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BERT NER Pytorch

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 About BERT NER Pytorch

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

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Python

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BERT NER Pytorch

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Chinese NER using Bert

BERT for Chinese NER.

update：其他一些可以参考,包括Biaffine、GlobalPointer等:examples

dataset list

  1. cner: datasets/cner
  2. CLUENER: https://github.com/CLUEbenchmark/CLUENER

model list

  1. BERT+Softmax
  2. BERT+CRF
  3. BERT+Span

requirement

  1. 1.1.0 =< PyTorch < 1.5.0
  2. cuda=9.0
  3. python3.6+

input format

Input format (prefer BIOS tag scheme), with each character its label for one line. Sentences are splited with a null line.

美	B-LOC
国	I-LOC
的	O
华	B-PER
莱	I-PER
士	I-PER

我	O
跟	O
他	O

run the code

  1. Modify the configuration information in run_ner_xxx.py or run_ner_xxx.sh .
  2. sh scripts/run_ner_xxx.sh

note: file structure of the model

├── prev_trained_model
|  └── bert_base
|  |  └── pytorch_model.bin
|  |  └── config.json
|  |  └── vocab.txt
|  |  └── ......

CLUENER result

The overall performance of BERT on dev:

Accuracy (entity) Recall (entity) F1 score (entity)
BERT+Softmax 0.7897 0.8031 0.7963
BERT+CRF 0.7977 0.8177 0.8076
BERT+Span 0.8132 0.8092 0.8112
BERT+Span+adv 0.8267 0.8073 0.8169
BERT-small(6 layers)+Span+kd 0.8241 0.7839 0.8051
BERT+Span+focal_loss 0.8121 0.8008 0.8064
BERT+Span+label_smoothing 0.8235 0.7946 0.8088

ALBERT for CLUENER

The overall performance of ALBERT on dev:

model version Accuracy(entity) Recall(entity) F1(entity) Train time/epoch
albert base_google 0.8014 0.6908 0.7420 0.75x
albert large_google 0.8024 0.7520 0.7763 2.1x
albert xlarge_google 0.8286 0.7773 0.8021 6.7x
bert google 0.8118 0.8031 0.8074 -----
albert base_bright 0.8068 0.7529 0.7789 0.75x
albert large_bright 0.8152 0.7480 0.7802 2.2x
albert xlarge_bright 0.8222 0.7692 0.7948 7.3x

Cner result

The overall performance of BERT on dev(test):

Accuracy (entity) Recall (entity) F1 score (entity)
BERT+Softmax 0.9586(0.9566) 0.9644(0.9613) 0.9615(0.9590)
BERT+CRF 0.9562(0.9539) 0.9671(0.9644) 0.9616(0.9591)
BERT+Span 0.9604(0.9620) 0.9617(0.9632) 0.9611(0.9626)
BERT+Span+focal_loss 0.9516(0.9569) 0.9644(0.9681) 0.9580(0.9625)
BERT+Span+label_smoothing 0.9566(0.9568) 0.9624(0.9656) 0.9595(0.9612)