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fastNLP/reproduction/seqence_labelling/ner
yh 807a8f1fb3 1. BucketSampler不需要自己传入batch_size了，由Trainer自动设置 6 years ago
..
data fix import bug 6 years ago
model fix import bug 6 years ago
test 新增NER的数据加载与模型代码; 修改metric中的typo; 修改LSTM中的默认初始化将forget gate设置为1. 6 years ago
README.md ner的readme更新 6 years ago
__init__.py 新增NER的数据加载与模型代码; 修改metric中的typo; 修改LSTM中的默认初始化将forget gate设置为1. 6 years ago
train_cnn_lstm_crf_conll2003.py 修复Embedding中的bug 6 years ago
train_idcnn.py fix import bug 6 years ago
train_ontonote.py 1. BucketSampler不需要自己传入batch_size了，由Trainer自动设置 6 years ago

README.md

NER任务模型复现

这里使用fastNLP复现经典的BiLSTM-CNN的NER任务的模型，旨在达到与论文中相符的性能。

论文链接Named Entity Recognition with Bidirectional LSTM-CNNs

数据集及复现结果汇总

使用fastNLP复现的结果vs论文汇报结果(/前为fastNLP实现，后面为论文报道)

model name Conll2003 Ontonotes
BiLSTM-CNN 91.17/90.91 86.47/86.35
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一款轻量级的自然语言处理（NLP）工具包，目标是减少用户项目中的工程型代码，例如数据处理循环、训练循环、多卡运行等

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