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  # <font color="IndianRed"> BertForSequenceClassification model (Classical Chinese) </font>
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- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jVu2LrNwkLolItPALKGNjeT6iCfzF8Ic?usp=sharing/)
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- This Korean-person-name-romanization-transformer model is intended to convert Korean person name into English romanization. The model's input sentence is a Korean name, and the output sentence is its corresponding English romanization.
 
 
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  ## <font color="IndianRed"> Model description </font>
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  ## <font color="IndianRed"> Intended uses & limitations </font>
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- Note that this model primarily aims at translating Korean names into English romanization.
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- ### <font color="IndianRed"> How to use </font>
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- Here is how to use this model to get the features of a given text in PyTorch:
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- <font color="cornflowerblue"> 1. Import model and packages </font>
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- to be finished...
 
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  # <font color="IndianRed"> BertForSequenceClassification model (Classical Chinese) </font>
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+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1aIGvyvqRdHv7QTRahhD1sf8L6yV39kxc?usp=sharing/)
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+ The Kraft (Korean Romanization From Transformer) model translates the characters (Hangul) of a Korean person name into the Roman alphabet ([McCune–Reischauer system](https://en.wikipedia.org/wiki/McCune%E2%80%93Reischauer)). Kraft uses the Transformer architecture, which is a type of neural network architecture that was introduced in the 2017 paper "Attention Is All You Need" by Google researchers. It is designed for sequence-to-sequence tasks, such as machine translation, language modeling, and summarization.
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+ Translating a Korean name into an English romanization is a type of machine translation task, where the input is a sequence of characters representing a Korean name, and the output is a sequence of characters representing the English romanization of that name. The Transformer model, with its attention mechanism and ability to handle input sequences of varying lengths, is well-suited to this type of task, and is able to accurately translate Korean names to English romanization.
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  ## <font color="IndianRed"> Model description </font>
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  ## <font color="IndianRed"> Intended uses & limitations </font>
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+ Note that this model primarily aims at translating Korean names into English romanization.