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Question and Answering Model with TensorFlow
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Latest commit 5f52636 Jul 13, 2018
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data #2 add egret-wenda data and dataset/ Feb 18, 2017
deepqa2 support py2.7 Oct 20, 2017
logs refactor code structure Jan 24, 2017
save refactor code structure Jan 24, 2017
scripts #4 update CMD Apr 4, 2017
.gitignore support py2.7 Oct 20, 2017
Dockerfile.gpu #4 update Docker CMD Apr 4, 2017
requirements.txt #4 enable training with docker Apr 4, 2017 #4 update CMD Apr 4, 2017

Note, this repo is deprecated.

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Part 1: Introduction

Part 2: Bot Engine

Part 3: Bot Model

This repository is align with Part 3: Bot Model.

Train and serve QA Model with TensorFlow

Tested with TensorFlow#0.11.0rc2, Python#3.5.

Install Nvidia Drivers, CUDNn, Python, TensorFlow on Ubuntu 16.04


Inspired and inherited from DeepQA.

Install deps

pip install -r requirements.txt

Install TensorFlow

pip install —-upgrade $TF_BINARY_URL

Pre-process data

Process data, build vocabulary, word embedding, conversations, etc.

cp config.sample.ini config.ini
python deepqa2/dataset/

Sample Corpus

Train Model

Train language model with Seq2seq.

cp config.sample.ini config.ini # modify keys
python deepqa2/

Serve Model

Provide RESt API to access language model.

cd DeepQA2/save/deeplearning.cobra.vulcan.20170127.175256/deepqa2/serve
cp db.sample.sqlite3 db.sqlite3 
python runserver

Access Service with RESt API

POST /api/v1/question HTTP/1.1
Content-Type: application/json
Authorization: Basic YWRtaW46cGFzc3dvcmQxMjM=
Cache-Control: no-cache

{"message": "good to know"}

  "rc": 0,
  "msg": "hello"

Train with Docker



docker pull samurais/deepqa2:latest
cd DeepQA2
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