MRC_NER

In this project, I want to accumulate the NLP model I have learned Not Only MRC_NER.

Additionally, I try to build them up in one package,

which means when I want to add and test a new model, I can avoid writing the repeated code.

QAQ

MRC_NER

According to A Unifed MRC Frameword for Named Entity Recognition, It is good idea to solve the nested named entity recognition task. I reproduce this paper in another way. In this paper, the author designed 2 binary-classifier. One is to predict start index, another is to predict end Index. And In the loss function, the author proposed a Start-End Matching Loss. Personally speaking, it sounds very complex. In my code, I just simplify the model as a classifier which can classify one word into 3 categories, ['B','I','O']. As for the loss function, I use crossEntropy.

Because it is difficult to find the nested NER dataset (the famous datasets are too expensive 233), I just use flat ner dataset.

How to use my code

Step 1

Preprocess the data, transform the raw data into MRC_training data

python -m src.data.preprocess

Step 2

training , validation , testing

python -m src.main

output

	B	I	O	macro	micro
acc	0.9971	0.9928	0.9911
precision	0.9470	0.9346	0.9986	0.9601	0.9905
recall	0.9817	0.9885	0.9911	0.9871	0.9905
f1_score	0.9640	0.9608	0.9948	0.9734	0.9905

To do list:

• A implementation or a method to directly get the output if people give a sentence. You know it !

Transformer

According to Attention is all you need, for the code, I borrow the The Annotated Transformer.

I finished the model architecture.

To do List:

• Finding Dataset
• Dataset Preprocessing
• Model Wrapper for training,validation,testing
• running model function

Package Building

To do List:

• Log Package
• Load and Save the torch model instead of the whole wrapper