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python:3.5
tensorflow: 1.11
use machine reading comprehension (MRC) model to solve NER task.
each data is a tuple (question,passage,start_pisition,end_position)
In NER, question is the lable definition for each entity type, passage is each sentence, start_position is the start position of each entity
and end_position is the end position of each entity.
use single one-pass model to solve NER task.
Each data ia a tuple (passage, start_position1, end_position1, start_position2, end_position2, ...)
Because we use the last checkpoint of BERT to predict, so the development set is just to verify the performance of model.
we just set an example for mrc data
we just set an example for SOne data
for SOne model, the type information is defined in advance. For example, normalize_bert.npy is bert representation of guideline information.
get the answer of submit file format
python trans2answer.py
医学自然语言处理算法库,包括命名实体 、语义关系抽取、时间序列预测、预训练模型等。
Text Pickle Python HTML SVG other
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