• Running by command
  • require environment
  • install python venv
  • convert to test_venv
  • install packages
  • set PYTHONPATH
  • train model
  • run model
  • test
  • 起服务
  • 客户端
  • 参考我的博客

说明

• rasa-nlu 使用bert的例子,先train进行自定义预料的fine-tune，在用rasa启动nlu服务
• 需要自己下载bert的预训练模型chinese_wwm_ext_L-12_H-768_A-1
• https://github.com/ymcui/Chinese-BERT-wwm#%E4%B8%AD%E6%96%87%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD
• 放到代码中的google目录下，具体检查配置文件config.yml
• 自定义预料放在data/nlu/nlu.md中 配置intent和对应的话术

Running by command

require environment

• python == 3.7

install python venv

conda create -n rasa_bert python=3.7

convert to test_venv

conda activate rasa_bert

install packages

pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
#in linux
pip uninstall tensorflow
pip install tensorflow-gpu=1.14.0
#in mac
pip install tensorflow==1.14.0
#in all
pip install kashgari-tf==0.5.5 -i https://mirrors.aliyun.com/pypi/simple/

set PYTHONPATH

export PYTHONPATH=/path/to/your/component
export PYTHONPATH=/opt/mt/rasa/rasa_bert/components

上面完成后所需要的环境就搭建完成，下面就可以开始训练了，当然你需要在config.yml里面将bert的预训练路径指定下。 这个不需要也可以，看是否能找到源码下的component目录即可

###some error when you install kashgari-tf in linux:

ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.

We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.

rasa 1.1.8 requires tensorflow~=1.13.0, which is not installed.
rasa 1.1.8 requires scikit-learn~=0.20.0, but you'll have scikit-learn 0.23.2 which is incompatible.
rasa 1.1.8 requires scikit-learn~=0.20.2, but you'll have scikit-learn 0.23.2 which is incompatible.
keras-bert 0.86.0 requires Keras>=2.4.3, but you'll have keras 2.2.4 which is incompatible.

train model

make train
如果只训练nlu：
rasa train nlu -u data/nlu -c config.yml --out models/nlu

训练nlu和core模型，新版本中会将模型自动打包成zip文件。

run model

make run

test in cmdline

make run-cmdline

可以在命令行中测试

test:

 rasa shell -m models/nlu/nlu-20200816-165652.tar.gz

起服务:

export PYTHONPATH=/opt/mt/rasa/rasa_bert/components

rasa run --enable-api -m  models/nlu/nlu-20200816-165652.tar.gz
或：
rasa run --enable-api -m  models/nlu/nlu-20200818-134939.tar.gz -p 5500 --cors "*" --log-file out.log &

客户端：

curl localhost:5005/model/parse -d '{"text":"你好"}'|jq

参考我的博客：

https://www.iteye.com/blog/haoningabc-2516308 简易安装参考 INSTALL.md 注意tensorflow要用1.14.0的版本 检查 pip freeze|grep tensorflow