a implementation of google QAnet, a tensorflow estimator version, have very good proved performance
A Tensorflow implementation of Google's QANet. thanks nlplearn a lot, some implementation details came from their work. but overall, this is a whole new version using estimator-api, dataset-api and layers-api. these can make code more concise and clear and I've done extra processing on padding to ensure that the padding data doesn't pollute the normal input data in the neural network. I've got higher F1 and EM under the same suite parameter than nlplearn's version
- Python>=3.5
- TensorFlow>=1.10
I use a parameter to switch models (there is a bidaf flow model inside)
--procedure "wordembedding","QAnetEmbedding","dcn","QaModelBlock","QAOutput"
using this procedure, a qanet is built.
add elmo to replace embedding layer
The dataset used for this task is Stanford Question Answering Dataset I did not use it directly, but copy my network to nlplearn's qanet, you can see the replacement in my fork version ，and the comparison of result is shown below
nlplearn's em
nlplearn's f1
my version's em
my version's f1
