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NLP model Bert: from introduction to mastery (2)
2020-11-06 01:22:00 【Elementary school students in IT field】
Named entity recognition
First download the corresponding bert modular
pip install bert-base==0.0.9 -i https://pypi.python.org/simple
Also can reference Official website Handle
install
What the package now supports
1. Named entity recognition training
2. Services for Named Entity Recognition C/S
3. Inherit excellent open source software :bert_as_service(hanxiao) Of BERT All services
4. Text categorization Services
The following functions will continue to increase
Training named entity recognition model based on named row :
installed bert-base after , Two tools based on named rows will be generated , among bert-base-ner-train Support the training of named entity recognition model , You just need to specify the directory of training data ,BERT The directory of relevant parameters can be . You can use the following command to view help
The examples of training are named as follows :
bert-base-ner-train \
-data_dir {your dataset dir}\
-output_dir {training output dir}\
-init_checkpoint {Google BERT model dir}\
-bert_config_file {bert_config.json under the Google BERT model dir} \
-vocab_file {vocab.txt under the Google BERT model dir}
Parameter description
among data_dir It's the directory where your data is located , Training data , The naming format of validation data and test data is :train.txt, dev.txt,test.txt, Please name the file in this format , Otherwise, an error will be reported .
The format of training data is as follows :
The sea O
fishing O
Than O
" O
The earth O
spot O
stay O
mansion B-LOC
door I-LOC
And O
gold B-LOC
door I-LOC
And O
between O
Of O
The sea O
Domain O
. O
The first word in each line is , The second is its label , Use spaces ’ ' Separate , Please make sure to use spaces . Use blank lines between sentences . The program will automatically read your data .
output_dir: Training model output file path , Model checkpoint And some tag mapping tables will be stored here , This path is used as a service , Can be specified as -ner_model_dir
init_checkpoint: Download Google BERT Model
bert_config_file : Google BERT Under the model bert_config.json
vocab_file: Google BERT Under the model vocab.txt
After training , You can specify in your output_dir To see the results of your training .
More operations :
https://blog.csdn.net/macanv/article/details/85684284
One more bert Encapsulation of models
https://www.jianshu.com/p/1d6689851622
https://cloud.tencent.com/developer/article/1470051
https://www.h3399.cn/201908/714454.html
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