README.md

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license: mit
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##**BertForSequenceClassification model (Classical Chinese)**

This BertForSequenceClassification Classical Chinese model is intended to predict whether a Classical Chinese sentence is a letter title (书信标题) or not. This model is first inherited from the BERT base Chinese model (MLM), and finetuned using a large corpus of Classical Chinese language (3GB textual dataset), then concatenated with the BertForSequenceClassification architecture to perform a binary classification task.

**Labels: 0 = non-letter, 1 = letter**

##**Model description**

The BertForSequenceClassification model architecture inherits the BERT base model and concatenates a fully-connected linear layer to perform a binary-class classification task.

**Masked language modeling (MLM):** The masked language modeling architecture randomly masks 15% of the words in the inputs, and the model is trained to predict the masked words. The BERT base model uses this MLM architecture and is pre-trained on a large corpus of data. BERT is proven to produce robust word embedding and can capture rich contextual and semantic relationships. Our model inherits the publicly available pre-trained BERT Chinese model trained on modern Chinese data. To perform a Classical Chinese letter classification task, we first finetuned the model using a large corpus of Classical Chinese data (3GB textual data), and then connected it to the BertForSequenceClassification architecture for Classical Chinese letter classification.  

**Sequence classification:** the model concatenates a fully-connected linear layer to output the probability of each class. In our binary classification task, the final linear layer has two classes.

##**Intended uses & limitations**
Note that this model is primiarly aimed at predicting whether a Classical Chinese sentence is a letter title (书信标题) or not.

##**How to use**
You can use this model directly with a pipeline for masked language modeling: