Bert Restore Punctuation

🚀 ✨ BERT恢复标点模型

这是一个基于bert-base-uncased的模型，经过微调后可用于在Yelp评论上进行标点恢复。该模型能够预测纯小写文本的标点和大写形式，例如可用于自动语音识别（ASR）的输出，或其他文本丢失标点的情况。此模型既可以直接用作通用英语的标点恢复模型，也可以在特定领域的文本上进行进一步微调，以完成标点恢复任务。模型可以恢复以下标点符号 -- [! ? . , - : ; ' ]，同时也能恢复单词的大写形式。

🚀 快速开始

下面是快速使用该模型的方法。

📦 安装指南

首先，安装所需的包。

pip install rpunct

💻 使用示例

基础用法

from rpunct import RestorePuncts
# The default language is 'english'
rpunct = RestorePuncts()
rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were
a few atoms thick anything thicker would cause the electrons to scatter in ways that could not be disentangled now a team again led by david muller the samuel b eckert
professor of engineering has bested its own record by a factor of two with an electron microscope pixel array detector empad that incorporates even more sophisticated
3d reconstruction algorithms the resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves""")
# Outputs the following:
# In 2018, Cornell researchers built a high-powered detector that, in combination with an algorithm-driven process called Ptychography, set a world record by tripling the
# resolution of a state-of-the-art electron microscope. As successful as it was, that approach had a weakness. It only worked with ultrathin samples that were a few atoms
# thick. Anything thicker would cause the electrons to scatter in ways that could not be disentangled. Now, a team again led by David Muller, the Samuel B. 
# Eckert Professor of Engineering, has bested its own record by a factor of two with an Electron microscope pixel array detector empad that incorporates even more
# sophisticated 3d reconstruction algorithms. The resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves.

该模型可以处理任意长度的英文文本，并在可用时使用GPU进行加速。

📚 详细文档

📡 训练数据

以下是用于微调模型的产品评论数量：

我们发现模型在大约 3个训练周期 时收敛效果最佳，这里展示的就是该训练周期下的模型，并且可以下载使用。

🎯 准确率

微调后的模型在45,990个保留文本样本上取得了以下准确率：

以下是模型在每个标签上的性能细分：

📄 许可证

本项目采用MIT许可证。

☕ 联系我们

如有问题、反馈或需要类似模型，请联系 Daulet Nurmanbetov。