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All Projects → xxxsssyyy → DeepNER

xxxsssyyy / DeepNER

Licence: Apache-2.0 license
An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

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named-entity-recognitionberttransformer-tensorflowtransformer-ner

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DeepNER

An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

This repository contains complex Deep Learning models for named entity recognition.

Requirements

Train Models

  • Train Transformer with CRF
python test_Transformer_CRF.py --num_blocks 2
  • Train Bilstm with CRF
python test_BiLSTM_CRF.py
  • fintuning bert with freezing bert Variables
python test_BiLSTM_CRF.py --freeze_bert True
  • fintuning bert Variables simultaneously
python test_BiLSTM_CRF.py --freeze_bert False

Simply Use Bert Sentence Embeddings

Please have a look at tests/predict_bert_sentence_emb.py

bc = BertVector(config)
embs = bc.encode(["这个项目好用吗", "希望被多多点赞！", "修改配置请看config字典"])

Using Bert for text classification

Please have a look at tests/bert_classification.py

model = BertClassification(config)
model.train()

Performances Comparison

models Precision Recall F1-Score
Transformer-CRF(2 Layers) 67.56% 62.88% 65.14%
BiGRU-CRF 91.66% 89.85% 90.75%
BiLSTM-CRF 91.90% 89.85% 90.87%
Bert-BiLSTM-CRF(freeze) 94.56% 95.09% 94.82%
Bert-BiLSTM-CRF(fintuning bert simultaneously) 95.33% 94.69% 95.01%

TODO

  • Lexicon enhance
  • Label Attention Network for fine-gained NER
  • Nested NER

References

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