-speech tagging(POS Tagging)
.
We use BERT+CRF
as the baseline model to compare the performance of siku-bert
, siku-roberta
and bert-ancient-chinese
on downstream tasks. To fully utilize the entire training dataset, we employ K-fold cross-validation
, while keeping other hyperparameters the same. The evaluation index is the F1 value
.
Zuozhuan | Shiji | |||
CWS | POS | CWS | POS | |
siku-bert | 96.0670% | 92.0156% | 92.7909% | 87.1188% |
siku-roberta | 96.0689% | 92.0496% | 93.0183% | 87.5339% |
bert-ancient-chinese | 96.3273% | 92.5027% | 93.2917% | 87.8749% |
Citing
If our content is helpful for your research work, please quote it in the paper.
Disclaim
The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project.
Acknowledgment
bert-ancient-chinese
is based on bert-base-chinese to continue training.
Thanks to Prof. Xipeng Qiu and the Natural Language Processing Laboratory of Fudan University.
Contact us
Pengyu Wang:[email protected]
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