该模型预测英语、意大利语、法语和德语文本的标点符号。我们开发了该模型用于恢复转录的口语文本的标点符号。

该多语言模型是基于由提供的 Europarl Dataset 进行训练的，对于荷兰语，我们还包括了 SoNaR Dataset 。请注意，该数据集包含政治演讲。因此，该模型在其他领域的文本上可能会表现不同。

该模型恢复以下标点符号标记：“。”“,”“?”“-”“:”

示例代码

我们提供了一个简单的Python软件包，可以处理任意长度的文本。

安装

要开始使用，请从 pypi 安装软件包：

pip install deepmultilingualpunctuation

恢复标点符号

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
result = model.restore_punctuation(text)
print(result)

输出

我的名字是克拉拉，我住在加利福尼亚的伯克利。Ist das eine Frage, Frau Müller?

预测标签

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
clean_text = model.preprocess(text)
labled_words = model.predict(clean_text)
print(labled_words)

输出

[['My', '0', 0.99998856], ['name', '0', 0.9999708], ['is', '0', 0.99975926], ['Clara', '0', 0.6117834], ['and', '0', 0.9999014], ['I', '0', 0.9999808], ['live', '0', 0.9999666], ['in', '0', 0.99990165], ['Berkeley', ',', 0.9941764], ['California', '。', 0.9952892], ['Ist', '0', 0.9999577], ['das', '0', 0.9999678], ['eine', '0', 0.99998224], ['Frage', '，', 0.9952265], ['Frau', '0', 0.99995995], ['Müller', '？', 0.972517]]

结果

对于连字符和冒号等单个标点符号的性能有所不同，在许多情况下，它们是可选的，可以用逗号或句号代替。该模型根据不同语言的实现以下F1分数：

语言

模型

社区模型

您可以通过设置模型参数来使用不同的模型：

model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction")

如何引用我们

@article{guhr-EtAl:2021:fullstop,
  title={FullStop: Multilingual Deep Models for Punctuation Prediction},
  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  Böhme, Hans Joachim},
  booktitle      = {Proceedings of the Swiss Text Analytics Conference 2021},
  month          = {June},
  year           = {2021},
  address        = {Winterthur, Switzerland},
  publisher      = {CEUR Workshop Proceedings},  
  url       = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}

@misc{https://doi.org/10.48550/arxiv.2301.03319,
  doi = {10.48550/ARXIV.2301.03319},
  url = {https://arxiv.org/abs/2301.03319},
  author = {Vandeghinste, Vincent and Guhr, Oliver},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},
  title = {FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers},
  publisher = {arXiv},
  year = {2023},  
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}