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README
MIT

A light tool for TF Model building/training/evaluating for simple nlp tasks just by params configuration, training/evaluating monitor and params configure GUI with streamlit, and auto publish all models' predictor apis

copy pretrained LM into hub/bases/ (bert, rbt, albert ...)

How to run:

  1. run init_params first to generate params template...
  2. streamlit run home.py --server.port=PORT
  3. celery -A celery_training worker -l INFO --pidfile=celery/%n.pid --logfile=celery/%n%I.log

open localhost:PORT with a broswer ...

Rest service:

  • uvicorn rest_service.handlers:app --port=REST_SERVER-PORT or python3 rest_server.py -p=REST_SERVER-PORT

ENV

python >= 3.6 test with tf 2.4.0, 2.2.2

Preview

common params setting

model layers settings

model layers' params settings

trainning monitoring

trainning scores

api published automaticly

references:

keras-bert

Kashgari

Bert4Keras

MIT License Copyright (c) 2021 jeusgao Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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TF Model building/training/evaluating for simple nlp task just by params configuration, training/evaluating monitor and params configure GUI with streamlit. 展开 收起
Python
MIT
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